Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer

The XGBoost-based model showed strong predictive ability and may serve as a practical tool for early risk stratification and timely clinical management of CTRCD.

Authors

Kundi Chen
Fang Nie
Yuqiong An (ORCID: https://orcid.org/0000-0002-7186-7168)
Zhen Wang

Institutions

Lanzhou University Second Hospital (CN)
Lanzhou University (CN)

Publication Details

Journal: Frontiers in Cardiovascular Medicine
Published: 2026-01-06
DOI: https://doi.org/10.3389/fcvm.2025.1707889
Primary Topic: Chemotherapy-induced cardiotoxicity and mitigation
Type: article
Field-Weighted Citation Impact: 0.00

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article

Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer

Kundi Chen, Fang Nie, Yuqiong An, Zhen Wang

Jan 6, 2026

Frontiers in Cardiovascular Medicine

Chemotherapy-induced cardiotoxicity and mitigation

article

Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer

Kundi Chen, Fang Nie, Yuqiong An, Zhen Wang

January 6, 2026article en

Abstract

The XGBoost-based model showed strong predictive ability and may serve as a practical tool for early risk stratification and timely clinical management of CTRCD.

Frontiers in Cardiovascular MedicineVol. 12

Lanzhou University Second Hospital (CN), Lanzhou University (CN)

Openalex Percentile: Top 95%

Chemotherapy-induced cardiotoxicity and mitigation

DOI Open Access PDF OpenAlex

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Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer — Kundi Chen, Fang Nie, et al. · Frontiers in Cardiovascular Medicine (2026) | TGRS Research Map | TGRS