Enhancing cancer prognostics with group penalty models: a comparative study on radiomics feature selection in lung adenocarcinomas and meningiomas

Group penalty models demonstrated superior feature selection stability while maintaining comparable predictive performance to Lasso. By selecting biologically meaningful feature groups rather than individual features, these models enhance interpretability and align better with clinical reasoning, offering a robust framework for radiomics-based cancer prognostics.