Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality - Daniela Krepper; Matteo Cesari; Niclas J. Hubel; Philipp Zelger; Monika J. Sztankay (SpringerOpen, 2024)
Abstract Purpose To critically examine the current state of machine learning (ML) models including patient-reported outcome measure (PROM) scores in cancer research, by investigating the reporting quality of currently available studies and proposing areas of improvement for future use of ML in the field. Methods PubMed and Web of Science were systematically searched for publications of studies on patients with cancer applying ML models with PROM scores as either predictors or outcomes. The reporting quality of applied ML models was assessed utilizing an adapted version of the MI-CLAIM (Minimum Information about CLinical Artificial Intelligence Modelling) checklist. The key variables of the checklist are study design, data preparation, model development, optimization, performance, and examination. Reproducibility and transparency complement the reporting quality criteria. Results The literature search yielded 1634 hits, of which 52 (3.2%) were eligible. Thirty-six (69.2%) publications included PROM scores as a predictor and 32 (61.5%) as an outcome. Results of the reporting quality appraisal indicate a potential for improvement, especially in the areas of model examination. According to the standards of the MI-CLAIM checklist, the reporting quality of ML models in included studies proved to be low. Only nine (17.3%) publications present a discussion about the clinical applicability of the developed model and reproducibility and only three (5.8%) provide a code to reproduce the model and the results. Conclusion The herein performed critical examination of the status quo of the application of ML models including PROM scores in published oncological studies allowed the identification of areas of improvement for reporting and future use of ML in the field.