Background: The current technology for remote assessment of fine-needle aspiration-rapid on-site evaluation (FNA-ROSE) is limited. Recent advances may provide solutions. This study compared the performance of VisionTek digital microscope (VDM) (Sakura, Japan) and Hamamatsu NanoZoomer C9600-12 single Z-stack digital scan (SZDS) to conventional light microscopy (CLM) for FNA-ROSE. Methods: We assembled sixty FNA cases from the thyroid (n = 16), lymph node (n = 16), pancreas (n = 9), head and neck (n = 9), salivary gland (n = 5), lung (n = 4), and rectum (n = 1) based on a single institution's routine workflow. One Diff-Quik-stained slide was selected for each case. Two board-certified cytopathologists independently evaluated the cases using VDM, SZDS, and CLM. A “washout” period of at least 14 days was placed between the reviews. The results were categorized into satisfactory versus unsatisfactory for adequacy assessment (AA) and unsatisfactory, benign, atypical, suspicious, and malignant for preliminary diagnosis (PD). Results: For AA, the Cohen's kappa statistics (CKS) scores of intermodality agreement (IMA) were 0.74–0.94 (CLM vs. VDM) and 0.86–1 (CLM vs. SZDS). The discordant rates of IMA were 3.3% (4/120) for VDM versus CLM, and 1.7% (2/120) for SZDS versus CLM. For PD, the CKS scores of IMA ranged 0.7–0.93. The overall discordant rates of IMA were 15% (18/120) for CLM versus VDM and 10.8% (13/120) for CLM versus SZDS. The discordant rates of IMA with 2 or higher degrees were 5.8% (7/120) for CLM versus VDM and 1.7% (2/120) for CLM versus SZDS. The average time spent per slide was 270 s for VDM, significantly longer than that for CLM (113 s) or for SZDS (122 s). Conclusions: Our data demonstrate that both VDM and SZDS are suitable to provide AA and reasonable PD evaluation. VDM, however, has a significantly longer turnaround time and worse diagnostic performance. The study demonstrates both the potentials and challenges of using VDM and SZDS for FNA-ROSE.

Background: Accurate digital pathology image analysis depends on high-quality images. As such, it is imperative to obtain digital images with high resolution for downstream data analysis. While hematoxylin and eosin (H&E)-stained tissue section slides from solid tumors contain three-dimensional information, these data have been ignored in digital pathology. In addition, in cytology and bone marrow aspirate smears, the three-dimensional nature of the specimen has precluded efficient analysis of such morphologic data. An individual image snapshot at a single focal distance is often not sufficient for accurate diagnoses and multiple whole-slide images at different focal distances are necessary for diagnostics. Materials and Methods: We describe a novel computational pipeline and processing program for obtaining a super-resolved image from multiple static images at different z-planes in overlapping but separate frames. This program, MULTI-Z, performs image alignment, Gaussian smoothing, and Laplacian filtering to construct a final super-resolution image from multiple images. Results: We applied this algorithm and program to images of cytology and H&E-stained sections and demonstrated significant improvements in both resolution and image quality by objective data analyses (24% increase in sharpness and focus). Conclusions: With the use of our program, super-resolved images of cytology and H&E-stained tissue sections can be obtained to potentially allow for more optimal downstream computational analysis. This method is applicable to whole-slide scanned images.

Introduction/Background: Cancer registries in the US collect timely and systematic data on new cancer cases, extent of disease, staging, biomarker status, treatment, survival, and mortality of cancer cases. Existing methodologies for accessing local cancer registry data for research are time-consuming and often rely on the manual merging of data by staff registrars. In addition, existing registries do not provide direct access to these data nor do they routinely provide linkage to discrete electronic health record (EHR) data, reports, or imaging data. Automation of such linkage can provide an impressive data resource and make valuable data available for translational cancer research. Methods: The UPMC Network Cancer Registry collects highly structured, longitudinal data on all reportable cancer patients, from the point of the diagnosis throughout treatment and follow-up/outcomes. Using commercial registry software, we collect data in compliance with standards governed by the North American Association of Central Cancer Registries. This standardization ensures that the data are highly structured with standard coding and collection methods, which support data exchange among central cancer registries and the Centers for Disease Control and Prevention. Results: At the UPMC Hillman Cancer Center and University of Pittsburgh, we explored the feasibility of linking this well-curated, structured cancer registry data with unstructured text (i.e., pathology and radiology reports), using the Text Information Extraction System (TIES). We used the TIES platform to integrate breast cancer cases from the UPMC Network Cancer Registry system and then combine these data with other EHR data as a pilot use case that can be replicated for other cancers. Conclusions: As a result of this integration, we now have a single searchable repository of information for breast cancer patients from the UPMC registry, combined with their pathology and radiology reports. The system that we developed is easily scalable to other health systems and cancer centers.

