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| Home > Publications database > Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning. |
| Journal Article | DKFZ-2026-01428 |
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2026
Nature Publ. Group
London
Abstract: Breast fibrosis (BF) after radiotherapy remains one of the most dreaded late toxicities in breast cancer care, yet multiple additive predictors struggle to capture its underlying biological complexity. Radiation-induced lymphocyte apoptosis (RILA) has recently been associated with the risk of fibrosis more than 10 years post-RT. Here, we show that a combination of five independent factors, RILA, two SNPs in the CTGF and NBS1 genes, and two clinical variables (body-mass index and hypertension) exhibits several important interactions. Partition analysis identified six partly nested subgroups, which could be consolidated into three clinically meaningful risk groups. Machine-learning modelling verified and refined these groups, demonstrating a five-fold variation (17-83%) in BF risk with an AUC = 0.735 in ROC analysis using only these five features. Our study provides proof-of-concept that a biologically realistic subgroup-based approach sharpens predictive performance and may enable clinical identification of a subgroup of breast cancer patients highly susceptible to BF.
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