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The Predictive Value of Myocardial Native T1 Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population.
Zhang, Jian; Xu, Yuanwei; Li, Weihao; Zhang, Chao; Liu, Wentao; Li, Dong; Chen, Yucheng.
Afiliação
  • Zhang J; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Xu Y; Med-X Center for Informatics, Sichuan University, Chengdu, China.
  • Li W; Division of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Zhang C; Division of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Liu W; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
  • Li D; Med-X Center for Informatics, Sichuan University, Chengdu, China.
  • Chen Y; Fundamental Technology Center of CCB Financial Technology Co., Ltd, Shanghai, China.
J Magn Reson Imaging ; 58(3): 772-779, 2023 09.
Article em En | MEDLINE | ID: mdl-36416613
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cardiomiopatia Dilatada Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cardiomiopatia Dilatada Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China