Supervised machine learning models for cancer prognosis and treatment response prediction: A systematic review of algorithm performance, feature importance, and clinical deployment

Recently, Supervised machine learning (SML) has become an exciting paradigm in clinical oncology for building prediction models based on the available clinical, genomic, imaging and treatment data, to predict outcomes and responses to cancer treatment. Although numerous studies in SML have been published, there is no systematic evaluation of the performance of the algorithms, the extent of consistency across SML studies and algorithms, the quality of calibration, or the readiness for clinical implementation. This review aims to bridge this gap by summarising the findings of 36 studies in a variety of cancers. Methods: We searched for the PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science and the ACM Digital Library for publications between January 2018 and January 2025, according to the PRISMA 2020 guidelines and registered in PROSPERO (CRD42025412104). Cancer prognosis and/or prediction of treatment response studies that developed or had models validated by other groups were included. A five domain PROBAST-AI quality assessment was used. Results: 36 studies with 34 different oncology datasets comprising of more than 3.1 million patient records were eligible. Random Forest was the