Journal of Pathology Informatics Journal of Pathology Informatics
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Abstracts: Pathology Informatics Summit 2017

J Pathol Inform 2017, 8:26 (14 July 2017)
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Technical Note: Development and implementation of a coagulation factor testing method utilizing autoverification in a high-volume clinical reference laboratory environment
Paul W Riley, Benoit Gallea, Andre Valcour
J Pathol Inform 2017, 8:25 (19 June 2017)
Background: Testing coagulation factor activities requires that multiple dilutions be assayed and analyzed to produce a single result. The slope of the line created by plotting measured factor concentration against sample dilution is evaluated to discern the presence of inhibitors giving rise to nonparallelism. Moreover, samples producing results on initial dilution falling outside the analytic measurement range of the assay must be tested at additional dilutions to produce reportable results. Methods: The complexity of this process has motivated a large clinical reference laboratory to develop advanced computer algorithms with automated reflex testing rules to complete coagulation factor analysis. A method was developed for autoverification of coagulation factor activity using expert rules developed with on an off the shelf commercially available data manager system integrated into an automated coagulation platform. Results: Here, we present an approach allowing for the autoverification and reporting of factor activity results with greatly diminished technologist effort. Conclusions: To the best of our knowledge, this is the first report of its kind providing a detailed procedure for implementation of autoverification expert rules as applied to coagulation factor activity testing. Advantages of this system include ease of training for new operators, minimization of technologist time spent, reduction of staff fatigue, minimization of unnecessary reflex tests, optimization of turnaround time, and assurance of the consistency of the testing and reporting process.
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Research Article: A reduced set of features for chronic kidney disease prediction
Rajesh Misir, Malay Mitra, Ranjit Kumar Samanta
J Pathol Inform 2017, 8:24 (19 June 2017)
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs.
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Review Article: Current state of the regulatory trajectory for whole slide imaging devices in the USA
Esther Abels, Liron Pantanowitz
J Pathol Inform 2017, 8:23 (15 May 2017)
DOI:10.4103/jpi.jpi_11_17  PMID:28584684
The regulatory field for digital pathology (DP) has advanced significantly. A major milestone was accomplished when the FDA allowed the first vendor to market their device for primary diagnostic use in the USA and published in the classification order that this device, and substantially equivalent devices of this generic type, should be classified into class II instead of class III as previously proposed. The Digital Pathology Association (DPA) regulatory task force had a major role in the accomplishment of getting the application request for Whole Slide Imaging (WSI) Systems recommended for a de novo. This article reviews the past and emerging regulatory environment of WSI for clinical use in the USA. A WSI system with integrated subsystems is defined in the context of medical device regulations. The FDA technical performance assessment guideline is also discussed as well as parameters involved in analytical testing and clinical studies to demonstrate that WSI devices are safe and effective for clinical use.
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Book Review: Review of “Travels on conferences: Evolution of digital pathology” by Klaus Kayser
Gabor Fischer
J Pathol Inform 2017, 8:22 (15 May 2017)
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Research Article: Training nuclei detection algorithms with simple annotations
Henning Kost, André Homeyer, Jesper Molin, Claes Lundström, Horst Karl Hahn
J Pathol Inform 2017, 8:21 (15 May 2017)
DOI:10.4103/jpi.jpi_3_17  PMID:28584683
Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.
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PV16 Abstracts: Abstracts

J Pathol Inform 2017, 8:20 (10 May 2017)
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Technical Note: The need for careful data collection for pattern recognition in digital pathology
Raphaël Marée
J Pathol Inform 2017, 8:19 (10 April 2017)
DOI:10.4103/jpi.jpi_94_16  PMID:28480122
Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.
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Original Article: Alchemy: A web 2.0 real-time quality assurance platform for human immunodeficiency Virus, hepatitis C Virus, and BK Virus quantitation assays
Emmanuel Agosto-Arroyo, Gina M Coshatt, Thomas S Winokur, Shuko Harada, Seung L Park
J Pathol Inform 2017, 8:18 (10 April 2017)
DOI:10.4103/jpi.jpi_69_16  PMID:28480121
Background: The molecular diagnostics laboratory faces the challenge of improving test turnaround time (TAT). Low and consistent TATs are of great clinical and regulatory importance, especially for molecular virology tests. Laboratory information systems (LISs) contain all the data elements necessary to do accurate quality assurance (QA) reporting of TAT and other measures, but these reports are in most cases still performed manually: a time-consuming and error-prone task. The aim of this study was to develop a web-based real-time QA platform that would automate QA reporting in the molecular diagnostics laboratory at our institution, and minimize the time expended in preparing these reports. Methods: Using a standard Linux, Nginx, MariaDB, PHP stack virtual machine running atop a Dell Precision 5810, we designed and built a web-based QA platform, code-named Alchemy. Data files pulled periodically from the LIS in comma-separated value format were used to autogenerate QA reports for the human immunodeficiency virus (HIV) quantitation, hepatitis C virus (HCV) quantitation, and BK virus (BKV) quantitation. Alchemy allowed the user to select a specific timeframe to be analyzed and calculated key QA statistics in real-time, including the average TAT in days, tests falling outside the expected TAT ranges, and test result ranges. Results: Before implementing Alchemy, reporting QA for the HIV, HCV, and BKV quantitation assays took 45–60 min of personnel time per test every month. With Alchemy, that time has decreased to 15 min total per month. Alchemy allowed the user to select specific periods of time and analyzed the TAT data in-depth without the need of extensive manual calculations. Conclusions: Alchemy has significantly decreased the time and the human error associated with QA report generation in our molecular diagnostics laboratory. Other tests will be added to this web-based platform in future updates. This effort shows the utility of informatician-supervised resident/fellow programming projects as learning opportunities and workflow improvements in the molecular laboratory.
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Letter to Editor: Compromising the security of “Generating unique identifiers from patient identification data using security models”
Arran Schlosberg
J Pathol Inform 2017, 8:17 (10 April 2017)
DOI:10.4103/jpi.jpi_1_17  PMID:28480120
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Technical Note: Evaluation of android smartphones for telepathology
Donald Ekong, Fang Liu, G Thomas Brown, Arunima Ghosh, Paul Fontelo
J Pathol Inform 2017, 8:16 (10 April 2017)
DOI:10.4103/jpi.jpi_93_16  PMID:28480119
Background: In the year 2014, Android smartphones accounted for one-third of mobile connections globally but are predicted to increase to two-thirds by 2020. In developing countries, where teleconsultations can benefit health-care providers most, the ratio is even higher. This study compared the use of two Android phones, an 8 megapixel (MP) and a 16 MP phone, for capturing microscopic images. Method: The Android phones were used to capture images and videos of a gastrointestinal biopsy teaching set of referred cases from the Armed Forces Institute of Pathology (AFIP). The acquired images and videos were reviewed online by two pathologists for image quality, adequacy for diagnosis, usefulness of video overviews, and confidence in diagnosis, on a 5-point Likert scale. Results: The results show higher means in a 5-point Likert scale for the 8 MP versus the 16 MP phone that were statistically significant in adequacy of images (4.0 vs. 3.75) for rendering diagnosis and for agreement with the reference diagnosis (2.33 vs. 2.07). Although the quality of images was found higher in the 16 MP phone (3.8 vs. 3.65), these were not statistically significant. Adding video images of the entire specimen was found to be useful for evaluating the slides (combined mean, 4.0). Conclusion: For telepathology and other image dependent practices in developing countries, Android phones could be a useful tool for capturing images.
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Original Article: Predictive nuclear chromatin characteristics of melanoma and dysplastic nevi
Matthew G Hanna, Chi Liu, Gustavo K Rohde, Rajendra Singh
J Pathol Inform 2017, 8:15 (10 April 2017)
DOI:10.4103/jpi.jpi_84_16  PMID:28480118
Background: The diagnosis of malignant melanoma (MM) is among the diagnostic challenges pathologists encounter on a routine basis. Melanoma may arise in patients with preexisting dysplastic nevi (DN) and it is still the cause of 1.7% of all cancer-related deaths. Melanomas often have overlapping histological features with DN, especially those with severe dysplasia. Nucleotyping for identifying nuclear textural features can analyze nuclear DNA structure and organization. The aim of this study is to differentiate MM and DN using these methodologies. Methods: Dermatopathology slides diagnosed as MM and DN were retrieved. The glass slides were scanned using an Aperio ScanScopeXT at ×40 (0.25 μ/pixel). Whole slide images (WSI) were annotated for nuclei selection. Nuclear features to distinguish between MM and DN based on chromatin distributions were extracted from the WSI. The morphological characteristics for each nucleus were quantified with the optimal transport-based linear embedding in the continuous domain. Label predictions for individual cell nucleus are achieved through a modified version of linear discriminant analysis, coupled with the k-nearest neighbor classifier. Label for an unknown patient was set by the voting strategy with its pertaining cell nuclei. Results: Nucleotyping of 139 patient cases of melanoma (n = 67) and DN (n = 72) showed that our method had superior classification accuracy of 81.29%. This is a 6.4% gain in differentiating MM and DN, compared with numerical feature-based method. The distribution differences in nuclei morphology between MM and DN can be visualized with biological interpretation. Conclusions: Nucleotyping using quantitative and qualitative analyses may provide enough information for differentiating MM from DN using pixel image data. Our method to segment cell nuclei may offer a practical and inexpensive solution in aiding in the accurate diagnosis of melanoma.
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Commentary: Making pathology diagnoses with glass or digital slides: Which modality is inferior?
Jonhan Ho, Liron Pantanowitz
J Pathol Inform 2017, 8:14 (10 April 2017)
DOI:10.4103/jpi.jpi_10_17  PMID:28480117
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Original Article: Performance of a web-based method for generating synoptic reports
Megan A Renshaw, Scott A Renshaw, Mercy Mena-Allauca, Patricia P Carrion, Xiaorong Mei, Arniris Narciandi, Edwin W Gould, Andrew A Renshaw
J Pathol Inform 2017, 8:13 (10 March 2017)
DOI:10.4103/jpi.jpi_91_16  PMID:28382227
Context: The College of American Pathologists (CAP) requires synoptic reporting of all tumor excisions. Objective: To compare the performance of different methods of generating synoptic reports. Methods: Completeness, amendment rates, rate of timely ordering of ancillary studies (KRAS in T4/N1 colon carcinoma), and structured data file extraction were compared for four different synoptic report generating methods. Results: Use of the printed tumor protocols directly from the CAP website had the lowest completeness (84%) and highest amendment (1.8%) rates. Reformatting these protocols was associated with higher completeness (94%, P < 0.001) and reduced amendment (1%, P = 0.20) rates. Extraction into a structured data file was successful 93% of the time. Word-based macros improved completeness (98% vs. 94%, P < 0.001) but not amendment rates (1.5%). KRAS was ordered before sign out 89% of the time. In contrast, a web-based product with a reminder flag when items were missing, an embedded flag for data extraction, and a reminder to order KRAS when appropriate resulted in improved completeness (100%, P = 0.005), amendment rates (0.3%, P = 0.03), KRAS ordering before sign out (100%, P = 0.23), and structured data extraction (100%, P < 0.001) without reducing the speed (P = 0.34) or accuracy (P = 1.00) of data extraction by the reader. Conclusion: Completeness, amendment rates, ancillary test ordering rates, and data extraction rates vary significantly with the method used to construct the synoptic report. A web-based method compares favorably with all other methods examined and does not reduce reader usability.
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Research Article: A randomized study comparing digital imaging to traditional glass slide microscopy for breast biopsy and cancer diagnosis
Joann G Elmore, Gary M Longton, Margaret S Pepe, Patricia A Carney, Heidi D Nelson, Kimberly H Allison, Berta M Geller, Tracy Onega, Anna N. A Tosteson, Ezgi Mercan, Linda G Shapiro, Tad T Brunyé, Thomas R Morgan, Donald L Weaver
J Pathol Inform 2017, 8:12 (10 March 2017)
DOI:10.4103/2153-3539.201920  PMID:28382226
Background: Digital whole slide imaging may be useful for obtaining second opinions and is used in many countries. However, the U.S. Food and Drug Administration requires verification studies. Methods: Pathologists were randomized to interpret one of four sets of breast biopsy cases during two phases, separated by ≥9 months, using glass slides or digital format (sixty cases per set, one slide per case, n = 240 cases). Accuracy was assessed by comparing interpretations to a consensus reference standard. Intraobserver reproducibility was assessed by comparing the agreement of interpretations on the same cases between two phases. Estimated probabilities of confirmation by a reference panel (i.e., predictive values) were obtained by incorporating data on the population prevalence of diagnoses. Results: Sixty-five percent of responding pathologists were eligible, and 252 consented to randomization; 208 completed Phase I (115 glass, 93 digital); and 172 completed Phase II (86 glass, 86 digital). Accuracy was slightly higher using glass compared to digital format and varied by category: invasive carcinoma, 96% versus 93% (P = 0.04); ductal carcinoma in situ (DCIS), 84% versus 79% (P < 0.01); atypia, 48% versus 43% (P = 0.08); and benign without atypia, 87% versus 82% (P < 0.01). There was a small decrease in intraobserver agreement when the format changed compared to when glass slides were used in both phases (P = 0.08). Predictive values for confirmation by a reference panel using glass versus digital were: invasive carcinoma, 98% and 97% (not significant [NS]); DCIS, 70% and 57% (P = 0.007); atypia, 38% and 28% (P = 0.002); and benign without atypia, 97% and 96% (NS). Conclusions: In this large randomized study, digital format interpretations were similar to glass slide interpretations of benign and invasive cancer cases. However, cases in the middle of the spectrum, where more inherent variability exists, may be more problematic in digital format. Future studies evaluating the effect these findings exert on clinical practice and patient outcomes are required.
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Technical Note: Turning microscopy in the medical curriculum digital: Experiences from the faculty of health and medical sciences at University of Copenhagen
Ben Vainer, Niels Werner Mortensen, Steen Seier Poulsen, Allan Have Sørensen, Jørgen Olsen, Hans Henrik Saxild, Flemming Fryd Johansen
J Pathol Inform 2017, 8:11 (10 March 2017)
DOI:10.4103/2153-3539.201919  PMID:28382225
Familiarity with the structure and composition of normal tissue and an understanding of the changes that occur during disease is pivotal to the study of the human body. For decades, microscope slides have been central to teaching pathology in medical courses and related subjects at the University of Copenhagen. Students had to learn how to use a microscope and envisage three-dimensional processes that occur in the body from two-dimensional glass slides. Here, we describe how a PathXL virtual microscopy system for teaching pathology and histology at the Faculty has recently been implemented, from an administrative, an economic, and a teaching perspective. This fully automatic digital microscopy system has been received positively by both teachers and students, and a decision was made to convert all courses involving microscopy to the virtual microscopy format. As a result, conventional analog microscopy will be phased out from the fall of 2016.
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Original Article: An open source, whole slide image-based pathology education system
Paul A Christensen, Nathan E Lee, Michael J Thrall, Suzanne Z Powell, Patricia Chevez-Barrios, S Wesley Long
J Pathol Inform 2017, 8:10 (10 March 2017)
DOI:10.4103/jpi.jpi_72_16  PMID:28382224
Background: Our institution's pathology unknown conferences provide educational cases for our residents. However, the cases have not been previously available digitally, have not been collated for postconference review, and were not accessible to a wider audience. Our objective was to create an inexpensive whole slide image (WSI) education suite to address these limitations and improve the education of pathology trainees. Materials and Methods: We surveyed residents regarding their preference between four unique WSI systems. We then scanned weekly unknown conference cases and study set cases and uploaded them to our custom built WSI viewer located at We measured site utilization and conference participation. Results: Residents preferred our OpenLayers WSI implementation to Ventana Virtuoso, Google Maps API, and OpenSlide. Over 16 months, we uploaded 1366 cases from 77 conferences and ten study sets, occupying 793.5 GB of cloud storage. Based on resident evaluations, the interface was easy to use and demonstrated minimal latency. Residents are able to review cases from home and from their mobile devices. Worldwide, 955 unique IP addresses from 52 countries have viewed cases in our site. Conclusions: We implemented a low-cost, publicly available repository of WSI slides for resident education. Our trainees are very satisfied with the freedom to preview either the glass slides or WSI and review the WSI postconference. Both local users and worldwide users actively and repeatedly view cases in our study set.
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Original Article: Identification of histological correlates of overall survival in lower grade gliomas using a bag-of-words paradigm: A preliminary analysis based on hematoxylin & eosin stained slides from the lower grade glioma cohort of the cancer genome Atlas
Reid Trenton Powell, Adriana Olar, Shivali Narang, Ganesh Rao, Erik Sulman, Gregory N Fuller, Arvind Rao
J Pathol Inform 2017, 8:9 (10 March 2017)
DOI:10.4103/jpi.jpi_43_16  PMID:28382223
Background: Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II–III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge. In this study, we aimed to identify tissue-derived image features using a machine learning approach to predict OS in a mixed histology and grade cohort of lower grade glioma patients. To achieve this aim, we used H and E stained slides from the public LGG cohort of The Cancer Genome Atlas (TCGA) to create a machine learned dictionary of “image-derived visual words” associated with OS. We then evaluated the combined efficacy of using these visual words in predicting short versus long OS by training a generalized machine learning model. Finally, we mapped these predictive visual words back to molecular signaling cascades to infer potential drivers of the machine learned survival-associated phenotypes. Methods: We analyzed digitized histological sections downloaded from the LGG cohort of TCGA using a bag-of-words approach. This method identified a diverse set of histological patterns that were further correlated with OS, histology, and molecular signaling activity using Cox regression, analysis of variance, and Spearman correlation, respectively. A support vector machine (SVM) model was constructed to discriminate patients into short and long OS groups dichotomized at 24-month. Results: This method identified disease-relevant phenotypes associated with OS, some of which are correlated with disease-associated molecular pathways. From these image-derived phenotypes, a generalized SVM model which could discriminate 24-month OS (area under the curve, 0.76) was obtained. Conclusion: Here, we demonstrated one potential strategy to incorporate image features derived from H and E stained slides into predictive models of OS. In addition, we showed how these image-derived phenotypic characteristics correlate with molecular signaling activity underlying the etiology or behavior of LGG.
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Symposium: Summary of the 4th nordic symposium on digital pathology
Claes Lundström, Marie Waltersson, Anders Persson, Darren Treanor
J Pathol Inform 2017, 8:8 (10 March 2017)
DOI:10.4103/jpi.jpi_5_17  PMID:28382222
The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange across stakeholders in health care, industry, and academia. In 2016, the 4th NDP installment took place in Linköping, Sweden, promoting development and collaboration in digital pathology for the benefit of routine care advances. This article summarizes the symposium, gathering 170 attendees from 13 countries. This summary also contains results from a survey on integrated diagnostics aspects, in particular radiology-pathology collaboration.
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Technical Note: Open-source software for demand forecasting of clinical laboratory test volumes using time-series analysis
Emad A Mohammed, Christopher Naugler
J Pathol Inform 2017, 8:7 (28 February 2017)
DOI:10.4103/jpi.jpi_65_16  PMID:28400996
Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand.
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Original Article: WhatsApp for teaching pathology postgraduates: a pilot study
Aditi Goyal, Nadeem Tanveer, Pooja Sharma
J Pathol Inform 2017, 8:6 (28 February 2017)
DOI:10.4103/2153-3539.201111  PMID:28400995
Introduction: Postgraduate students spend a sizeable proportion of their time on social media platforms such as WhatsApp and Facebook. This change in our social interaction needs to be accommodated in our teaching methods. To engage them and arouse their curiosity, WhatsApp is an ideal platform. Digital photography by cell phone cameras has made it possible to share cases and discuss them with students round the clock. Objective: The primary aim of the study was to develop sharing and discussion of images using WhatsApp. It also aimed at gathering feedback by means of a questionnaire from pathology residents about their views about the use of WhatsApp for teaching purpose. Materials and Methods: A WhatsApp group by the name “Pathology on the Go” was created with the authors of this study as group administrators and all junior and senior resident doctors (69) as members. The group was used to discuss interesting cases, quiz questions, and other pathology-related academic issues. At the end of 4 weeks, a questionnaire was distributed among the members, and feedback was sought regarding their experience in the group. Results: Over a 4-week period, 16 cases were discussed with 647 posts. A total of 45 participants out of 69 were active participants, and they had an average of 14 posts over the 4-week period. Majority of the participants found the discussions very useful with minimal disruption of the daily routine. Conclusion: There is a need to incorporate Web 2.0 tools such as WhatsApp in our teaching methods to capture as much screen time of the students as possible.
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Research Article: Pathological diagnosis of gastric cancers with a novel computerized analysis system
Kosuke Oikawa, Akira Saito, Tomoharu Kiyuna, Hans Peter Graf, Eric Cosatto, Masahiko Kuroda
J Pathol Inform 2017, 8:5 (28 February 2017)
DOI:10.4103/2153-3539.201114  PMID:28400994
Background: Recent studies of molecular biology have provided great advances for diagnostic molecular pathology. Automated diagnostic systems with computerized scanning for sampled cells in fluids or smears are now widely utilized. Automated analysis of tissue sections is, however, very difficult because they exhibit a complex mixture of overlapping malignant tumor cells, benign host-derived cells, and extracellular materials. Thus, traditional histological diagnosis is still the most powerful method for diagnosis of diseases. Methods: We have developed a novel computer-assisted pathology system for rapid, automated histological analysis of hematoxylin and eosin (H and E)-stained sections. It is a multistage recognition system patterned after methods that human pathologists use for diagnosis but harnessing machine learning and image analysis. The system first analyzes an entire H and E-stained section (tissue) at low resolution to search suspicious areas for cancer and then the selected areas are analyzed at high resolution to confirm the initial suspicion. Results: After training the pathology system with gastric tissues samples, we examined its performance using other 1905 gastric tissues. The system's accuracy in detecting malignancies was shown to be almost equal to that of conventional diagnosis by expert pathologists. Conclusions: Our novel computerized analysis system provides a support for histological diagnosis, which is useful for screening and quality control. We consider that it could be extended to be applicable to many other carcinomas after learning normal and malignant forms of various tissues. Furthermore, we expect it to contribute to the development of more objective grading systems, immunohistochemical staining systems, and fluorescent-stained image analysis systems.
