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.
背景：皮肤黑色素瘤（CM）因其生物学异质性及组织病理学标准的主观解读，在诊断上面临显著挑战。早期准确诊断对患者预后至关重要，但传统病理学受限于观察者间的差异性和诊断模糊性，尤其在交界性病变中更为突出。 目的：本文综述探讨了数字病理学（DP）与人工智能（AI）——包括深度学习（DL）、机器学习（ML）及可解释模型——在皮肤黑色素瘤组织病理学诊断流程中的整合应用。 方法：我们系统检索了PubMed、Scopus和Web of Science（2013–2025年）中利用全切片成像（WSI）与AI辅助黑色素瘤诊断的研究。研究结果被归纳为五个方面：基于WSI的分类模型、特征提取（如核分裂象、溃疡）、空间建模与肿瘤浸润淋巴细胞（TIL）分析、分子预测（如BRAF突变）以及基于细胞核形态的可解释分析流程。 结果：共纳入87项采用不同AI方法的研究。卷积神经网络（CNNs）的诊断准确性已达到与皮肤病理学专家相当的水平。U-Net和Mask R-CNN模型能够稳健检测关键组织学特征，而细胞核水平分析提供了可解释的分类策略。空间与形态计量建模实现了肿瘤-免疫相互作用的量化，部分模型可直接从H&E切片推断分子改变。然而，由于数据集规模小、同质性强且缺乏外部验证，模型的泛化能力仍有限。 结论：AI增强的数字病理学在皮肤黑色素瘤诊断中具有变革性潜力，能够提供更高的准确性、可重复性和可解释性。但临床整合仍需多中心验证、标准化流程，并关注工作流程、伦理及医学法律挑战。未来的发展，包括多模态AI及与分子肿瘤委员会的整合，可能重新定义黑色素瘤的诊断精度。
From Slide to Insight: The Emerging Alliance of Digital Pathology and AI in Melanoma Diagnostics
肿瘤(癌症)患者之家