AUTHOR=Tao Yan , Zeng Xiang , Mao Hua 

TITLE=Predictive models for chemotherapy-induced oral mucositis: a systematic review

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1608505

DOI=10.3389/fonc.2025.1608505

ISSN=2234-943X

ABSTRACT=ObjectiveTo critically appraise and synthesise existing risk prediction models for chemotherapy-induced oral mucositis (CIOM) in cancer patients, identifying their methodological strengths, limitations, and clinical utility to guide future model refinement.MethodsRelevant literature on CIOM risk prediction models published in PubMed, Cochrane Library, Embase, Web of Science, CNKI, Wanfang Data Knowledge Service Platform, VIP, and CBM was searched, covering the period from the inception of the databases to May 9, 2025. Researchers independently screened the literature and extracted data, utilising the Prediction Model Risk Of Bias Assessment Tool (PROBAST) to evaluate the quality of the models.ResultAfter deduplication, a total of 3,603 articles were identified, encompassing 8 studies that presented 11 models of chemotherapy-induced oral mucositis. All 11 models reported the area under the receiver operating characteristic curve, which ranged from 0.630 to 0.966. The combined AUC value of the 5 models was 0.87 (95% CI: 0.81, 0.93). Five models reported calibration, 8 underwent internal validation, and only 4 underwent external validation. Age, oral hygiene, smoking history, chemotherapy cycles, and chemotherapy regimens were frequently reported predictors in the models. The applicability of the included studies was satisfactory; however, the overall risk of bias was high.ConclusionWhile the risk prediction models for CIOM in patients with malignant tumours demonstrate good applicability, they carry a high risk of bias. Future research should focus on developing more targeted models with lower bias risks based on different tumour types and conduct internal and external validations.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42024532626