Journal of Pathology Informatics Journal of Pathology Informatics
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Review of the current state of whole slide imaging in pathology
Liron Pantanowitz, Paul N Valenstein, Andrew J Evans, Keith J Kaplan, John D Pfeifer, David C Wilbur, Laura C Collins, Terence J Colgan
2011, 2:36 (13 August 2011)
DOI:10.4103/2153-3539.83746  PMID:21886892
Whole slide imaging (WSI), or "virtual" microscopy, involves the scanning (digitization) of glass slides to produce "digital slides". WSI has been advocated for diagnostic, educational and research purposes. When used for remote frozen section diagnosis, WSI requires a thorough implementation period coupled with trained support personnel. Adoption of WSI for rendering pathologic diagnoses on a routine basis has been shown to be successful in only a few "niche" applications. Wider adoption will most likely require full integration with the laboratory information system, continuous automated scanning, high-bandwidth connectivity, massive storage capacity, and more intuitive user interfaces. Nevertheless, WSI has been reported to enhance specific pathology practices, such as scanning slides received in consultation or of legal cases, of slides to be used for patient care conferences, for quality assurance purposes, to retain records of slides to be sent out or destroyed by ancillary testing, and for performing digital image analysis. In addition to technical issues, regulatory and validation requirements related to WSI have yet to be adequately addressed. Although limited validation studies have been published using WSI there are currently no standard guidelines for validating WSI for diagnostic use in the clinical laboratory. This review addresses the current status of WSI in pathology related to regulation and validation, the provision of remote and routine pathologic diagnoses, educational uses, implementation issues, and the cost-benefit analysis of adopting WSI in routine clinical practice.
  75 14,258 2,625
Next generation sequencing in clinical medicine: Challenges and lessons for pathology and biomedical informatics
Rama R Gullapalli, Ketaki V Desai, Lucas Santana-Santos, Jeffrey A Kant, Michael J Becich
2012, 3:40 (31 October 2012)
DOI:10.4103/2153-3539.103013  PMID:23248761
The Human Genome Project (HGP) provided the initial draft of mankind's DNA sequence in 2001. The HGP was produced by 23 collaborating laboratories using Sanger sequencing of mapped regions as well as shotgun sequencing techniques in a process that occupied 13 years at a cost of ~$3 billion. Today, Next Generation Sequencing (NGS) techniques represent the next phase in the evolution of DNA sequencing technology at dramatically reduced cost compared to traditional Sanger sequencing. A single laboratory today can sequence the entire human genome in a few days for a few thousand dollars in reagents and staff time. Routine whole exome or even whole genome sequencing of clinical patients is well within the realm of affordability for many academic institutions across the country. This paper reviews current sequencing technology methods and upcoming advancements in sequencing technology as well as challenges associated with data generation, data manipulation and data storage. Implementation of routine NGS data in cancer genomics is discussed along with potential pitfalls in the interpretation of the NGS data. The overarching importance of bioinformatics in the clinical implementation of NGS is emphasized. [7] We also review the issue of physician education which also is an important consideration for the successful implementation of NGS in the clinical workplace. NGS technologies represent a golden opportunity for the next generation of pathologists to be at the leading edge of the personalized medicine approaches coming our way. Often under-emphasized issues of data access and control as well as potential ethical implications of whole genome NGS sequencing are also discussed. Despite some challenges, it's hard not to be optimistic about the future of personalized genome sequencing and its potential impact on patient care and the advancement of knowledge of human biology and disease in the near future.
