Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma

TL;DR

AI improves histopathology analysis in melanoma by predicting immunotherapy response through automated lymphocyte assessment.

Contribution

AI enables automated quantification and spatial profiling of tumor-infiltrating lymphocytes for immunotherapy prediction in melanoma.

Findings

AI-based quantification of TIL density is automated and reproducible across whole-slide images.

Spatial immune profiling using AI is a powerful biomarker for predicting immunotherapy response in melanoma.

Automated TIL analysis improves prognostic evaluation and immunotherapy outcome prediction.

Abstract

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.

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