Introduction: A whole slide image (WSI) is typically comprised of a macro image (low-power snapshot of the entire glass slide) and stacked tiles in a pyramid structure (with the lowest resolution thumbnail at the top). The macro image shows the label and all pieces of tissue on the slide. Many whole slide scanner vendors do not readily show the macro overview to pathologists. We demonstrate that failure to do so may result in a serious misdiagnosis. Materials and Methods: Various examples of errors were accumulated that occurred during the digitization of glass slides where the virtual slide differed from the macro image of the original glass slide. Such examples were retrieved from pathology laboratories using different types of scanners in the USA, Canada, Europe, and Asia. Results: The reasons for image errors were categorized into technical problems (e.g., automatic tissue finder failure, image mismatches, and poor scan coverage) and human operator mistakes (e.g., improper manual region of interest selection). These errors were all detected because they were highlighted in the macro image. Conclusion: Our experience indicates that WSI can be subject to inadvertent errors related to glitches in scanning slides, corrupt images, or mistakes made by humans when scanning slides. Displaying the macro image that accompanies WSIs is critical from a quality control perspective in digital pathology practice as this can help detect these serious image-related problems and avoid compromised diagnoses.

Introduction: The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated. Materials and Methods: To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process. Results: We describe several “use cases” that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms. Conclusions: The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.

Background: In an effort to provide improved user experience and system reliability at a moderate cost, our department embarked on targeted upgrades of a total of 87 computers over a period of 3 years. Upgrades came in three forms: (i) replacement of the computer with newer architecture, (ii) replacement of the computer's hard drive with a solid-state drive (SSD), or (iii) replacement of the computer with newer architecture and a SSD. Methods: We measured the impact of each form of upgrade on a set of pathology-relevant tasks that fell into three categories: standard use, whole-slide navigation, and whole-slide analysis. We used time to completion of a task as the primary variable of interest. Results: We found that for most tasks, the SSD upgrade had a greater impact than the upgrade in architecture. This effect was especially prominent for whole-slide viewing, likely due to the way in which most whole-slide viewers cached image tiles. However, other tasks, such as whole-slide image analysis, often relied less on disk input or output and were instead more sensitive to the computer architecture. Conclusions: Based on our experience, we suggest that SSD upgrades are viewed in some settings as a viable alternative to complete computer replacement and recommend that computer replacements in a digital pathology setting are accompanied by an upgrade to SSDs.

Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology. Aim: The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. Subjects and Methods: An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40). Results: The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback. Conclusion: With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.

Background: Recent medical education trends encourage the use of teaching strategies that emphasize student centeredness and self-learning. In this context, the use of new educative technologies is stimulated at the Faculty of Medicine of Eduardo Mondlane University (FMUEM) in Mozambique. The Faculty of Medicine of University of Porto (FMUP) and FMUEM have a long-lasting record of collaborative work. Within this framework, both institutions embarked in a partnership, aimed to develop a blended learning course of pathology for undergraduates, shared between the two faculties and incorporating interactive digital microscopy as a central learning tool. Methods: A core team of faculty members from both institutions identified the existing resources and previous experiences in the two faculties. The Moodle course for students from the University of Porto was the basis to implement the current project. The objective was to develop educational modules of mutual interest, designed for e-learning, followed by a voluntary student's survey conducted in FMUEM to get their perception about the process. Results: We selected contents from the pathology curricula of FMUP and FMUEM that were of mutual interest. We next identified and produced new contents for the shared curricula. The implementation involved joint collaboration and training to prepare the new contents, together with building quizzes for self-evaluation. All the practical sessions were based on the use of interactive digital microscopy. The students have reacted enthusiastically to the incorporation of the online component that increased their performance and motivation for pathology learning. For the students in Porto, the major acquisition was the access to slides from infectious diseases as well as autopsy videos. Conclusions: Our study indicates that students benefited from high-quality educational contents, with emphasis on digital microscopy, in a platform generated in a win-win situation for FMUP and FMUEM.

Background: Telepathology can potentially be utilized as an alternative to having on-site pathology services for rural and regional hospitals. The goal of the study was to validate two small-footprint desktop telepathology systems for remote parathyroid frozen sections. Subjects and Methods: Three pathologists retrospectively diagnosed 76 parathyroidectomy frozen sections of 52 patients from three pathology services in Australia using the “live-view mode” of MikroScan D2 and Aperio LV1 and in-house direct microscopy. The final paraffin section diagnosis served as the “gold standard” for accuracy evaluation. Concordance rates of the telepathology systems with direct microscopy, inter-pathologist and intra-pathologist agreement, and the time taken to report each slide were analyzed. Results: Both telepathology systems showed high diagnostic accuracy (>99%) and high concordance (>99%) with direct microscopy. High inter-pathologist agreement for telepathology systems was demonstrated by overall kappa values of 0.92 for Aperio LV1 and 0.85 for MikroScan D2. High kappa values (from 0.85 to 1) for intra-pathologist agreement within the three systems were also observed. The time taken per slide by Aperio LV1 and MicroScan D2 within three pathologists was about 3.0 times (P < 0.001, 95% confidence interval [CI]: 2.8–3.2) and 7.7 times (P < 0.001, 95% CI: 7.1–8.3) as long as direct microscopy, respectively, while MikroScan D2 took about 2.6 times as long as Aperio LV1 (P < 0.001, 95% CI: 2.4–2.7). All pathologists evaluated Aperio LV1 as being more user-friendly. Conclusions: Telepathology diagnosis of parathyroidectomy frozen sections through small-footprint desktop systems is accurate, reliable, and comparable with in-house direct microscopy. Telepathology systems take longer than direct microscopy; however, the time taken is within clinically acceptable limits. Aperio LV1 takes shorter time than MikroScan D2 and is more user-friendly.