  48 49,643 5,572
Digital images and the future of digital pathology
Liron Pantanowitz
2010, 1:15 (10 August 2010)
DOI:10.4103/2153-3539.68332  PMID:20922032
  39 7,770 1,956
Automated quantification of aligned collagen for human breast carcinoma prognosis
Jeremy S Bredfeldt, Yuming Liu, Matthew W Conklin, Patricia J Keely, Thomas R Mackie, Kevin W Eliceiri
2014, 5:28 (28 August 2014)
DOI:10.4103/2153-3539.139707  PMID:25250186
Background: Mortality in cancer patients is directly attributable to the ability of cancer cells to metastasize to distant sites from the primary tumor. This migration of tumor cells begins with a remodeling of the local tumor microenvironment, including changes to the extracellular matrix and the recruitment of stromal cells, both of which facilitate invasion of tumor cells into the bloodstream. In breast cancer, it has been proposed that the alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. Here, we implement the semi-automated acquisition and analysis of this TACS candidate biomarker and demonstrate a protocol that will allow consistent scoring to be performed throughout large patient cohorts. Methods: Using large field of view high resolution microscopy techniques, image processing and supervised learning methods, we are able to quantify and score features of collagen fiber alignment with respect to adjacent tumor-stromal boundaries. Results: Our semi-automated technique produced scores that have statistically significant correlation with scores generated by a panel of three human observers. In addition, our system generated classification scores that accurately predicted survival in a cohort of 196 breast cancer patients. Feature rank analysis reveals that TACS positive fibers are more well-aligned with each other, are of generally lower density, and terminate within or near groups of epithelial cells at larger angles of interaction. Conclusion: These results demonstrate the utility of a supervised learning protocol for streamlining the analysis of collagen alignment with respect to tumor stromal boundaries.
  35 4,247 847
Mitosis detection in breast cancer histological images An ICPR 2012 contest
Ludovic Roux, Daniel Racoceanu, Nicolas Loménie, Maria Kulikova, Humayun Irshad, Jacques Klossa, Frédérique Capron, Catherine Genestie, Gilles Le Naour, Metin N Gurcan
2013, 4:8 (30 May 2013)
DOI:10.4103/2153-3539.112693  PMID:23858383
Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Results : Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Conclusions : Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.
  35 9,014 1,475
Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: How reliable is it?
Mark C Lloyd, Pushpa Allam-Nandyala, Chetna N Purohit, Nancy Burke, Domenico Coppola, Marilyn M Bui
2010, 1:29 (23 December 2010)
DOI:10.4103/2153-3539.74186  PMID:21221174
Background : Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) are important and well-established prognostic and predictive biomarkers for breast cancers and routinely tested on patient's tumor samples by immunohistochemical (IHC) study. The accuracy of these test results has substantial impact on patient management. A critical factor that contributes to the result is the interpretation (scoring) of IHC. This study investigates how computerized image analysis can play a role in a reliable scoring, and identifies potential pitfalls with common methods. Materials and Methods : Whole slide images of 33 invasive ductal carcinoma (IDC) (10 ER and 23 HER2) were scored by pathologist under the light microscope and confirmed by another pathologist. The HER2 results were additionally confirmed by fluorescence in situ hybridization (FISH). The scoring criteria were adherent to the guidelines recommended by the American Society of Clinical Oncology/College of American Pathologists. Whole slide stains were then scored by commercially available image analysis algorithms from Definiens (Munich, Germany) and Aperio Technologies (Vista, CA, USA). Each algorithm was modified specifically for each marker and tissue. The results were compared with the semi-quantitative manual scoring, which was considered the gold standard in this study. Results : For HER2 positive group, each algorithm scored 23/23 cases within the range established by the pathologist. For ER, both algorithms scored 10/10 cases within range. The performance of each algorithm varies somewhat from the percentage of staining as compared to the pathologist's reading. Conclusions : Commercially available computerized image analysis can be useful in the evaluation of ER and HER2 IHC results. In order to achieve accurate results either manual pathologist region selection is necessary, or an automated region selection tool must be employed. Specificity can also be gained when strict quality assurance by a pathologist is invested. Quality assurance of image analysis by pathologists is always warranted. Automated image analysis should only be used as adjunct to pathologist's evaluation.
  28 5,979 1,130
Going fully digital: Perspective of a Dutch academic pathology lab
Nikolas Stathonikos, Mitko Veta, André Huisman, Paul J van Diest
2013, 4:15 (29 June 2013)
DOI:10.4103/2153-3539.114206  PMID:23858390
During the last years, whole slide imaging has become more affordable and widely accepted in pathology labs. Digital slides are increasingly being used for digital archiving of routinely produced clinical slides, remote consultation and tumor boards, and quantitative image analysis for research purposes and in education. However, the implementation of a fully digital Pathology Department requires an in depth look into the suitability of digital slides for routine clinical use (the image quality of the produced digital slides and the factors that affect it) and the required infrastructure to support such use (the storage requirements and integration with lab management and hospital information systems). Optimization of digital pathology workflow requires communication between several systems, which can be facilitated by the use of open standards for digital slide storage and scanner management. Consideration of these aspects along with appropriate validation of the use of digital slides for routine pathology can pave the way for pathology departments to go "fully digital." In this paper, we summarize our experiences so far in the process of implementing a fully digital workflow at our Pathology Department and the steps that are needed to complete this process.
  27 4,171 1,000
Automated mitosis detection in histopathology using morphological and multi-channel statistics features
Humayun Irshad
2013, 4:10 (30 May 2013)
DOI:10.4103/2153-3539.112695  PMID:23858385
Context: According to Nottingham grading system, mitosis count plays a critical role in cancer diagnosis and grading. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to improve the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features, which classify mitosis from other objects. Materials and Methods: We propose a framework that includes comprehensive analysis of statistics and morphological features in selected channels of various color spaces that assist pathologists in mitosis detection. In candidate detection phase, we perform Laplacian of Gaussian, thresholding, morphology and active contour model on blue-ratio image to detect and segment candidates. In candidate classification phase, we extract a total of 143 features including morphological, first order and second order (texture) statistics features for each candidate in selected channels and finally classify using decision tree classifier. Results and Discussion: The proposed method has been evaluated on Mitosis Detection in Breast Cancer Histological Images (MITOS) dataset provided for an International Conference on Pattern Recognition 2012 contest and achieved 74% and 71% detection rate, 70% and 56% precision and 72% and 63% F-Measure on Aperio and Hamamatsu images, respectively. Conclusions and Future Work: The proposed multi-channel features computation scheme uses fixed image scale and extracts nuclei features in selected channels of various color spaces. This simple but robust model has proven to be highly efficient in capturing multi-channels statistical features for mitosis detection, during the MITOS international benchmark. Indeed, the mitosis detection of critical importance in cancer diagnosis is a very challenging visual task. In future work, we plan to use color deconvolution as preprocessing and Hough transform or local extrema based candidate detection in order to reduce the number of candidates in mitosis and non-mitosis classes.
  27 5,184 1,074
Classification of mitotic figures with convolutional neural networks and seeded blob features
Christopher D Malon, Eric Cosatto
2013, 4:9 (30 May 2013)
DOI:10.4103/2153-3539.112694  PMID:23858384
Background: The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR) challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral). Methods: Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN). The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. Results : On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. Conclusions : We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.
  25 6,914 1,188
Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013
Sten Thorstenson, Jesper Molin, Claes Lundström
2014, 5:14 (28 March 2014)
DOI:10.4103/2153-3539.129452  PMID:24843825
Recent technological advances have improved the whole slide imaging (WSI) scanner quality and reduced the cost of storage, thereby enabling the deployment of digital pathology for routine diagnostics. In this paper we present the experiences from two Swedish sites having deployed routine large-scale WSI for primary review. At Kalmar County Hospital, the digitization process started in 2006 to reduce the time spent at the microscope in order to improve the ergonomics. Since 2008, more than 500,000 glass slides have been scanned in the routine operations of Kalmar and the neighboring Linköping University Hospital. All glass slides are digitally scanned yet they are also physically delivered to the consulting pathologist who can choose to review the slides on screen, in the microscope, or both. The digital operations include regular remote case reporting by a few hospital pathologists, as well as around 150 cases per week where primary review is outsourced to a private clinic. To investigate how the pathologists choose to use the digital slides, a web-based questionnaire was designed and sent out to the pathologists in Kalmar and Linköping. The responses showed that almost all pathologists think that ergonomics have improved and that image quality was sufficient for most histopathologic diagnostic work. 38 ± 28% of the cases were diagnosed digitally, but the survey also revealed that the pathologists commonly switch back and forth between digital and conventional microscopy within the same case. The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today.
  24 4,567 1,014
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Andrew Janowczyk, Anant Madabhushi
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.