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Original Article: Criteria to screen molecular tests for the diagnosis of herpes simplex virus in the central nervous system have no propensity to harm
Ronald George Hauser, Cynthia A Brandt, Richard A Martinello
J Pathol Inform 2017, 8:4 (28 February 2017)
DOI:10.4103/2153-3539.201113  PMID:28400993
Objectives: Investigators have ruled out herpes simplex virus (HSV) infection without the detection of herpes simplex deoxyribonucleic acid in cerebrospinal fluid (CSF) (i.e., HSV polymerase chain reaction [PCR]) by laboratory (normal CSF white blood cell count and protein) and clinical criteria (age ≥2 years, no history of human immunodeficiency virus or solid-organ transplant). Compared to HSV PCR of all samples, the algorithm saves money in test costs and may decrease exposure to acyclovir by illustrating the low probability that the patient has HSV. Concern exists that algorithm use may cause harm through alteration of empiric acyclovir treatment in patients with true HSV central nervous system infection. Methods: All Department of Veterans Affair's patients with a positive HSV PCR of the CSF between 2000 and 2013 were identified and their medical records reviewed to determine the extent and possible impact of omitted HSV PCR testing by the algorithm. Results: Of 6357 total results, 101 patients had a positive CSF HSV PCR in the study period. Among the positive CSF HSV PCR results, the algorithm excluded 7 (7%) from PCR testing. Record review indicated these seven patients not tested by the algorithm with a positive CSF HSV PCR were considered by their attending physician not to have active HSV. Conclusion: The algorithm to screen HSV tests had no propensity to harm.
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Technical Note: Implementation of a software application for presurgical case history review of frozen section pathology cases
Andrew P Norgan, Mathew L Okeson, Justin E Juskewitch, Kabeer K Shah, William R Sukov
J Pathol Inform 2017, 8:3 (28 February 2017)
DOI:10.4103/2153-3539.201112  PMID:28400992
Background: The frozen section pathology practice at Mayo Clinic in Rochester performs ~20,000 intraoperative consultations a year (~70–80/weekday). To prepare for intraoperative consultations, surgical pathology fellows and residents review the case history, previous pathology, and relevant imaging the day before surgery. Before the work described herein, review of pending surgical pathology cases was a paper-based process requiring handwritten transcription from the electronic health record, a laborious and potentially error prone process. Methods: To facilitate more efficient case review, a modular extension of an existing surgical listing software application (Surgical and Procedure Scheduling [SPS]) was developed. The module (SPS-pathology-specific module [PM]) added pathology-specific functionality including recording case notes, prefetching of radiology, pathology, and operative reports from the medical record, flagging infectious cases, and real-time tracking of cases in the operating room. After implementation, users were surveyed about its impact on the surgical pathology practice. Results: There were 16 survey respondents (five staff pathologists and eleven residents or fellows). All trainees (11/11) responded that the application improved an aspect of surgical list review including abstraction from medical records (10/11), identification of possibly infectious cases (7/11), and speed of list preparation (10/11). The average reported time savings in list preparation was 1.4 h/day. Respondents indicated the application improved the speed (11/16), clarity (13/16), and accuracy (10/16) of morning report. During the workday, respondents reported the application improved real-time case review (14/16) and situational awareness of ongoing cases (13/16). Conclusions: A majority of respondents found the SPS-PM improved all preparatory and logistical aspects of the Mayo Clinic frozen section surgical pathology practice. In addition, use of the SPS-PM saved an average of 1.4 h/day for residents and fellows engaged in preparatory case review.
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Editorial: Computer science, biology and biomedical informatics academy: outcomes from 5 years of immersing high-school students into informatics research
Andrew J King, Arielle M Fisher, Michael J Becich, David N Boone
J Pathol Inform 2017, 8:2 (28 February 2017)
DOI:10.4103/2153-3539.201110  PMID:28400991
The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist.
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Original Article: Classifications of multispectral colorectal cancer tissues using convolution neural network
Hawraa Haj-Hassan, Ahmad Chaddad, Youssef Harkouss, Christian Desrosiers, Matthew Toews, Camel Tanougast
J Pathol Inform 2017, 8:1 (28 February 2017)
DOI:10.4103/jpi.jpi_47_16  PMID:28400990
Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.
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Original Article: A quantitative approach to evaluate image quality of whole slide imaging scanners
Prarthana Shrestha, R Kneepkens, J Vrijnsen, D Vossen, E Abels, B Hulsken
J Pathol Inform 2016, 7:56 (30 December 2016)
DOI:10.4103/2153-3539.197205  PMID:28197359
Context: The quality of images produced by whole slide imaging (WSI) scanners has a direct influence on the readers' performance and reliability of the clinical diagnosis. Therefore, WSI scanners should produce not only high quality but also consistent quality images. Aim: We aim to evaluate reproducibility of WSI scanners based on the quality of images produced over time and among multiple scanners. The evaluation is independent of content or context of test specimen. Methods: The ultimate judge of image quality is a pathologist, however, subjective evaluations are heavily influenced by the complexity of a case and subtle variations introduced by a scanner can be easily overlooked. Therefore, we employed a quantitative image quality assessment method based on clinically relevant parameters, such as sharpness and brightness, acquired in a survey of pathologists. The acceptable level of quality per parameter was determined in a subjective study. The evaluation of scanner reproducibility was conducted with Philips Ultra-Fast Scanners. A set of 36 HercepTest™ slides were used in three sub-studies addressing variations due to systems and time, producing 8640 test images for evaluation. Results: The results showed that the majority of images in all the sub-studies are within the acceptable quality level; however, some scanners produce higher quality images more often than others. The results are independent of case types, and they match our perception of quality. Conclusion: The quantitative image quality assessment method was successfully applied in the HercepTest™ slides to evaluate WSI scanner reproducibility. The proposed method is generic and applicable to any other types of slide stains and scanners.
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Technical Note: Software: Generating unique IDs from patient identification data using security models
Emad A Mohammed, Jonathan C Slack, Christopher T Naugler
J Pathol Inform 2016, 7:55 (30 December 2016)
DOI:10.4103/2153-3539.197203  PMID:28163977
Background: The use of electronic health records (EHRs) has continued to increase within healthcare systems in the developed and developing nations. EHRs allow for increased patient safety, grant patients easier access to their medical records, and offer a wealth of data to researchers. However, various bioethical, financial, logistical, and information security considerations must be addressed while transitioning to an EHR system. The need to encrypt private patient information for data sharing is one of the foremost challenges faced by health information technology. Method: We describe the usage of the message digest-5 (MD5) and secure hashing algorithm (SHA) as methods for encrypting electronic medical data. In particular, we present an application of the MD5 and SHA-1 algorithms in encrypting a composite message from private patient information. Results: The results show that the composite message can be used to create a unique one-way encrypted ID per patient record that can be used for data sharing. Conclusion: The described software tool can be used to share patient EMRs between practitioners without revealing patients identifiable data.
