Background: Accurate diagnosis of melanocytic lesions is challenging, even for expert pathologists. Nowadays, whole-slide imaging (WSI) is used for routine clinical pathology diagnosis in several laboratories. One of the limitations of WSI, as it is most often used, is the lack of a multiplanar focusing option. In this study, we aim to establish the diagnostic accuracy of WSI for melanocytic lesions and investigate the potential accuracy increase of z-stack scanning. Z-stack enables pathologists to use a software focus adjustment, comparable to the fine-focus knob of a conventional light microscope. Materials and Methods: Melanocytic lesions (n = 102) were selected from our pathology archives: 35 nevi, 5 spitzoid tumors of unknown malignant potential, and 62 malignant melanomas, including 10 nevoid melanomas. All slides were scanned at a magnification comparable to use of a ×40 objective, in z-stack mode. A ground truth diagnosis was established on the glass slides by four academic dermatopathologists with a special interest in the diagnosis of melanoma. Six nonacademic surgical pathologists subspecialized in dermatopathology examined the cases by WSI. Results: An expert consensus diagnosis was achieved in 99 (97%) of cases. Concordance rates between surgical pathologists and the ground truth varied between 75% and 90%, excluding nevoid melanoma cases. Concordance rates of nevoid melanoma varied between 10% and 80%. Pathologists used the software focusing option in 7%–28% of cases, which in 1 case of nevoid melanoma resulted in correcting a misdiagnosis after finding a dermal mitosis. Conclusion: Diagnostic accuracy of melanocytic lesions based on glass slides and WSI is comparable with previous publications. A large variability in diagnostic accuracy of nevoid melanoma does exist. Our results show that z-stack scanning, in general, does not increase the diagnostic accuracy of melanocytic.

Whole-slide images (WSIs) are a rich new source of biomedical imaging data. The use of automated systems to classify and segment WSIs has recently come to forefront of the pathology research community. While digital slides have obvious educational and clinical uses, their most exciting potential lies in the application of quantitative computational tools to automate search tasks, assist in classic diagnostic classification tasks, and improve prognosis and theranostics. An essential step in enabling these advancements is to apply advances in machine learning and artificial intelligence from other fields to previously inaccessible pathology datasets, thereby enabling the application of new technologies to solve persistent diagnostic challenges in pathology. Here, we applied convolutional neural networks to differentiate between two forms of melanocytic lesions (Spitz and conventional). Classification accuracy at the patch level was 99.0%–2% when applied to WSI. Importantly, when the model was trained without careful image curation by a pathologist, the training took significantly longer and had lower overall performance. These results highlight the utility of augmented human intelligence in digital pathology applications, and the critical role pathologists will play in the evolution of computational pathology algorithms.

Background: Determining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography. Materials and Methods: Relatively low-resolution WSI scans (0.50 μm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography. Results: The automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation (R² = 0.9916) and by agreement with a pathologist (R² = 0.8605) as well as through assessment of counting function using an independently tuned object counting algorithm (OCA) (R² = 0.9482). Limits of agreement analysis support method interchangeability. MF counts obtained led to accurate patient survival prediction in all (n = 30) except one case. By contrast, more variable performance was documented when several pathologists examined similar cases using microscopy (pair-wise correlations, rho range = 0.7597–0.9286). Conclusions: Automated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.

Background: In hospital transfusion services, visualization of blood product inventory in the form of web-based dashboards has the potential to improve the workflow and efficiency of blood product inventory management. While off-the-shelf “business intelligence” solutions by external vendors may offer the ability to display and analyze blood bank inventory data, laboratories may lack resources to readily access this technology. Using in-house talent, our transfusion service developed real-time, web-based dashboards to replace manual processes for managing both blood product inventory and cooler tracking at two large academic hospital blood banks. Methods: Dashboards were developed using Hypertext Markup Language, Cascading Style Sheets, and Hypertext Preprocessor scripting/programming languages. Data are extracted in real time from Sunquest (v7.3) Laboratory Information Systems Database (InterSystems Cache) and are refreshed every 2 min. Data are hosted internally by our institution's web servers and are accessed on a webpage via Microsoft Group Policy shortcuts. Results: Dashboards were designed and implemented to provide a fully customizable, dynamic, and secure method of displaying blood product inventory and blood product cooler status. Transfusion service staff utilized dashboard data to maintain adequate blood product supply, modify blood product replacement orders to prevent excess inventory, and transfer short-dated blood products between our facilities to minimize wastage. Conclusions: Dashboard technology can be readily implemented at hospital transfusion services with minimal capital expenditure. The implementation of real-time web-based dashboards for blood product inventory and cooler management at our centers facilitated on-demand blood product monitoring and replaced a tedious, manual process with a user-friendly and intuitive electronic tool.