{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Aldraimli M"],"funding":["National Institute for Health Research (NIHR)","Chief Scientist Office"],"pagination":["100890"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9133391"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(3)"],"pubmed_abstract":["Purpose
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.Methods and materials
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 performa"],"journal":["Advances in radiation oncology"],"pubmed_title":["Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort."],"pmcid":["PMC9133391"],"funding_grant_id":["ACF-2018-02-011","TCS/17/26"],"pubmed_authors":["Taboada-Valadares B","Giraldo A","Dunning A","Vakaet VJL","Vega A","Lambrecht M","Rosenstein BS","de Ruysscher D","de Santis MC","Seibold P","Gutierrez-Enriquez S","van Hulle H","Ingram S","Reyes V","Aldraimli M","Chaussalet TJ","Grishchuck D","Shelley LEA","Rancati T","Chang-Claude J","Stobart H","Rattay T","Dwek MV","Veldeman L","Talbot CJ","Azria D","Sperk E","Symonds RP","Veldwijk MR","Samuel R","Aguado-Barrera ME","Lyon R","Soria D","Green S","Lozza L","Weltens C","West CM","Mistry A","Webb A","Osman S","Oliveira J","Herskind C","REQUITE consortium"],"additional_accession":[]},"is_claimable":false,"name":"Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.","description":"Purpose
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.Methods and materials
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 performa","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May-Jun","modification":"2025-04-04T10:42:32.846Z","creation":"2025-04-04T10:42:32.846Z"},"accession":"S-EPMC9133391","cross_references":{"pubmed":["35647396"],"doi":["10.1016/j.adro.2021.100890"]}}