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Original Article: Reporting Gleason grade/score in synoptic reports of radical prostatectomies
Andrew A Renshaw, Mercy Mena-Allauca, Edwin W Gould
J Pathol Inform 2016, 7:54 (30 December 2016)
DOI:10.4103/2153-3539.197201  PMID:28163976
Context: The format of a synoptic report can significantly affect the accuracy, speed, and preference with which a reader can retrieve information. Objective: The objective of this study is to compare different formats of Gleason grading/score in synoptic reports of radical prostatectomies. Methods: The performance of 16 nonpathologists (cancer registrars, MDs, medical non-MDs, and nonmedical) at identifying specific information in various formatted synoptic reports using a computerized quiz that measured both accuracy and speed. Results: Compared to the standard format (primary, secondary, tertiary grades, and total score on separate lines), omitting tertiary grade when "Not applicable" reduced accuracy (72 vs. 97%, P < 0.001) and increased time to retrieve information 63% (P < 0.001). No user preferred to have tertiary grade omitted. Both the biopsy format (primary + secondary = total score, tertiary on a separate line) and the single line format (primary + secondary + (tertiary) -> total score) were associated with increased speed of data extraction (18 and 24%, respectively, P < 0.001). The single line format was more accurate (100% vs. 97%, P = 0.02). No user preferred the biopsy format, and only 7/16 users preferred the single line format. Conclusions : Different report formats for Gleason grading significantly affect users speed, accuracy, and preference; users do not always prefer either speed or accuracy.
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Technical Note: Use of application containers and workflows for genomic data analysis
Wade L Schulz, Thomas Durant, Alexa J Siddon, Richard Torres
J Pathol Inform 2016, 7:53 (30 December 2016)
DOI:10.4103/2153-3539.197197  PMID:28163975
Background: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Methods: Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. Results: While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. Conclusions: With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.
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Book Review: Review of "Digital Pathology Resource Guide, Version 6.0 Issue No. 1, 2016" by College of American Pathologists Digital Pathology Committee
Julie Diane Gibbs, Marilyn M Bui
J Pathol Inform 2016, 7:52 (30 December 2016)
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Original Research: Enhancements in localized classification for uterine cervical cancer digital histology image assessment
Peng Guo, Haidar Almubarak, Koyel Banerjee, R Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R Frazier, Randy H Moss, William V Stoecker
J Pathol Inform 2016, 7:51 (30 December 2016)
DOI:10.4103/2153-3539.197193  PMID:28163974
Background: In previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN assessment process, acellular and atypical cell concentration features were computed from vertical segment partitions of the epithelium region to quantize the relative distribution of nuclei. Methods: Feature data was extracted from 610 individual segments from 61 images for epithelium classification into categories of Normal, CIN1, CIN2, and CIN3. The classification results were compared against CIN labels obtained from two pathologists who visually assessed abnormality in the digitized histology images. In this study, individual vertical segment CIN classification accuracy improvement is reported using the logistic regression classifier for an expanded data set of 118 histology images. Results: We analyzed the effects on classification using the same pathologist labels for training and testing versus using one pathologist labels for training and the other for testing. Based on a leave-one-out approach for classifier training and testing, exact grade CIN accuracies of 81.29% and 88.98% were achieved for individual vertical segment and epithelium whole-image classification, respectively. Conclusions: The Logistic and Random Tree classifiers outperformed the benchmark SVM and LDA classifiers from previous research. The Logistic Regression classifier yielded an improvement of 10.17% in CIN Exact grade classification results based on CIN labels for training-testing for the individual vertical segments and the whole image from the same single expert over the baseline approach using the reduced features. Overall, the CIN classification rates tended to be higher using the training-testing labels for the same expert than for training labels from one expert and testing labels from the other expert. The Exact class fusion- based CIN discrimination results obtained in this study are similar to the Exact class expert agreement rate.
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Original Article: How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling
Agnieszka Onisko, Marek J Druzdzel, R Marshall Austin
J Pathol Inform 2016, 7:50 (30 December 2016)
DOI:10.4103/2153-3539.197191  PMID:28163973
Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion : Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
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Original Article: A multisite validation of whole slide imaging for primary diagnosis using standardized data collection and analysis
Katy Wack, Laura Drogowski, Murray Treloar, Andrew Evans, Jonhan Ho, Anil Parwani, Michael C Montalto
J Pathol Inform 2016, 7:49 (29 November 2016)
DOI:10.4103/2153-3539.194841  PMID:27994941
Context: Text-based reporting and manual arbitration for whole slide imaging (WSI) validation studies are labor intensive and do not allow for consistent, scalable, and repeatable data collection or analysis. Objective: The objective of this study was to establish a method of data capture and analysis using standardized codified checklists and predetermined synoptic discordance tables and to use these methods in a pilot multisite validation study. Methods and Study Design: Fifteen case report form checklists were generated from the College of American Pathology cancer protocols. Prior to data collection, all hypothetical pairwise comparisons were generated, and a level of harm was determined for each possible discordance. Four sites with four pathologists each generated 264 independent reads of 33 cases. Preestablished discordance tables were applied to determine site by site and pooled accuracy, intrareader/intramodality, and interreader intramodality error rates. Results: Over 10,000 hypothetical pairwise comparisons were evaluated and assigned harm in discordance tables. The average difference in error rates between WSI and glass, as compared to ground truth, was 0.75% with a lower bound of 3.23% (95% confidence interval). Major discordances occurred on challenging cases, regardless of modality. The average inter-reader agreement across sites for glass was 76.5% (weighted kappa of 0.68) and for digital it was 79.1% (weighted kappa of 0.72). Conclusion: These results demonstrate the feasibility and utility of employing standardized synoptic checklists and predetermined discordance tables to gather consistent, comprehensive diagnostic data for WSI validation studies. This method of data capture and analysis can be applied in large-scale multisite WSI validations.
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Original Research Article: Reflex test reminders in required cancer synoptic templates decrease order entry error: An analysis of mismatch repair immunohistochemical orders to screen for Lynch syndrome
Mark R Kilgore, Carrie A McIlwain, Rodney A Schmidt, Barbara M Norquist, Elizabeth M Swisher, Rochelle L Garcia, Mara H Rendi
J Pathol Inform 2016, 7:48 (29 November 2016)
DOI:10.4103/2153-3539.194840  PMID:27994940
Background: Endometrial carcinoma (EC) is the most common extracolonic malignant neoplasm associated with Lynch syndrome (LS). LS is caused by autosomal dominant germline mutations in DNA mismatch repair (MMR) genes. Screening for LS in EC is often evaluated by loss of immunohistochemical (IHC) expression of DNA MMR enzymes MLH1, MSH2, MSH6, and PMS2 (MMR IHC). In July 2013, our clinicians asked that we screen all EC in patients ≤60 for loss of MMR IHC expression. Despite this policy, several cases were not screened or screening was delayed. We implemented an informatics-based approach to ensure that all women who met criteria would have timely screening. Subjects and Methods: Reports are created in PowerPath (Sunquest Information Systems, Tucson, AZ) with custom synoptic templates. We implemented an algorithm on March 6, 2014 requiring pathologists to address MMR IHC in patients ≤60 with EC before sign out (S/O). Pathologists must answer these questions: is patient ≤60 (yes/no), if yes, follow-up questions (IHC done previously, ordered with addendum to follow, results included in report, N/A, or not ordered), if not ordered, one must explain. We analyzed cases from July 18, 2013 to August 31, 2016 preimplementation (PreImp) and postimplementation (PostImp) that met criteria. Data analysis was performed using the standard data package included with GraphPad Prism® 7.00 (GraphPad Software, Inc., La Jolla, CA, USA). Results: There were 147 patients who met criteria (29 PreImp and 118 PostImp). IHC was ordered in a more complete and timely fashion PostImp than PreImp. PreImp, 4/29 (13.8%) cases did not get any IHC, but PostImp, only 4/118 (3.39%) were missed (P = 0.0448). Of cases with IHC ordered, 60.0% (15/25) were ordered before or at S/O PreImp versus 91.2% (104/114) PostImp (P = 0.0004). Relative to day of S/O, the mean days of order delay were longer and more variable PreImp versus PostImp (12.9 ± 40.7 vs. -0.660 ± 1.15; P = 0.0227), with the average being before S/O PostImp. Conclusion: This algorithm ensures MMR IHC ordering in women ≤60 with EC and can be applied to similar scenarios. Ancillary tests for management are increasing, especially genetic and molecular-based methods. The burden of managing orders and results remains with the pathologist and relying on human intervention alone is ineffective. Ordering IHC before or at S/O prevents oversight and the additional work of retrospective ordering and reporting.
