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https://ir.cnu.edu.tw/handle/310902800/34536
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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 |
| 顯示於類別: | [保健營養系(所) ] 期刊論文
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