ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Cardio-Oncology
This article is part of the Research TopicCardio-Oncology in the Era of Precision Medicine: Risk Stratification, Surveillance, and Cardioprotection Across the Cancer ContinuumView all articles
Machine Learning Models Using Multimodal Data Accurately Predict Chemotherapy-Induced Cardiotoxicity in Breast Cancer
Provisionally accepted
- Lanzhou University Second Hospital, Lanzhou, China
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Background: Despite significant advances in breast cancer therapy, chemotherapy-related cardiac dysfunction (CTRCD) remains a critical clinical challenge. This study aimed to develop and validate machine learning (ML) models that integrate multimodal data to predict the risk of CTRCD in female breast cancer patients. Methods: We retrospectively analyzed data from 423 female breast cancer patients who received chemotherapy between January 2020 and January 2025. Multimodal data included demographic information, clinical variables, echocardiographic parameters, electrocardiographic (ECG) findings, and cardiac biomarkers. The dataset was randomly split into training and validation sets in a 7:3 ratio. Seven feature selection methods and eight ML algorithms were employed to construct and compare predictive models. Results: Among the 423 patients, CTRCD occurred in 111 patients (26.24%). Five variables were identified as robust predictors: age, baseline left ventricular ejection fraction < 60%, anthracycline-trastuzumab combination therapy, chemotherapy cycles, and abnormal ECG findings. Of all the models evaluated, the extreme gradient boosting (XGBoost) algorithm demonstrated the best performance, achieving an area under the curve of 0.782 (95% CI: 0.681-0.883) in 10-fold cross-validation. Conclusion: The XGBoost-based model showed strong predictive ability and may offer a practical tool for early risk stratification and timely clinical management of CTRCD.
Keywords: breast cancer, chemotherapy-related cardiac dysfunction, cardiotoxicity, Machinelearning, Prediction model, Multimodal data
Received: 18 Sep 2025; Accepted: 04 Dec 2025.
Copyright: © 2025 Chen, An, Wang and Nie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fang Nie
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