The art of diagnosing rare skin tumors: can DL-CNNs enhance dermatologists’ diagnostic accuracy?
Importance - Deep learning convolutional neural networks (DL-CNN) achieved diagnostic accuracies comparable to dermatologists in controlled test environments. However, their performance in diagnosing rare skin tumors (RST) remains unclear. This study aimed to evaluate a binary DL-CNN’s diagnostic pe...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 October 2025
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| In: |
European journal of cancer
Year: 2025, Jahrgang: 228, Pages: 1-8 |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2025.115751 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ejca.2025.115751 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0959804925005362 
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| Verfasserangaben: | A.S. Vollmer, J.K. Winkler, K. Kommoss, A. Blum, P. Tschandl, T. Kränke, E. Hofmann-Wellenhof, R. Hofmann-Wellenhof, W. Stolz, A. Enk, H.A. Haenssle |
| Zusammenfassung: | Importance - Deep learning convolutional neural networks (DL-CNN) achieved diagnostic accuracies comparable to dermatologists in controlled test environments. However, their performance in diagnosing rare skin tumors (RST) remains unclear. This study aimed to evaluate a binary DL-CNN’s diagnostic performance in RST and assess the level of support for an international group of dermatologists. - Methods - In a cross-sectional reader study, a market-approved binary DL-CNN (Moleanalyzer-Pro) assessed 200 dermoscopic images in a conveniance sample of histologically confirmed RST. An international panel of dermatologists rated malignancy and management across three levels: (I) dermoscopy only, (II) dermoscopy, close-up images, and metadata, (III) level-II plus DL-CNN malignancy predictions. Sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC-AUC) for the DL-CNN versus dermatologists (level-II). Secondary outcomes included performance changes across study levels. - Results - The DL-CNN achieved a sensitivity (95% CI) of 66.7% (56.4%-75.6%), specificity of 56.4% (47.0%-65.3%), and ROC-AUC of 0.634 (0.557-0.711). Dermatologists reached a significantly higher mean sensitivity (80.3%, 77.3%-83.4%), specificity (65.1%, 61.3%-69.0%), and ROC-AUC (0.839, 0.783-0.894; all p<0.001). With DL-CNN predictions, dermatologists’ sensitivity slightly increased (81.3%, p=0.032), specificity decreased (64.0%, p=0.036), and ROC-AUC remained unchanged. The DL-CNN could not improve dermatologists’ accuracy in misclassified cases. - Conclusion - The tested DL-CNN showed a limited diagnostic performance in diagnosing RST. While minor effects on expert decision-making were observed, overall diagnostic accuracy remained highest with full clinical context. Better training data are needed for improved DL-CNN performance in RST. |
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| Beschreibung: | Available online 29 August 2025, Version of Record 4 September 2025 Gesehen am 23.01.2026 |
| Beschreibung: | Online Resource |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2025.115751 |