Cancers 2025, 17(22), 3696; https://doi.org/10.3390/cancers17223696 (registering DOI) - 18 Nov 2025
Abstract
Background: Cutaneous melanoma (CM) poses significant diagnostic challenges due to its biological heterogeneity and the subjective interpretation of histopathologic criteria. While early and accurate diagnosis remains critical for patient outcomes, conventional pathology is limited by interobserver variability and diagnostic ambiguity, especially in borderline
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Background: Cutaneous melanoma (CM) poses significant diagnostic challenges due to its biological heterogeneity and the subjective interpretation of histopathologic criteria. While early and accurate diagnosis remains critical for patient outcomes, conventional pathology is limited by interobserver variability and diagnostic ambiguity, especially in borderline lesions. Objective: This narrative review explores the integration of digital pathology (DP) and artificial intelligence (AI)—including deep learning (DL), machine learning (ML), and interpretable models—into the histopathologic workflow for CM diagnosis. Methods: We systematically searched PubMed, Scopus, and Web of Science (2013–2025) for studies using whole slide imaging (WSI) and AI to assist melanoma diagnosis. We categorized findings across five domains: WSI-based classification models, feature extraction (e.g., mitoses, ulceration), spatial modeling and TIL analysis, molecular prediction (e.g., BRAF mutation), and interpretable pipelines based on nuclei morphology. Results: We included 87 studies with diverse AI methodologies. Convolutional neural networks (CNNs) achieved diagnostic accuracy comparable to expert dermatopathologists. U-Net and Mask R-CNN models enabled robust detection of critical histologic features, while nuclei-level analyses offered explainable classification strategies. Spatial and morphometric modeling allowed quantification of tumor–immune interactions, and select models inferred molecular alterations directly from H&E slides. However, generalizability remains limited due to small, homogeneous datasets and lack of external validation. Conclusions: AI-enhanced digital pathology holds transformative potential in CM diagnosis, offering accuracy, reproducibility, and interpretability. Yet, clinical integration requires multicentric validation, standardized protocols, and attention to workflow, ethical, and medico-legal challenges. Future developments, including multimodal AI and integration into molecular tumor boards, may redefine diagnostic precision in melanoma.
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(This article belongs to the Special Issue Novel Research on the Diagnosis and Treatment of Melanoma)
