ReviewInternational journal of molecular sciences2025
Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.
Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- Advancing precision immuno-oncology in melanoma: the synergistic convergence of personalized neoantigen vaccines and multi-omics biomarker profiling.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.