{"title":"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。","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":"6 1","pages":"e230323"},"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":"{\"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\":\"6 1\",\"pages\":\"e230323\"},\"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}","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}
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