Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.

Background: Determining the site of origin for metastatic well-differentiated neuroendocrine tumors (WDNETs) is challenging, and immunohistochemical (IHC) profiles do not always lead to a definitive diagnosis. We sought to determine if a deep-learning convolutional neural network (CNN) could improve upon established IHC profiles in predicting the site of origin in a cohort of WDNETs from the common primary sites. Materials and Methods: Hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) were created using 215 WDNETs arising from the known primary sites. A CNN trained and tested on 60% (n = 130) and 40% (n = 85) of these cases, respectively. One hundred and seventy-nine cases had TMA tissue remaining for the IHC analysis. These cases were stained with IHC markers pPAX8, CDX2, SATB2, and thyroid transcription factor-1 (markers of pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung WDNET sites of origin, respectively). The CNN diagnosis was deemed correct if it designated a majority or plurality of the tumor area as the known site of origin. The IHC diagnosis was deemed correct if the most specific marker for a particular site of origin met an H-score threshold determined by two pathologists. Results: When all cases were considered, the CNN correctly identified the site of origin at a lower rate compared to IHC (72% vs. 82%, respectively). Of the 85 cases in the CNN test set, 66 had sufficient TMA material for IHC stains, thus 66 cases were available for a direct case-by-case comparison of IHC versus CNN. The CNN correctly identified 70% of these cases, while IHC correctly identified 76%, a finding that was not statistically significant (P = 0.56). Conclusion: A CNN can identify WDNET site of origin at an accuracy rate close to the current gold standard IHC methods.

Background: Gross imaging of surgical specimens is paramount for the accurate gross examination and diagnosis of disease. Optimized imaging workflow can facilitate consistently high-quality gross photographs, especially in high-volume, metropolitan hospitals such as ours. Most commercial medical gross imaging technology provides ergonomically well-designed hardware, remotely operated cameras, intuitive software interfaces, and automation of workflow. However, these solutions are usually cost-prohibitive and require a large sum of capital budget. Materials and Methods: We applied lean techniques such as value stream mapping (VSM) to design a streamlined and error-free workflow for gross imaging process. We implemented a cost-effective technology, UniTwain, combined with high-resolution webcam to achieve the ideal results. Results: We reduced the mean process time from 600 min to 4.0 min (99.3% decrease in duration); the median process time was reduced from 580 min to 3.0 min. The process efficiency increased from 20% to 100%. The implemented solution has a comparable durability, scalability, and archiving feasibility to commercial medical imaging systems and costs four times less. The only limitations are manual operation of the webcam and lower resolution. The webcam sensors have 8.2 megapixel (MP) resolution, approximately 12 MP less than medical imaging devices. However, we believe that this difference is not visually significant and the effect on gross diagnosis with the naked eye is minimal. Conclusions: To our knowledge, this is the first study that utilized UniTwain as a viable, low-cost solution to streamline the gross imaging workflow. The UniTwain combined with high-resolution webcam could be a suitable alternative for our institution that does not plan to heavily invest in medical imaging.