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Original Article: Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers
Daniel M Spagnolo, Rekha Gyanchandani, Yousef Al-Kofahi, Andrew M Stern, Timothy R Lezon, Albert Gough, Dan E Meyer, Fiona Ginty, Brion Sarachan, Jeffrey Fine, Adrian V Lee, D Lansing Taylor, S Chakra Chennubhotla
J Pathol Inform 2016, 7:47 (29 November 2016)
DOI:10.4103/2153-3539.194839  PMID:27994939
Background: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity. Methods: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map. Results: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. Conclusions: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.
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Original Article: The utility of including pathology reports in improving the computational identification of patients
Wei Chen, Yungui Huang, Brendan Boyle, Simon Lin
J Pathol Inform 2016, 7:46 (29 November 2016)
DOI:10.4103/2153-3539.194838  PMID:27994938
Background: Celiac disease (CD) is a common autoimmune disorder. Efficient identification of patients may improve chronic management of the disease. Prior studies have shown searching International Classification of Diseases-9 (ICD-9) codes alone is inaccurate for identifying patients with CD. In this study, we developed automated classification algorithms leveraging pathology reports and other clinical data in Electronic Health Records (EHRs) to refine the subset population preselected using ICD-9 code (579.0). Materials and Methods: EHRs were searched for established ICD-9 code (579.0) suggesting CD, based on which an initial identification of cases was obtained. In addition, laboratory results for tissue transglutaminse were extracted. Using natural language processing we analyzed pathology reports from upper endoscopy. Twelve machine learning classifiers using different combinations of variables related to ICD-9 CD status, laboratory result status, and pathology reports were experimented to find the best possible CD classifier. Ten-fold cross-validation was used to assess the results. Results: A total of 1498 patient records were used including 363 confirmed cases and 1135 false positive cases that served as controls. Logistic model based on both clinical and pathology report features produced the best results: Kappa of 0.78, F1 of 0.92, and area under the curve (AUC) of 0.94, whereas in contrast using ICD-9 only generated poor results: Kappa of 0.28, F1 of 0.75, and AUC of 0.63. Conclusion: Our automated classification system presented an efficient and reliable way to improve the performance of CD patient identification.
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Technical Note: The growing need for microservices in bioinformatics
Christopher L Williams, Jeffrey C Sica, Robert T Killen, Ulysses G. J. Balis
J Pathol Inform 2016, 7:45 (29 November 2016)
DOI:10.4103/2153-3539.194835  PMID:27994937
Objective: Within the information technology (IT) industry, best practices and standards are constantly evolving and being refined. In contrast, computer technology utilized within the healthcare industry often evolves at a glacial pace, with reduced opportunities for justified innovation. Although the use of timely technology refreshes within an enterprise's overall technology stack can be costly, thoughtful adoption of select technologies with a demonstrated return on investment can be very effective in increasing productivity and at the same time, reducing the burden of maintenance often associated with older and legacy systems. In this brief technical communication, we introduce the concept of microservices as applied to the ecosystem of data analysis pipelines. Microservice architecture is a framework for dividing complex systems into easily managed parts. Each individual service is limited in functional scope, thereby conferring a higher measure of functional isolation and reliability to the collective solution. Moreover, maintenance challenges are greatly simplified by virtue of the reduced architectural complexity of each constitutive module. This fact notwithstanding, rendered overall solutions utilizing a microservices-based approach provide equal or greater levels of functionality as compared to conventional programming approaches. Bioinformatics, with its ever-increasing demand for performance and new testing algorithms, is the perfect use-case for such a solution. Moreover, if promulgated within the greater development community as an open-source solution, such an approach holds potential to be transformative to current bioinformatics software development. Context: Bioinformatics relies on nimble IT framework which can adapt to changing requirements. Aims: To present a well-established software design and deployment strategy as a solution for current challenges within bioinformatics Conclusions: Use of the microservices framework is an effective methodology for the fabrication and implementation of reliable and innovative software, made possible in a highly collaborative setting.
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Technical Note: Pathology report data extraction from relational database using R, with extraction from reports on melanoma of skin as an example
Jay J Ye
J Pathol Inform 2016, 7:44 (21 October 2016)
DOI:10.4103/2153-3539.192822  PMID:28066684
Background: Different methods have been described for data extraction from pathology reports with varying degrees of success. Here a technique for directly extracting data from relational database is described. Methods: Our department uses synoptic reports modified from College of American Pathologists (CAP) Cancer Protocol Templates to report most of our cancer diagnoses. Choosing the melanoma of skin synoptic report as an example, R scripting language extended with RODBC package was used to query the pathology information system database. Reports containing melanoma of skin synoptic report in the past 4 and a half years were retrieved and individual data elements were extracted. Using the retrieved list of the cases, the database was queried a second time to retrieve/extract the lymph node staging information in the subsequent reports from the same patients. Results: 426 synoptic reports corresponding to unique lesions of melanoma of skin were retrieved, and data elements of interest were extracted into an R data frame. The distribution of Breslow depth of melanomas grouped by year is used as an example of intra-report data extraction and analysis. When the new pN staging information was present in the subsequent reports, 82% (77/94) was precisely retrieved (pN0, pN1, pN2 and pN3). Additional 15% (14/94) was retrieved with certain ambiguity (positive or knowing there was an update). The specificity was 100% for both. The relationship between Breslow depth and lymph node status was graphed as an example of lesion-specific multi-report data extraction and analysis. Conclusions: R extended with RODBC package is a simple and versatile approach well-suited for the above tasks. The success or failure of the retrieval and extraction depended largely on whether the reports were formatted and whether the contents of the elements were consistently phrased. This approach can be easily modified and adopted for other pathology information systems that use relational database for data management.
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Review Article: Computer-based image analysis in breast pathology
Ziba Gandomkar, Patrick C Brennan, Claudia Mello-Thoms
J Pathol Inform 2016, 7:43 (21 October 2016)
DOI:10.4103/2153-3539.192814  PMID:28066683
Whole slide imaging (WSI) has the potential to be utilized in telepathology, teleconsultation, quality assurance, clinical education, and digital image analysis to aid pathologists. In this paper, the potential added benefits of computer-assisted image analysis in breast pathology are reviewed and discussed. One of the major advantages of WSI systems is the possibility of doing computer-based image analysis on the digital slides. The purpose of computer-assisted analysis of breast virtual slides can be (i) segmentation of desired regions or objects such as diagnostically relevant areas, epithelial nuclei, lymphocyte cells, tubules, and mitotic figures, (ii) classification of breast slides based on breast cancer (BCa) grades, the invasive potential of tumors, or cancer subtypes, (iii) prognosis of BCa, or (iv) immunohistochemical quantification. While encouraging results have been achieved in this area, further progress is still required to make computer-based image analysis of breast virtual slides acceptable for clinical practice.