  23 31,038 8,495
Experience with multimodality telepathology at the University of Pittsburgh Medical Center
Liron Pantanowitz, Clayton A Wiley, Anthony Demetris, Andrew Lesniak, Ishtiaque Ahmed, William Cable, Lydia Contis, Anil V Parwani
2012, 3:45 (20 December 2012)
DOI:10.4103/2153-3539.104907  PMID:23372986
Several modes of telepathology exist including static (store-and-forward), dynamic (live video streaming or robotic microscopy), and hybrid technology involving whole slide imaging (WSI). Telepathology has been employed at the University of Pittsburgh Medical Center (UPMC) for over a decade at local, national, and international sites. All modes of telepathology have been successfully utilized to exploit our institutions subspecialty expertise and to compete for pathology services. This article discusses the experience garnered at UPMC with each of these teleconsultation methods. Static and WSI telepathology systems have been utilized for many years in transplant pathology using a private network and client-server architecture. Only minor clinically significant differences of opinion were documented. In hematopathology, the CellaVision® system is used to transmit, via email, static images of blood cells in peripheral blood smears for remote interpretation. While live video streaming has remained the mode of choice for providing immediate adequacy assessment of cytology specimens by telecytology, other methods such as robotic microscopy have been validated and shown to be effective. Robotic telepathology has been extensively used to remotely interpret intra-operative neuropathology consultations (frozen sections). Adoption of newer technology and increased pathologist experience has improved accuracy and deferral rates in teleneuropathology. A digital pathology consultation portal ( was recently created at our institution to facilitate digital pathology second opinion consults, especially for WSI. The success of this web-based tool is the ability to handle vendor agnostic, large image files of digitized slides, and ongoing user-friendly customization for clients and teleconsultants. It is evident that the practice of telepathology at our institution has evolved in concert with advances in technology and user experience. Early and continued adoption of telepathology has promoted additional digital pathology resources that are now being leveraged for other clinical, educational, and research purposes.
  23 4,503 765
OpenSlide: A vendor-neutral software foundation for digital pathology
Adam Goode, Benjamin Gilbert, Jan Harkes, Drazen Jukic, Mahadev Satyanarayanan
2013, 4:27 (27 September 2013)
DOI:10.4103/2153-3539.119005  PMID:24244884
Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.
  23 10,707 1,228
Color standardization in whole slide imaging using a color calibration slide
Pinky A Bautista, Noriaki Hashimoto, Yukako Yagi
2014, 5:4 (31 January 2014)
DOI:10.4103/2153-3539.126153  PMID:24672739
Background: Color consistency in histology images is still an issue in digital pathology. Different imaging systems reproduced the colors of a histological slide differently. Materials and Methods: Color correction was implemented using the color information of the nine color patches of a color calibration slide. The inherent spectral colors of these patches along with their scanned colors were used to derive a color correction matrix whose coefficients were used to convert the pixels' colors to their target colors. Results: There was a significant reduction in the CIELAB color difference, between images of the same H & E histological slide produced by two different whole slide scanners by 3.42 units, P < 0.001 at 95% confidence level. Conclusion: Color variations in histological images brought about by whole slide scanning can be effectively normalized with the use of the color calibration slide.
