Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients

ABSTRACT: Graphical abstract Highlights • Prediction of RT-induced toxicities is important for an informed decision-making process in lung cancer.• Application of ML approaches to support this task lacks robust validation settings.• This work provides a generalizable methodology for benchmarking machine learning approaches for this purpose.• 300 predictive models have been validated following this methodology.• It shows potential to generate novel data-driven hypotheses in the field.

Background and purpose

Radiation-induced toxicities are common adverse events in lung cancer (LC) patients undergoing radiotherapy (RT). An accurate prediction of these adverse events might facilitate an informed and shared decision-making process between patient and radiation oncologist with a clearer view of

SUBMITTER: Nunez-Benjumea F 

PROVIDER: S-EPMC10213176 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature