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• W4366179600 abstract "The recent approval of immune checkpoint inhibitors for the management of high-risk early-stage melanoma has created an urgent need for accurate prognostic tools to identify patients at greatest risk of metastatic recurrence, and, therefore, most likely to benefit from adjuvant immunotherapy. Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Massachusetts General Hospital (MGH) and Dana-Farber Cancer Institute (DFCI) were identified. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event models (ensemble, linear, and deep learning) were compared in the metastatic versus locoregional/non-recurrence prediction. Models were evaluated internally and externally by concordance index, time-dependent area under the receiver operating characteristic curve (AUC), and Brier score using: (1) 5-fold cross-validation of the entire cohort; (2) the MGH cohort for model development and the DFCI cohort for external validation. This study included 954 stage I/II melanomas (155 metastatic, 163 locoregional, and 636 1:2 matched non-recurrent melanomas). Metastatic recurrence was associated with significantly worse survival compared to locoregional/non-recurrence (HR: 6.21, p<0.001) and compared to locoregional recurrence only (HR: 5.79, p<0.001). In the external validation, the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) was achieved by an ensemble model. This study suggests that time-to-event machine learning models can reliably predict the risk of metastatic recurrence from localized melanoma with minimal cost to the healthcare system." @default.
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• W4366179600 date "2023-05-01" @default.
• W4366179600 modified "2023-09-26" @default.
• W4366179600 title "217 Time-to-event machine learning prediction of metastatic recurrence of localized melanoma" @default.
• W4366179600 doi "https://doi.org/10.1016/j.jid.2023.03.219" @default.
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