An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline

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標題:	An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline
作者:	Chang, Wei-Ting Liu, Chung-Feng Feng, Yin-Hsun Liao, Chia-Te Wang, Jhi-Joung Chen, Zhih-Cherng Lee, Hsiang-Chun Shih, Jhih-Yuan
貢獻者:	Chi Mei Hospital Southern Taiwan University of Science & Technology National Cheng Kung University Chi Mei Hospital Chi Mei Hospital KU Leuven National Cheng Kung University Kaohsiung Medical University Kaohsiung Medical University Hospital Kaohsiung Medical University Department of Health and Nutrition, Chia Nan University of Pharmacy & Science
關鍵字:	society update
日期:	2022
上傳時間:	2023-12-11 13:56:36 (UTC+8)
出版者:	SPRINGER HEIDELBERG
摘要:	Although anti-cancer therapy-induced cardiotoxicity is known, until now it lacks a reliable risk predictive model of the subsequent cardiotoxicity in breast cancer patients receiving anthracycline therapy. An artificial intelligence (AI) with a machine learning approach has yet to be applied in cardio-oncology. Herein, we aimed to establish a predictive model for differentiating patients at a high risk of developing cardiotoxicity, including cancer therapy-related cardiac dysfunction (CTRCD) and symptomatic heart failure with reduced ejection fraction. This prospective single-center study enrolled patients with newly diagnosed breast cancer who were preparing for anthracycline therapy from 2014 to 2018. We randomized the patients into a 70%/30% split group for ML model training and testing. We used 15 variables, including clinical, chemotherapy, and echocardiographic parameters, to construct a random forest model to predict CTRCD and heart failure with a reduced ejection fraction (HFrEF) during the 3-year follow-up period (median, 30 months). Comparisons of the predictive accuracies among the random forest, logistic regression, support-vector clustering (SVC), LightGBM, K-nearest neighbor (KNN), and multilayer perceptron (MLP) models were also performed. Notably, predicting CTRCD using the MLP model showed the best accuracy compared with the logistic regression, random forest, SVC, LightGBM, and KNN models. The areas under the curves (AUC) of MLP achieved 0.66 with the sensitivity and specificity as 0.86 and 0.53, respectively. Notably, among the features, the use of trastuzumab, hypertension, and anthracycline dose were the major determinants for the development of CTRCD in the logistic regression. Similarly, MLP, logistic regression, and SVM also showed higher AUCs for predicting the development of HFrEF. We also validated the AI prediction model with an additional set of patients developing HFrEF, and MLP presented an AUC of 0.81. Collectively, an AI prediction model is promising for facilitating physicians to predict CTRCD and HFrEF in breast cancer patients receiving anthracycline therapy. Further studies are warranted to evaluate its impact in clinical practice.
關聯:	Archives of Toxicology, v.96, pp.2731–2737
显示于类别:	[保健營養系(所) ] 期刊論文

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