biostudies-literatureWan GU.S. Department of DefenseU.S. Department of Defense (United States Department of Defense)Dermatology FoundationDermatology Foundation (DF)NIGMS NIH HHS79https://www.ebi.ac.uk/biostudies/studies/S-EPMC9622809biostudies-literatureUnknown6(1)Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorNPJ precision oncologyPrediction of early-stage melanoma recurrence using clinical and histopathologic features.PMC9622809R35 GM142879W81XWH2110819Medical Dermatology Career Development AwardJiao MChoi MSSemenov YRBoland GMMarko-Varga GAlexander NAZhang SCollier MRTang KValdes JGNemeth IBChen WJairath RYu KHPhillipps JSWan GAmadife MHua YSorger PKDeSimone MSNguyen NHo DDuey SGusev ALeung BWRajeh ALiu DLiu FfalsePrediction of early-stage melanoma recurrence using clinical and histopathologic features.Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algor2022-01-01T00:00:00Z2022 Oct2026-05-03T10:52:47.047Z2025-04-07T13:01:57.354ZS-EPMC96228093631648210.1038/s41698-022-00321-4