Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches.

ABSTRACT:

Purpose

Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314).

Methods

The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model.

Results

The model's perform

SUBMITTER: Ubeira-Gabellini MG 

PROVIDER: S-EPMC10930533 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature