人工智能在组织病理学分析中预测皮肤黑色素瘤免疫治疗反应

Artificial intelligence (AI) has emerged as a transformative tool in histopathology, offering new opportunities to enhance prognostic accuracy and guide immunotherapy in cutaneous melanoma. The prognostic significance of tumor-infiltrating lymphocytes (TILs) is well established, yet their manual assessment remains subjective, labor-intensive, and often confined to selected tissue regions. Recent AI-based approaches enabled automated and reproducible quantification of TIL density and spatial immune profiling across whole-slide images, providing a more comprehensive view of the tumor immune microenvironment.

In melanoma, these methods have demonstrated the potential to predict response to immune checkpoint blockade, with spatially resolved TIL profiling emerging as a particularly powerful prognostic and predictive biomarker. This review summarizes recent advances in AI-driven histopathologic analysis of cutaneous melanoma, focusing on automated TIL quantification and spatial immune profiling, and highlights how these innovations refine prognostic evaluation and improve the prediction of immunotherapy outcomes.