{"title":"Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure.","authors":"Hongbo Zhang, Lei Zhao, Haoru Wang, Yuhan Yi, Keyao Hui, Chen Zhang, Xiaohai Ma","doi":"10.1148/ryct.230323","DOIUrl":null,"url":null,"abstract":"<p><p>Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; <i>P</i> < .001) and multivariable (hazard ratio, 10.25; <i>P</i> < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. <b>Keywords:</b> Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure <i>Supplemental material is available for this article</i>. © RSNA, 2024.</p>","PeriodicalId":21168,"journal":{"name":"Radiology. Cardiothoracic imaging","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912890/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology. Cardiothoracic imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1148/ryct.230323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.
从心血管磁共振视频图像中提取放射组学信息,用于识别心衰高风险肥厚型心肌病患者。
目的 建立一个模型,将心脏磁共振 cine 图像的放射组学特征与临床和标准心脏磁共振成像预测指标相结合,以识别心力衰竭(HF)高风险肥厚型心肌病(HCM)患者。材料与方法 在这项回顾性研究中,2015 年 1 月至 2021 年 6 月期间接受心脏 MRI 检查的 516 例 HCM 患者(中位年龄 51 岁 [IQR:40-62];367 例 [71.1%] 男性)被分为训练集和验证集(比例为 7:3)。从心脏ct图像中提取放射组学特征,并使用最小绝对收缩和选择算子Cox回归法根据可重复特征计算放射组学评分。在单变量考克斯回归分析中与高频事件显著相关的放射组学评分和临床及标准心脏磁共振成像预测因子被纳入多变量分析,以构建综合预测模型。利用随时间变化的接收者操作特征曲线下面积(AUC)验证了模型的性能,并确定了用于患者风险分层的组合模型的最佳临界值。结果 在单变量(危险比为 10.37;P < .001)和多变量(危险比为 10.25;P < .001)分析中,放射组学评分是预测高血压事件的最强指标。在训练集中,合并模型的 1 年和 3 年 AUC 分别为 0.81 和 0.80,在验证集中分别为 0.82 和 0.77,均为最高。与低风险患者相比,被分层为高风险的患者发生心房颤动事件的风险增加了六倍多。结论 结合放射组学特征、临床和标准心脏磁共振成像参数的综合模型能准确识别HCM高危患者。关键词心肌病、结果分析、心血管磁共振成像、肥厚型心肌病、放射组学、心力衰竭 本文有补充材料。© RSNA, 2024.
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