Clinical prediction models using machine learning in oncology: challenges and recommendations.

ABSTRACT: Clinical prediction models are widely developed in the field of oncology, providing individualised risk estimates to aid diagnosis and prognosis. Machine learning methods are increasingly being used to develop prediction models, yet many suffer from methodological flaws limiting clinical implementation. This review outlines key considerations for developing robust, equitable prediction models in cancer care. Critical steps include systematic review of existing models, protocol development, registration, end-user engagement, sample size calculations and ensuring data representativeness across target populations. Technical challenges encompass handling missing data, addressing fairness across demographic groups and managing complex data structures, including censored observations, competing

SUBMITTER: Collins GS 

PROVIDER: S-EPMC12506039 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature