Zheng Yi, Chen Ziliang, Huang Shan, Zhang Nan, Wang Yueying, Hong Shenda, Chan Jeffrey Shi Kai, Chen Kang-Yin, Xia Yunlong, Zhang Yuhui, Lip Gregory Y H, Qin Juan, Tse Gary, Liu Tong
Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
National Institute of Health Data Science at Peking University, Peking University, 100871 Beijing, China.
Rev Cardiovasc Med. 2023 Oct 19;24(10):296. doi: 10.31083/j.rcm2410296. eCollection 2023 Oct.
A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.
越来越多关于肿瘤治疗后广泛不良心脏事件的证据，促使心脏肿瘤学作为一个日益重要的跨学科专业应运而生。这也要求对接受癌症治疗的患者进行更好的风险分层。机器学习（ML）是人工智能的一个热门分支学科，通过识别变量之间的相互作用模式来处理复杂的大数据问题，在心脏肿瘤学研究中用于风险分层的应用越来越多。这篇综述的目的是概述机器学习方法在心脏肿瘤学中的应用，包括深度学习、人工神经网络、随机森林，并总结机器学习识别出的心脏毒性。当前文献表明，机器学习已应用于癌症患者心脏毒性的预测、诊断和治疗。此外，还讨论了机器学习在心脏结局的性别和种族差异中的作用以及心脏肿瘤学未来的潜在发展方向。未来在医院建立专门的多学科团队并教育医学专业人员熟悉并精通机器学习至关重要。
