Petzold, Anne and Wessely, Anja and Erdmann, Michael and Schliep, Stefan and Schreml, Stephan and Rivera Monroy, Luis Carlos and Vera, Julio and Drexler, Konstantin and Niebel, Dennis and Hayani, Kinan Maurice and Kiesewetter, Franklin and Berking, Carola and Koch, Elias A. T. and Heppt, Markus V. (2025) An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations. VIRCHOWS ARCHIV, 487 (5). pp. 1047-1058. ISSN 0945-6317, 1432-2307
Full text not available from this repository. (Request a copy)Abstract
Cutaneous squamous cell carcinoma (cSCC) and verruca vulgaris (VV) are skin conditions involving the proliferation of epidermal keratinocytes requiring fundamentally different treatments. Histological evaluation of highly differentiated squamous cell proliferations can be challenging, particularly in small or superficial samples. This study aims to improve diagnostic accuracy using an AI model to distinguish cSCC from VV. We developed a deep-learning model using clustering-constrained attention multiple instance learning (CLAM) to classify hematoxylin and eosin-stained whole-slide images (WSIs) as cSCC or VV. The dataset comprised 289 WSIs (n = 148 cSCC, n = 141 VV). Quality control was ensured through expert review: the training cohort was evaluated by four dermatopathologists, and the evaluation cohort by six additional experts. On the training set, the model achieved an AUROC of 0.99, with an accuracy of 94.9% for cSCC and 91.2% for VV. On the evaluation set, it reached an AUROC of 0.96, and accuracies of 82.4% (cSCC) and 97.4% (VV), similar to the average performance of individual dermatopathologists. We successfully trained and implemented an interpretable deep-learning-based weakly supervised model on WSIs distinguishing cSCC from VV, which could enhance AI-supported diagnostics in the future.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; Carcinoma, Squamous Cell; Warts; Pathology; Artificial Intelligence |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Dermatologie und Venerologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 23 Apr 2026 11:53 |
| Last Modified: | 23 Apr 2026 11:53 |
| URI: | https://pred.uni-regensburg.de/id/eprint/67551 |
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