  21 5,554 1,015
American Telemedicine Association clinical guidelines for telepathology
Liron Pantanowitz, Kim Dickinson, Andrew J Evans, Lewis A Hassell, Walter H Henricks, Jochen K Lennerz, Amanda Lowe, Anil V Parwani, Michael Riben, Daniel Smith, J Mark Tuthill, Ronald S Weinstein, David C Wilbur, Elizabeth A Krupinski, Jordana Bernard
2014, 5:39 (21 October 2014)
DOI:10.4103/2153-3539.143329  PMID:25379345
  18 2,659 634
Autoverification in a core clinical chemistry laboratory at an academic medical center
Matthew D Krasowski, Scott R Davis, Denny Drees, Cory Morris, Jeff Kulhavy, Cheri Crone, Tami Bebber, Iwa Clark, David L Nelson, Sharon Teul, Dena Voss, Dean Aman, Julie Fahnle, John L Blau
2014, 5:13 (28 March 2014)
DOI:10.4103/2153-3539.129450  PMID:24843824
Background: Autoverification is a process of using computer-based rules to verify clinical laboratory test results without manual intervention. To date, there is little published data on the use of autoverification over the course of years in a clinical laboratory. We describe the evolution and application of autoverification in an academic medical center clinical chemistry core laboratory. Subjects and Methods: At the institution of the study, autoverification developed from rudimentary rules in the laboratory information system (LIS) to extensive and sophisticated rules mostly in middleware software. Rules incorporated decisions based on instrument error flags, interference indices, analytical measurement ranges (AMRs), delta checks, dilution protocols, results suggestive of compromised or contaminated specimens, and 'absurd' (physiologically improbable) values. Results: The autoverification rate for tests performed in the core clinical chemistry laboratory has increased over the course of 13 years from 40% to the current overall rate of 99.5%. A high percentage of critical values now autoverify. The highest rates of autoverification occurred with the most frequently ordered tests such as the basic metabolic panel (sodium, potassium, chloride, carbon dioxide, creatinine, blood urea nitrogen, calcium, glucose; 99.6%), albumin (99.8%), and alanine aminotransferase (99.7%). The lowest rates of autoverification occurred with some therapeutic drug levels (gentamicin, lithium, and methotrexate) and with serum free light chains (kappa/lambda), mostly due to need for offline dilution and manual filing of results. Rules also caught very rare occurrences such as plasma albumin exceeding total protein (usually indicative of an error such as short sample or bubble that evaded detection) and marked discrepancy between total bilirubin and the spectrophotometric icteric index (usually due to interference of the bilirubin assay by immunoglobulin (Ig) M monoclonal gammopathy). Conclusions: Our results suggest that a high rate of autoverification is possible with modern clinical chemistry analyzers. The ability to autoverify a high percentage of results increases productivity and allows clinical laboratory staff to focus attention on the small number of specimens and results that require manual review and investigation.
  18 14,293 2,357
Telecytology: Clinical applications, current challenges, and future benefits
Michael Thrall, Liron Pantanowitz, Walid Khalbuss
2011, 2:51 (26 December 2011)
DOI:10.4103/2153-3539.91129  PMID:22276242
Telecytology is the interpretation of cytology material at a distance using digital images. For more than a decade, pioneering efforts to introduce telecytology into clinical practice have been reported. A Medline search for "telecytology" and "cytology" reveals a voluminous literature, though much of what has been published to date is based on technologies that are rapidly becoming obsolete. The technological limitations of previous techniques, including the transmission of static digital images and dynamic streaming images, have limited telecytology to minor niches. The primary problem with these technologies is that the remote viewer can only see a small fraction of the material on the original slides, introducing the possibility of diagnostic error based not only on image quality but also on image selection. Remote robotic microscopy offers one possible solution to this problem, but to date has found limited acceptance, principally attributable to slow operating times. Whole slide imaging seems to be a much more promising solution, though cytology-specific literature regarding its use is still scant. The advent of whole slide imaging opens up new possibilities for telecytology by enabling high-quality images of entire cytology specimens to be available to anyone, anywhere via the Internet. Although challenges remain, especially with regard to capturing the full microscopy experience including multiple planes of focus and sharp high-powered images, rapidly advancing technology promises to overcome these limitations. Increasing application of whole slide imaging technology in surgical pathology will undoubtedly also increase its application to cytology due to the increasing affordability and practicality of the equipment as it serves a larger number of useful roles within a pathology department. The current and expanding applications of telecytology for clinical practice, education, quality assurance, and testing will be reviewed.
  18 8,084 1,027
Implementation of whole slide imaging in surgical pathology: A value added approach
Mike Isaacs, Jochen K Lennerz, Stacey Yates, Walter Clermont, Joan Rossi, John D Pfeifer
2011, 2:39 (24 August 2011)
DOI:10.4103/2153-3539.84232  PMID:21969920
Background: Whole slide imaging (WSI) makes it possible to capture images of an entire histological slide. WSI has established roles in surgical pathology, including support of off-site frozen section interpretation, primary diagnosis, educational activities, and laboratory quality assurance (QA) activities. Analyses of the cost of WSI have traditionally been based solely on direct costs and diagnostic accuracy; however, these types of analyses largely ignore workflow and cost issues that arise as a result of redundancy, the need for additional staffing, and customized software development when WSI is integrated into routine diagnostic surgical pathology. The pre-scan, scan, and post-scan costs; quality control and QA costs; and IT process costs can be significant, and consequently, pathology groups can find it difficult to perform a realistic cost-benefit analysis of adding WSI to their practice. Materials and Methods: In this paper, we report a "value added" approach developed to guide our decisions regarding integration of WSI into surgical pathology practice. The approach focuses on specific operational measures (cost, time, and enhanced patient care) and practice settings (clinical, education, and research) to identify routine activities in which the addition of WSI can provide improvements. Results: When applied to our academic pathology group practice, the value added approach resulted in expanded and improved operations, as demonstrated by outcome based measures. Conclusion: A value added can be used to perform a realistic cost-benefit analysis of integrating WSI into routine surgical pathology practice.
  17 4,124 839
Virtual microscopy using whole-slide imaging as an enabler for teledermatopathology: A paired consultant validation study
Ayman Al Habeeb, Andrew Evans, Danny Ghazarian
2012, 3:2 (29 February 2012)
DOI:10.4103/2153-3539.93399  PMID:22439122
Background: There is a need for telemedicine, particularly in countries with large geographical areas and widely scattered low-density communities as is the case of the Canadian system, particularly if equality of care is to be achieved or the difference gap is to be narrowed between urban centers and more peripheral communities. Aims: 1. To validate teledermatopathology as a diagnostic tool in under-serviced areas; 2. To test its utilization in inflammatory and melanocytic lesions; 3. To compare the impact of 20× (0.5 μm/pixel) and 40× (0.25 μm/pixel) scans on the diagnostic accuracy. Materials and Methods: A total of 103 dermatopathology cases divided into three arms were evaluated by two pathologists and results compared. The first arm consisted of 79 consecutive routine cases (n=79). The second arm consisted of 12 inflammatory skin biopsies (n=12) and the third arm consisted of 12 melanocytic lesions (n=12). Diagnosis concordance was used to evaluate the first arm. Whereas concordance of preset objective findings were used to evaluate the second and third arms. Results: The diagnostic concordance rate for the first arm was 96%. The concordance rates of the objective findings for the second and third arms were 100%. The image quality was deemed superior to light microscopy for 40× scans. Conclusion: The current scanners produce high-resolution images that are adequate for evaluation of a variety of cases of different complexities.
  17 2,694 411
Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues
Manu Jain, Nidhi Shukla, Maryem Manzoor, Sylvie Nadolny, Sushmita Mukherjee
2011, 2:28 (14 June 2011)
DOI:10.4103/2153-3539.82053  PMID:21773059
Background: Here, we report the first use of a commercial prototype of full-field optical coherence tomography called Light-CT TM . Based on the principle of white light interferometry, Light-CT TM generates quick high-resolution three-dimensional tomographic images from unprocessed tissues. Its advantage over the current intra-surgical diagnostic standard, i.e. frozen section analysis, lies in the absence of freezing artifacts, which allows real-time diagnostic impressions, and/or for the tissues to be triaged for subsequent conventional histopathology. Materials and Methods: In this study, we recapitulate known normal histology in nine formalin fixed ex vivo rat organs (skin, heart, lung, liver, stomach, kidney, prostate, urinary bladder, and testis). Large surface and virtually sectioned stacks of images at varying depths were acquired by a pair of 10x/0.3 numerical aperture water immersion objectives, processed and visualized in real time. Results: Normal histology of the following organs was recapitulated by identifying various tissue microstructures. Skin: epidermis, dermal-epidermal junction and hair follicles with surrounding sebaceous glands in the dermis. Stomach: mucosa with surface pits, submucosa, muscularis propria and serosa. Liver: hepatocytes separated by sinusoidal spaces, central veins and portal triad. Kidney: convoluted tubules, medullary rays (straight tubules) and collecting ducts. Prostate: acini and fibro-muscular stroma. Lung: bronchi, bronchioles, alveolar ducts, alveoli and pleura. Urinary bladder: urothelium, lamina propria, muscularis propria, and serosa. Testis: seminiferous tubules with intra-tubular sperms. Conclusion: Light-CT TM is a powerful imaging tool to perform fast histology on fresh and fixed tissues, without introducing artifacts. Its compact size, ease of handling, fast image acquisition and safe incident light levels makes it well-suited for various intra-operative and intra-procedural triaging and decision making applications.