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Original Article: Detecting and segmenting cell nuclei in two-dimensional microscopy images
Chi Liu, Fei Shang, John A Ozolek, Gustavo K Rohde
J Pathol Inform 2016, 7:42 (21 October 2016)
DOI:10.4103/2153-3539.192810  PMID:28066682
Introduction: Cell nuclei are important indicators of cellular processes and diseases. Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted from microscopy images. Given the wide variety of nuclei appearance in different organs and staining procedures, a plethora of methods have been described in the literature to improve the segmentation accuracy and robustness. Materials and Methods: In this paper, we propose an unsupervised method for cell nuclei detection and segmentation in two-dimensional microscopy images. The nuclei in the image are detected automatically using a matching-based method. Next, edge maps are generated at multiple image blurring levels followed by edge selection performed in polar space. The nuclei contours are refined iteratively in the constructed edge pyramid. The validation study was conducted over two cell nuclei datasets with manual labeling, including 25 hematoxylin and eosin-stained liver histopathology images and 35 Papanicolaou-stained thyroid images. Results: The nuclei detection accuracy was measured by miss rate, and the segmentation accuracy was evaluated by two types of error metrics. Overall, the nuclei detection efficiency of the proposed method is similar to the supervised template matching method. In comparison to four existing state-of-the-art segmentation methods, the proposed method performed the best with average segmentation error 10.34% and 0.33 measured by area error rate and normalized sum of distances (×10). Conclusion: Quantitative analysis showed that the method is automatic and accurate when segmenting cell nuclei from microscopy images with noisy background and has the potential to be used in clinic settings.
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Erratum: Erratum: Antibody supervised deep learning for quantification of tumor infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

J Pathol Inform 2016, 7:41 (28 September 2016)
DOI:10.4103/2153-3539.191031  PMID:27761297
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Original Article: Rates of provision of clinical information in the skin biopsy requisition form and corresponding encounter visit note
Meredith A Olson, Christine M Lohse, Nneka I Comfere
J Pathol Inform 2016, 7:40 (1 September 2016)
DOI:10.4103/2153-3539.189705  PMID:27688931
Background: The skin biopsy requisition form (RF) serves as a key communication tool for transfer of relevant information related to skin biopsy between clinicians and pathologists. Clinical information in the skin biopsy RF is frequently missing or incomplete. Objective: To determine the rates of provision of critical clinical information necessary for histopathologic interpretation in the skin biopsy RF and encounter visit note (EVN). Methods: A retrospective review of 300 RFs and corresponding EVNs from May 1 to 7, 2012, in a tertiary care dermatology practice. Results: Age (100%), lesion location (100%), and clinical impression (93%) were the most commonly supplied elements in the RF and EVN. Clinical elements that were commonly not provided in the RF but present in the EVN included sampling method - partial versus complete (46%), duration of lesion (54%), morphology of lesion (97%), clinical symptoms (63%), clinical photos (63%), previous clinical (97%), and dermatopathologic diagnoses (82%). Limitations: Retrospective study design. Conclusions: These data suggest that while missing critical clinical information in the RF is often present in the EVN, some information is still not present in either source.
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Letter to Editor: A novel leadership fellowship in digital pathology
Bethany Jill Williams, Darren Treanor
J Pathol Inform 2016, 7:39 (1 September 2016)
DOI:10.4103/2153-3539.189704  PMID:27688930
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Research Article: Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
Riku Turkki, Nina Linder, Panu E Kovanen, Teijo Pellinen, Johan Lundin
J Pathol Inform 2016, 7:38 (1 September 2016)
DOI:10.4103/2153-3539.189703  PMID:27688929
Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Methods: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. Results: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (k = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (k = 0.78). Conclusions: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.
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Technical Note: Experience of maintaining laboratory educational website's sustainability
Izak B Dimenstein
J Pathol Inform 2016, 7:37 (1 September 2016)
DOI:10.4103/2153-3539.189702  PMID:27688928
Laboratory methodology websites are specialized niche websites. The visibility of a niche website transforms it into an authority site on a particular "niche of knowledge." This article presents some ways in which a laboratory methodology website can maintain its sustainability. The optimal composition of the website includes a basic content, a blog, and an ancillary part. This article discusses experimenting with the search engine optimization query results page. Strategic placement of keywords and even phrases, as well as fragmentation of the post's material, can improve the website's visibility to search engines. Hyperlinks open a chain reaction of additional links and draw attention to the previous posts. Publications in printed periodicals are a substantial part of a niche website presence on the Internet. Although this article explores a laboratory website on the basis of our hands-on expertise maintaining "Grossing Technology in Surgical Pathology" ( website with a high volume of traffic for more than a decade, the recommendations presented here for developing an authority website can be applied to other professional specialized websites. The authority websites visibility and sustainability are preconditions for aggregating them in a specialized educational laboratory portal.
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Technical Note: A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix
Akira Saito, Yasushi Numata, Takuya Hamada, Tomoyoshi Horisawa, Eric Cosatto, Hans-Peter Graf, Masahiko Kuroda, Yoichiro Yamamoto
J Pathol Inform 2016, 7:36 (1 September 2016)
DOI:10.4103/2153-3539.189699  PMID:27688927
Background: Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. Methods and Results: In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. Conclusion: CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.
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Editorial: The coming paradigm shift: A transition from manual to automated microscopy
Navid Farahani, Corey E Monteith
J Pathol Inform 2016, 7:35 (1 September 2016)
DOI:10.4103/2153-3539.189698  PMID:27688926
The field of pathology has used light microscopy (LM) extensively since the mid-19 th century for examination of histological tissue preparations. This technology has remained the foremost tool in use by pathologists even as other fields have undergone a great change in recent years through new technologies. However, as new microscopy techniques are perfected and made available, this reliance on the standard LM will likely begin to change. Advanced imaging involving both diffraction-limited and subdiffraction techniques are bringing nondestructive, high-resolution, molecular-level imaging to pathology. Some of these technologies can produce three-dimensional (3D) datasets from sampled tissues. In addition, block-face/tissue-sectioning techniques are already providing automated, large-scale 3D datasets of whole specimens. These datasets allow pathologists to see an entire sample with all of its spatial information intact, and furthermore allow image analysis such as detection, segmentation, and classification, which are impossible in standard LM. It is likely that these technologies herald a major paradigm shift in the field of pathology.
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Book Review: Book review on "digital pathology": Historical perspectives, current concepts, & future applications
Paul J van Diest
J Pathol Inform 2016, 7:34 (23 August 2016)
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Abstracts: Pathology Informatics Summit 2016 Highly accessed article
Jeremy Molligan, Robert Stapp, Miraj Patel, Jack London, Chirayu Goswami, James Evans, Stephen Peiper
J Pathol Inform 2016, 7:33 (28 July 2016)
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Research Article: Improving the creation and reporting of structured findings during digital pathology review
Ida Cervin, Jesper Molin, Claes Lundstrom
J Pathol Inform 2016, 7:32 (26 July 2016)
DOI:10.4103/2153-3539.186917  PMID:27563491
Background: Today, pathology reporting consists of many separate tasks, carried out by multiple people. Common tasks include dictation during case review, transcription, verification of the transcription, report distribution, and report the key findings to follow-up registries. Introduction of digital workstations makes it possible to remove some of these tasks and simplify others. This study describes the work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Methods: We explored the possibility to have a digital tool that simplifies image review by assisting note-taking, and with minimal extra effort, populates a structured report. Thus, our prototype sees reporting as an activity interleaved with image review rather than a separate final step. We created an interface to collect, sort, and display findings for the most common reporting needs, such as tumor size, grading, and scoring. Results: The interface was designed to reduce the need to retain partial findings in the head or on paper, while at the same time be structured enough to support automatic extraction of key findings for follow-up registry reporting. The final prototype was evaluated with two pathologists, diagnosing complicated partial mastectomy cases. The pathologists experienced that the prototype aided them during the review and that it created a better overall workflow. Conclusions: These results show that it is feasible to simplify the reporting tasks in a way that is not distracting, while at the same time being able to automatically extract the key findings. This simplification is possible due to the realization that the structured format needed for automatic extraction of data can be used to offload the pathologists' working memory during the diagnostic review.
