心肌固有 T1 映射放射组学对扩张型心肌病的预测价值：一项中国人群研究。
The Predictive Value of Myocardial Native T1 Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population.
机构信息
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Med-X Center for Informatics, Sichuan University, Chengdu, China.
出版信息
J Magn Reson Imaging. 2023 Sep;58(3):772-779. doi: 10.1002/jmri.28527. Epub 2022 Nov 23.
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.
背景
研究影响扩张型心肌病（DCM）预后的因素很重要，因为这有助于风险分层和指导临床决策。
目的
评估磁共振成像（MRI）原生 T1 映射放射组学分析对 DCM 的预后价值。
研究类型
前瞻性。
受试者
330 例连续非缺血性 DCM 患者（平均年龄 48.42±14.20 岁，247 例男性）。
磁场强度/序列：3T 下的平衡稳态自由进动和改良 Look-Locker 反转恢复 T1 映射序列。
评估
获得临床特征、常规 MRI 参数（心室容积、功能和质量）、原生心肌 T1 和从原生 T1 映射中提取的放射组学特征。研究终点定义为全因死亡率或心脏移植。模型基于以下 1）临床数据；2）基于 T1 映射的放射组学数据；3）临床和常规 MRI 数据；4）临床、常规 MRI 和原生 T1 数据；和 5）临床、常规 MRI 和放射组学 T1 映射数据。根据随访结果，每个预测模型都使用 AdaBoost、随机森林和逻辑回归分类器进行训练。
统计学检验
通过 5 折交叉验证评估预测性能，使用接收者操作特征曲线（AUC）下面积和 F1 分数。
结果
在中位随访 53.5 个月（四分位间距，41.6-69.5 个月）期间，77 例 DCM 患者发生全因死亡率或心脏移植。基于放射组学与临床和常规 MRI 参数相结合的随机森林模型表现最佳，AUC 和 F1 评分分别为 0.95 和 0.89。
数据结论
一种基于 T1 映射放射组学分析的机器学习框架，用于预测 DCM 的预后。
证据水平
1 技术功效：阶段 2。
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