  17 6,815 1,021
Smartphone adapters for digital photomicrography
Somak Roy, Liron Pantanowitz, Milon Amin, Raja R Seethala, Ahmed Ishtiaque, Samuel A Yousem, Anil V Parwani, Ioan Cucoranu, Douglas J Hartman
2014, 5:24 (30 July 2014)
DOI:10.4103/2153-3539.137728  PMID:25191623
Background: Photomicrographs in Anatomic Pathology provide a means of quickly sharing information from a glass slide for consultation, education, documentation and publication. While static image acquisition historically involved the use of a permanently mounted camera unit on a microscope, such cameras may be expensive, need to be connected to a computer, and often require proprietary software to acquire and process images. Another novel approach for capturing digital microscopic images is to use smartphones coupled with the eyepiece of a microscope. Recently, several smartphone adapters have emerged that allow users to attach mobile phones to the microscope. The aim of this study was to test the utility of these various smartphone adapters. Materials and Methods: We surveyed the market for adapters to attach smartphones to the ocular lens of a conventional light microscope. Three adapters (Magnifi, Skylight and Snapzoom) were tested. We assessed the designs of these adapters and their effectiveness at acquiring static microscopic digital images. Results: All adapters facilitated the acquisition of digital microscopic images with a smartphone. The optimal adapter was dependent on the type of phone used. The Magnifi adapters for iPhone were incompatible when using a protective case. The Snapzoom adapter was easiest to use with iPhones and other smartphones even with protective cases. Conclusions: Smartphone adapters are inexpensive and easy to use for acquiring digital microscopic images. However, they require some adjustment by the user in order to optimize focus and obtain good quality images. Smartphone microscope adapters provide an economically feasible method of acquiring and sharing digital pathology photomicrographs.
  17 5,532 1,156
Computerized provider order entry in the clinical laboratory
Jason M Baron, Anand S Dighe
2011, 2:35 (13 August 2011)
DOI:10.4103/2153-3539.83740  PMID:21886891
Clinicians have traditionally ordered laboratory tests using paper-based orders and requisitions. However, paper orders are becoming increasingly incompatible with the complexities, challenges, and resource constraints of our modern healthcare systems and are being replaced by electronic order entry systems. Electronic systems that allow direct provider input of diagnostic testing or medication orders into a computer system are known as Computerized Provider Order Entry (CPOE) systems. Adoption of laboratory CPOE systems may offer institutions many benefits, including reduced test turnaround time, improved test utilization, and better adherence to practice guidelines. In this review, we outline the functionality of various CPOE implementations, review the reported benefits, and discuss strategies for using CPOE to improve the test ordering process. Further, we discuss barriers to the implementation of CPOE systems that have prevented their more widespread adoption.
  16 12,242 1,248
Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach
Humayun Irshad, Sepehr Jalali, Ludovic Roux, Daniel Racoceanu, Lim Joo Hwee, Gilles Le Naour, Frédérique Capron
2013, 4:12 (30 March 2013)
DOI:10.4103/2153-3539.109870  PMID:23766934
Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. Results: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Conclusions: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.
  16 3,684 577
The feasibility of using natural language processing to extract clinical information from breast pathology reports
Julliette M Buckley, Suzanne B Coopey, John Sharko, Fernanda Polubriaginof, Brian Drohan, Ahmet K Belli, Elizabeth M. H. Kim, Judy E Garber, Barbara L Smith, Michele A Gadd, Michelle C Specht, Constance A Roche, Thomas M Gudewicz, Kevin S Hughes
2012, 3:23 (30 June 2012)
DOI:10.4103/2153-3539.97788  PMID:22934236
Objective: The opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports. Approach and Procedure: Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text. Results: There was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders. Conclusion: We have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.
  16 9,519 932
An open-source software program for performing Bonferroni and related corrections for multiple comparisons
Kyle Lesack, Christopher Naugler
2011, 2:52 (26 December 2011)
DOI:10.4103/2153-3539.91130  PMID:22276243
Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for "family-wide significance" using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the "false discovery rate" with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections.
  16 6,467 879
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