The Predictive Value of Myocardial Native T1 Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population

• Published in: Journal of magnetic resonance imaging Vol. 58; no. 3; pp. 772 - 779
• Main Authors: Zhang, Jian, Xu, Yuanwei, Li, Weihao, Zhang, Chao, Liu, Wentao, Li, Dong, Chen, Yucheng
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2023; Wiley Subscription Services, Inc
• Subjects: Adult; Cardiomyopathy; Cardiomyopathy, Dilated; Decision making; Decision trees; Dilated cardiomyopathy; East Asian People; Field strength; Heart transplantation; Humans; Ischemia; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mapping; Mathematical models; Medical imaging; Middle Aged; Mortality; Myocardium - pathology; native T1 mapping; Parameters; Performance evaluation; Performance prediction; Population studies; Prediction models; Predictive Value of Tests; Prognosis; prognostic prediction; Prospective Studies; Radiomics; Retrospective Studies; Statistical analysis; Statistical tests; Transplantation; Ventricle; prognostic prediction; machine learning; dilated cardiomyopathy; native T1 mapping
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28527

Background Investigation of the factors influencing dilated cardiomyopathy (DCM) prognosis is important as it could facilitate risk stratification and guide clinical decision‐making. Purpose To assess the prognostic value of magnetic resonance imaging (MRI) radiomics analysis of native T1 mapping in DCM. Study Type Prospective. Subjects Three hundred and thirty consecutive patients with non‐ischemic DCM (mean age 48.42 ± 14.20 years, 247 males). Field Strength/Sequence Balanced steady‐state free precession and modified Look‐Locker inversion recovery T1 mapping sequences at 3 T. Assessment Clinical characteristics, conventional MRI parameters (ventricular volumes, function, and mass), native myocardial T1, and radiomics features extracted from native T1 mapping were obtained. The study endpoint was defined as all‐cause mortality or heart transplantation. Models were developed based on 1) clinical data; 2) radiomics data based on T1 mapping; 3) clinical and conventional MRI data; 4) clinical, conventional MRI, and native T1 data; and 5) clinical, conventional MRI, and radiomics T1 mapping data. Each prediction model was trained according to follow‐up results with AdaBoost, random forest, and logistic regression classifiers. Statistical Tests The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score by 5‐fold cross‐validation. Results During a median follow‐up of 53.5 months (interquartile range, 41.6–69.5 months), 77 patients with DCM experienced all‐cause mortality or heart transplantation. The random forest model based on radiomics combined with clinical and conventional MRI parameters achieved the best performance, with AUC and F1 score of 0.95 and 0.89, respectively. Data Conclusion A machine‐learning framework based on radiomics analysis of T1 mapping prognosis prediction in DCM. Level of Evidence 1 Technical Efficacy Stage 2