Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma
Rattachement africain : us, se, es, au, ro, rs, gb, pt, hu, il, be, it, tr, de, at, si, ch, pl. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Importance: Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin-stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. Objective: To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. Design, Setting, and Participants: This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. Main Outcomes and Measures: Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. Results: There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin-stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W, 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). Conclusions and Relevance: In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma
- Date Crossref
- 03/07/2025
- Éditeur
- American Medical Association (AMA)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Yale University Department of Pathology pays non établi dans la noticeUniversité ou école supérieure
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Yale Cancer Center pays non établi dans la noticeStructure de recherche
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The Ohio State University Wexner Medical Center Department of Pathology pays non établi dans la noticeÉtablissement de santé
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Karolinska University Hospital Department of Clinical Pathology and Cancer Diagnostics pays non établi dans la noticeÉtablissement de santé
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Karolinska Institutet Department of Oncology and Pathology pays non établi dans la noticeUniversité ou école supérieure
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Hospital Clínic de Barcelona Medical Oncology Department pays non établi dans la noticeÉtablissement de santé
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The University of Sydney Melanoma Institute Australia pays non établi dans la noticeUniversité ou école supérieure
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Melanoma Institute Australia pays non établi dans la noticeOrganisation à but non lucratif
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The University of Queensland Frazer Institute pays non établi dans la noticeUniversité ou école supérieure
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Mayo Clinic in Arizona pays non établi dans la noticeÉtablissement de santé
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Victor Babeș University of Medicine and Pharmacy Timișoara ANAPATMOL Research Center pays non établi dans la noticeUniversité ou école supérieure
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University of Belgrade pays non établi dans la noticeUniversité ou école supérieure
Department of Pathology — Yale University, Yale Cancer Center et Department of Pathology — The Ohio State University Wexner Medical Center, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.