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Technical Note: NDER: A novel web application using annotated whole slide images for rapid improvements in human pattern recognition
Nicholas P Reder, Daniel Glasser, Suzanne M Dintzis, Mara H Rendi, Rochelle L Garcia, Jonathan C Henriksen, Mark R Kilgore
J Pathol Inform 2016, 7:31 (26 July 2016)
DOI:10.4103/2153-3539.186913  PMID:27563490
Context: Whole-slide images (WSIs) present a rich source of information for education, training, and quality assurance. However, they are often used in a fashion similar to glass slides rather than in novel ways that leverage the advantages of WSI. We have created a pipeline to transform annotated WSI into pattern recognition training, and quality assurance web application called novel diagnostic electronic resource (NDER). Aims: Create an efficient workflow for extracting annotated WSI for use by NDER, an attractive web application that provides high-throughput training. Materials and Methods: WSI were annotated by a resident and classified into five categories. Two methods of extracting images and creating image databases were compared. Extraction Method 1: Manual extraction of still images and validation of each image by four breast pathologists. Extraction Method 2: Validation of annotated regions on the WSI by a single experienced breast pathologist and automated extraction of still images tagged by diagnosis. The extracted still images were used by NDER. NDER briefly displays an image, requires users to classify the image after time has expired, then gives users immediate feedback. Results: The NDER workflow is efficient: annotation of a WSI requires 5 min and validation by an expert pathologist requires An additional one to 2 min. The pipeline is highly automated, with only annotation and validation requiring human input. NDER effectively displays hundreds of high-quality, high-resolution images and provides immediate feedback to users during a 30 min session. Conclusions: NDER efficiently uses annotated WSI to rapidly increase pattern recognition and evaluate for diagnostic proficiency.
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Research Article: Comparing whole slide digital images versus traditional glass slides in the detection of common microscopic features seen in dermatitis
Nikki S Vyas, Michael Markow, Carlos Prieto-Granada, Sudeep Gaudi, Leslie Turner, Paul Rodriguez-Waitkus, Jane L Messina, Drazen M Jukic
J Pathol Inform 2016, 7:30 (26 July 2016)
DOI:10.4103/2153-3539.186909  PMID:27563489
Background: The quality and limitations of digital slides are not fully known. We aimed to estimate intrapathologist discrepancy in detecting specific microscopic features on glass slides and digital slides created by scanning at ×20. Methods: Hematoxylin and eosin and periodic acid-Schiff glass slides were digitized using the Mirax Scan (Carl Zeiss Inc., Germany). Six pathologists assessed 50-71 digital slides. We recorded objective magnification, total time, and detection of the following: Mast cells; eosinophils; plasma cells; pigmented macrophages; melanin in the epidermis; fungal bodies; neutrophils; civatte bodies; parakeratosis; and sebocytes. This process was repeated using the corresponding glass slides after 3 weeks. The diagnosis was not required. Results: The mean time to assess digital slides was 176.77 s and 137.61 s for glass slides (P < 0.001, 99% confidence interval [CI]). The mean objective magnification used to detect features using digital slides was 18.28 and 14.07 for glass slides (P < 0.001, 99.99% CI). Parakeratosis, civatte bodies, pigmented macrophages, melanin in the epidermis, mast cells, eosinophils, plasma cells, and neutrophils, were identified at lower objectives on glass slides (P = 0.023-0.001, 95% CI). Average intraobserver concordance ranged from κ = 0.30 to κ = 0.78. Features with poor to fair average concordance were: Melanin in the epidermis (κ = 0.15-0.58); plasma cells (κ = 0.15-0.49); and neutrophils (κ = 0.12-0.48). Features with moderate average intrapathologist concordance were: parakeratosis (κ = 0.21-0.61); civatte bodies (κ = 0.21-0.71); pigment-laden macrophages (κ = 0.34-0.66); mast cells (κ = 0.29-0.78); and eosinophils (κ = 0.31-0.79). The average intrapathologist concordance was good for sebocytes (κ = 0.51-1.00) and fungal bodies (κ = 0.47-0.76). Conclusions: Telepathology using digital slides scanned at ×20 is sufficient for detection of histopathologic features routinely encountered in dermatitis cases, though less efficient than glass slides.
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Original Article: Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases Highly accessed article
Andrew Janowczyk, Anant Madabhushi
J Pathol Inform 2016, 7:29 (26 July 2016)
DOI:10.4103/2153-3539.186902  PMID:27563488
Background: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results : Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). Conclusion: This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.
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Original Article: Clinically-inspired automatic classification of ovarian carcinoma subtypes
Aicha BenTaieb, Masoud S Nosrati, Hector Li-Chang, David Huntsman, Ghassan Hamarneh
J Pathol Inform 2016, 7:28 (26 July 2016)
DOI:10.4103/2153-3539.186899  PMID:27563487
Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists' workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists' workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier's confusion (confusion matrix) between the five different ovarian carcinoma subtypes agrees with clinician's confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician's diagnostic procedure by providing a second opinion.
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Research Article: Pathology informatics essentials for residents: A flexible informatics curriculum linked to accreditation council for graduate medical education milestones
Walter H Henricks, Donald S Karcher, James H Harrison, John H Sinard, Michael W Riben, Philip J Boyer, Sue Plath, Arlene Thompson, Liron Pantanowitz
J Pathol Inform 2016, 7:27 (6 July 2016)
DOI:10.4103/2153-3539.185673  PMID:27563486
Context: Recognition of the importance of informatics to the practice of pathology has surged. Training residents in pathology informatics have been a daunting task for most residency programs in the United States because faculty often lacks experience and training resources. Nevertheless, developing resident competence in informatics is essential for the future of pathology as a specialty. Objective: The objective of the study is to develop and deliver a pathology informatics curriculum and instructional framework that guides pathology residency programs in training residents in critical pathology informatics knowledge and skills and meets Accreditation Council for Graduate Medical Education Informatics Milestones. Design: The College of American Pathologists, Association of Pathology Chairs, and Association for Pathology Informatics formed a partnership and expert work group to identify critical pathology informatics training outcomes and to create a highly adaptable curriculum and instructional approach, supported by a multiyear change management strategy. Results: Pathology Informatics Essentials for Residents (PIER) is a rigorous approach for educating all pathology residents in important pathology informatics knowledge and skills. PIER includes an instructional resource guide and toolkit for incorporating informatics training into residency programs that vary in needs, size, settings, and resources. PIER is available at (accessed April 6, 2016). Conclusions: PIER is an important contribution to informatics training in pathology residency programs. PIER introduces pathology trainees to broadly useful informatics concepts and tools that are relevant to practice. PIER provides residency program directors with a means to implement a standardized informatics training curriculum, to adapt the approach to local program needs, and to evaluate resident performance and progress over time.
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