Machine learning enhanced baseline risk prediction models for cancer therapy related cardiac dysfunction: a systematic review and critical appraisal of methodological quality
M Andres, V Maharajan, A Pons Rivarola, F Thuny, S Ramalingam, M Howe, M A Mamas, A Banarjee, C Manisty, A Gosh, A Bharadwaj, A R Lyon, M MohamedAbstract
Background
Cancer therapy-related cardiac dysfunction (CTRCD) is a prevalent complication with adverse clinical implications. Major guidelines have emphasised the need for reliable baseline risk prediction to guide prevention strategies. While machine learning (ML) has shown promise in cardiovascular medicine, its readiness for application in cardio-oncology remains unexplored.
Aim
To systematically review and critically appraise the performance, methodological quality, and clinical applicability of ML-based models for predicting baseline CTRCD risk.
Methods
A search on Embase and Medline was conducted up to April 2025. Eligible studies used at least one ML algorithm to estimate CTRCD risk prior to cancer treatment. Data were extracted using an adapted version of the CHARMS checklist. Risk of bias (ROB) and reporting quality were assessed using PROBAST and TRIPOD+AI respectively.
Results
Seven studies were included. Five developed novel models using multiple predictors, and two repurposed or validated existing AI-enhanced electrocardiography tools. While their levels of discrimination were at least moderate (AUC 0.65–0.88), calibration was not reported in any of the studies and only one study included a small external validation cohort. ECG-only models showed modest performance, whereas models integrating clinical and imaging predictors performed better but were methodologically constrained. Four studies scored high ROB, one low, and two unclear. Common limitations included small datasets with low number of events, limited transparency in reporting, and lack of reproducibility measures.
Conclusions
Current ML-based models for predicting CTRCD are underdeveloped and not ready for clinical deployment. The present findings underscore the need for more rigorous and standardised study designs, with greater emphasis on methodological transparency with clearly-defined adjudicated outcomes and external validation to improve reproducibility.Central Illustration Studies and models characteristics