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

Area of Science:

• Oncology
• Computational Pathology
• Immunology

Background:

• Manual assessment of tumor-infiltrating lymphocytes (TILs) in cutaneous melanoma is subjective and labor-intensive.
• TILs are crucial prognostic markers, but their accurate quantification is challenging.
• Existing methods often analyze limited tissue areas, missing comprehensive immune microenvironment insights.

Purpose of the Study:

• To review advancements in AI-driven histopathology for cutaneous melanoma.
• To highlight automated TIL quantification and spatial immune profiling methods.
• To demonstrate how AI refines prognostic evaluation and predicts immunotherapy outcomes.

Main Methods:

• AI algorithms for automated TIL density quantification.
• Spatial immune profiling using whole-slide imaging analysis.
• Review of studies applying AI to melanoma histopathology and immunotherapy response prediction.

Main Results:

• AI enables reproducible and objective TIL quantification across entire slides.
• Spatially resolved TIL profiling shows strong potential as a prognostic and predictive biomarker.
• AI-driven analysis improves the understanding of the tumor immune microenvironment in melanoma.

Conclusions:

• AI transforms melanoma histopathology by automating complex analyses.
• Automated TIL quantification and spatial profiling enhance prognostic accuracy.
• AI innovations are key to optimizing immunotherapy selection and predicting patient outcomes.