{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Aung TN"],"funding":["NCI NIH HHS"],"pagination":["e2518906"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12232186"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(7)"],"pubmed_abstract":["<h4>Importance</h4>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.<h4>Objective</h4>To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods.<h4>Design, setting, and participants</h4>This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditio"],"journal":["JAMA network open"],"pubmed_title":["Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma."],"pmcid":["PMC12232186"],"funding_grant_id":["P50 CA121974","P50 CA225450","U54 CA263001","P50 CA196530","P30 CA016359"],"pubmed_authors":["Su D","Tan SX","Osman I","Gama J","Vesely MD","Fortarezza F","Rau T","Rimm DL","Tsiknakis N","Acs B","Bracero Y","Oliveira RC","Wilmott J","Tzoras E","Gonzalez MS","Plummer J","Dema A","Brautigam K","Wong PF","Queipo Gutierrez FJ","Solass W","Jovic V","Pezzuto F","Nourmohammadi S","Bosic M","He M","Cowper S","Sun W","Gullo I","Gavirelatou N","Sinnberg T","Szymonski K","Saenger Y","Aung TN","Kreis B","Pinto J","Pop O","Vigdorovits A","Vignati D","Bates K","Poulios C","Warrell J","Barna R","Liu M","Skok K","Neppl C","Vidal JM","Illabochaca I","Toli MA","Pillar N","Plotar V","Micevic G","Lazar B","Burela S","Boyaci C","Maher N","Serbes ED","Stanowska O","Thimm F","Shafi S","Hacihasanoglu E","Olinca M","Bai Y","Szasz M","Cserni G","Djureinovic D","Steen S","Chenhao Z","Gomes Pinto D","Prieto V","Chan NNN","Bosisio F","Mohacsi R","Hartman J"],"additional_accession":[]},"is_claimable":false,"name":"Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma.","description":"<h4>Importance</h4>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.<h4>Objective</h4>To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods.<h4>Design, setting, and participants</h4>This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditio","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-06-06T17:23:56.809Z","creation":"2026-06-03T03:10:16.585Z"},"accession":"S-EPMC12232186","cross_references":{"pubmed":["40608341"],"doi":["10.1001/jamanetworkopen.2025.18906"]}}