Reddish-brown, papulonodular skin lesions in the periungual region.

Multicentric reticulohistiocytosis is a rare, non-Langerhans cell histiocytosis that most commonly presents in women in their fourth or fifth decades of life. Cutaneous involvement, characterized by reddish-brown papules in a "string of pearls" or "coral bead" linear formation, and joint involvement are the two most common manifestations at presentation. Histopathology demonstrates dermal proliferation of epithelioid histiocytic-appearing cells with ground glass cytoplasm. We report a 51-year-old woman who presented with ruddy, periungual papules and bilateral joint pain in the hands, consistent with multicentric reticulohistiocytosis. We describe the clinical and histopathologic presentation, therapeutic options, and differential diagnosis of this rare condition.

Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.

BACKGROUND: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. AIMS: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. METHODS: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. RESULTS: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. CONCLUSIONS: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.

Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study.

Importance: Digital pathology represents a transformative technology that impacts dermatologists and dermatopathologists from residency to academic and private practice. Two concerns are accuracy of interpretation from whole-slide images (WSI) and effect on workflow. Studies of considerably large series involving single-organ systems are lacking. Objective: To evaluate whether diagnosis from WSI on a digital microscope is inferior to diagnosis of glass slides from traditional microscopy (TM) in a large cohort of dermatopathology cases with attention on image resolution, specifically eosinophils in inflammatory cases and mitotic figures in melanomas, and to measure the workflow efficiency of WSI compared with TM. Design, Setting, and Participants: Three dermatopathologists established interobserver ground truth consensus (GTC) diagnosis for 499 previously diagnosed cases proportionally representing the spectrum of diagnoses seen in the laboratory. Cases were distributed to 3 different dermatopathologists who diagnosed by WSI and TM with a minimum 30-day washout between methodologies. Intraobserver WSI/TM diagnoses were compared, followed by interobserver comparison with GTC. Concordance, major discrepancies, and minor discrepancies were calculated and analyzed by paired noninferiority testing. We also measured pathologists' read rates to evaluate workflow efficiency between WSI and TM. This retrospective study was caried out in an independent, national, university-affiliated dermatopathology laboratory. Main Outcomes and Measures: Intraobserver concordance of diagnoses between WSI and TM methods and interobserver variance from GTC, following College of American Pathology guidelines. Results: Mean intraobserver concordance between WSI and TM was 94%. Mean interobserver concordance was 94% for WSI and GTC and 94% for TM and GTC. Mean interobserver concordance between WSI, TM, and GTC was 91%. Diagnoses from WSI were noninferior to those from TM. Whole-slide image read rates were commensurate with WSI experience, achieving parity with TM by the most experienced user. Conclusions and Relevance: Diagnosis from WSI was found equivalent to diagnosis from glass slides using TM in this statistically powerful study of 499 dermatopathology cases. This study supports the viability of WSI for primary diagnosis in the clinical setting.

CD4+ CD56+ hematodermic neoplasm and plasmacytoid dendritic cell tumor: case report and review of the literature.

CD4+ CD56+ hematodermic neoplasm (HN) is a rare and aggressive neoplasm that has raised controversy regarding its etiology. CD4+ CD56+ HN is thought to be derived from plasmacytoid dendritic cells (pDCs) and most commonly stains with CD4, CD56, CD123, and T-cell leukemia/lymphoma 1 (TCL1). Skin manifestations usually are the presenting signs and vary in appearance. Lymph node involvement also is common at the time of presentation, and the natural course of the disease is a progression to leukemia. Treatment of CD4+CD56+ HN focuses on multiple chemotherapeutic regimens but none have been proven to successfully impact overall survival.