Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases.

BACKGROUND: The establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DESIGN: Hematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. RESULTS: The accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of "fixed" workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. CONCLUSION: Most features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists' preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists' display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard "unlocked" vendor-agnostic displays for clinical digital pathology workflow rather than purchasing "locked" and more expensive displays that are part of a digital pathology system.

Cutaneous Atypical Fibroxanthoma With Osteoclast-Like Giant Cell: A Rare but Diagnostic Pitfall.

BACKGROUND: Atypical fibroxanthoma (AFX) is a dermal-based, low-grade neoplasm with no specific lineage of differentiation. The occurrence of AFX with osteoclast-like giant cells is exceptionally rare. Less than 20 cases have been reported in the literature. CASE PRESENTATION: A 77-year-old man with a medical history of multiple basal and squamous cell carcinomas of the skin, presented with a progressively growing erythematous nodule on the sun-damaged right central parietal scalp. A shave biopsy showed a dermal spindle cell proliferation accompanied by numerous osteoclast-like multinucleated giant cells and predominant atypical mitotic figures. The immunohistochemical staining showed a diffuse positive staining for CD68 and SMA, patchy staining for CD10, and negative staining for SOX-10, pan-cytokeratin, CK5/6, S100, CD34, and desmin. The tumor was completely excised with negative margins. A subsequent follow-up over a period of 13 months showed no recurrence. CONCLUSION: Distinguishing AFX with osteoclast-like giant cells from both malignant and benign skin lesions with osteoclast-like giant cells is crucial. Although AFX tumors display worrisome malignant histologic features, most cases have a favorable prognosis with a local recurrence rate below 5% and exceedingly rare metastasis.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Anastomosing hemangioma: A case report of a benign tumor often misdiagnosed as a malignant epithelioid angiosarcoma.

Anastomosing hemangioma (AH), a rare benign genitourinary tract hemangioma is subject to frequent misdiagnosis due to its rarity and clinical, histological, and immunohistochemical similarities it shares with several diagnoses, including well-differentiated angiosarcoma (AS). This is particularly true of angiosarcoma, nearly identical to AH when presented in tissue samples of limited size. Lack of specific clinical and radiologic manifestations on initial preoperative assessment, coupled with limited diagnostic experience or awareness, can lead to misinterpretation of this entity, potentially leading to unnecessary clinical management. We present an initial misdiagnosis of AS which, upon review of the entire lesion, was identified as AH.