ArticleAdvances in radiation oncology
Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Prediction models of adverse outcomes following surgery and radiotherapy for breast cancer: a systematic review.ESMO real world data and digital oncology · 2026Review
- Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy.Breast (Edinburgh, Scotland) · 2025Article
- A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.European journal of medical research · 2024Article
- Article
- Longitudinal cosmetic outcome after planned IORT boost with low kV X-rays-monocentric results from the TARGIT BQR registry.Translational cancer research · 2023Article
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry.Frontiers in oncology · 2022Article
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Authors and funding
43 authors at 20 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
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 performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.
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