biostudies-literatureAldraimli MNational Institute for Health Research (NIHR)Chief Scientist Office100890https://www.ebi.ac.uk/biostudies/studies/S-EPMC9133391biostudies-literatureUnknown7(3)<h4>Purpose</h4>Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.<h4>Methods and materials</h4>Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performaAdvances in radiation oncologyDevelopment and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.PMC9133391ACF-2018-02-011TCS/17/26Taboada-Valadares BGiraldo ADunning AVakaet VJLVega ALambrecht MRosenstein BSde Ruysscher Dde Santis MCSeibold PGutierrez-Enriquez Svan Hulle HIngram SReyes VAldraimli MChaussalet TJGrishchuck DShelley LEARancati TChang-Claude JStobart HRattay TDwek MVVeldeman LTalbot CJAzria DSperk ESymonds RPVeldwijk MRSamuel RAguado-Barrera MELyon RSoria DGreen SLozza LWeltens CWest CMMistry AWebb AOsman SOliveira JHerskind CREQUITE consortiumfalseDevelopment and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.<h4>Purpose</h4>Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.<h4>Methods and materials</h4>Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performa2022-01-01T00:00:00Z2022 May-Jun2025-04-04T10:42:32.846Z2025-04-04T10:42:32.846ZS-EPMC91333913564739610.1016/j.adro.2021.100890