Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

OBJECTIVE

To critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer (HNC) patients, emphasizing the contribution of advanced imaging biomarkers such as radiomic and dosiomic features.

MATERIALS AND

METHODS

This systematic review followed the PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251075245). The PICOS framework guided the review question and inclusion criteria. Studies developing or validating ML models for OM prediction using radiomic and/or dosiomic data were included. Electronic databases (PubMed, EMBASE, Web of Science, Scopus) were searched up to July 2025. The risk of bias was assessed using the PROBAST tool, and models were compared based on predictor types.

RESULTS

Seven studies involving 2,311 patients were included. All models incorporated dosimetric predictors, while some used additional radiomic (n = 3), dosiomic (n = 2), contouromic (n = 1), or oral health indices (n = 1). Reported discrimination (AUC) ranged from 0.70 to 0.96, but external validations showed reduced performance (AUC ~ 0.65). High risk of bias was mainly observed in analysis domains due to limited sample sizes and incomplete calibration.

CONCLUSION

ML models for OM prediction remain exploratory and are not yet ready for routine clinical implementation due to poor generalizability and calibration issues.