Zhang Yi, Yang Hao-Ran, Ji Xing-Yu, Xiong Tian-Yuan, Chen Mao
Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Cardiac Structure and Function Research Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
J Geriatr Cardiol. 2024 Dec 28;21(12):1109-1118. doi: 10.26599/1671-5411.2024.12.005.
Epicardial adipose tissue (EAT) radiomics derived from cardiac computed tomography (CT) images may provide insights into EAT characteristics, which can further predict regression of left ventricular mass index (LVMI) after transcatheter aortic valve replacement (TAVR). This study aimed to develop and validate a radiomics nomogram based on pre-procedural EAT CT to predict inadequate LVMI regression following TAVR.
Inadequate LVMI regression was defined as ΔLVMI% < 15% at one-year post TAVR. Radiomics features from pre-procedural CT images were selected mainly by least absolute shrinkage and selection operator algorithm. The patients were randomly divided into the training and validation cohorts to establish and evaluate three feature classifier models based on the selected features, using which the Radiomics scores (Radscores) were then calculated. A radiomics nomogram was constructed using independent risk factors and further assessed using area under the curve, calibration curve, and decision curve analysis.
A total of 144 consecutive TAVR patients (42 patients with inadequate and 102 patients with adequate LVMI regression) were randomly assigned to the training and validation cohorts (116 patients and 28 patients, respectively). A total of 1130 radiomics features from each patient yielded 6 features for the Radscore construction after selection, with logistic regression and support vector machine models favored. Subsequently, a nomogram based solely on the Radscore was constructed, with an area under the curve of 0.743 in the validation cohort, along with favorable decision curve analysis and calibration curves.
The developed radiomics nomogram, serving as a non-invasive tool, achieved satisfactory preoperative prediction of inadequate LVMI regression in TAVR patients, thereby facilitating clinical management.
源自心脏计算机断层扫描（CT）图像的心外膜脂肪组织（EAT）放射组学可提供有关EAT特征的见解，这可以进一步预测经导管主动脉瓣置换术（TAVR）后左心室质量指数（LVMI）的回归情况。本研究旨在开发并验证一种基于术前EAT CT的放射组学列线图，以预测TAVR后LVMI回归不足的情况。
LVMI回归不足定义为TAVR术后一年时ΔLVMI% < 15%。术前CT图像的放射组学特征主要通过最小绝对收缩和选择算子算法进行选择。将患者随机分为训练组和验证组，以基于所选特征建立并评估三种特征分类器模型，然后使用这些模型计算放射组学评分（Radscores）。使用独立危险因素构建放射组学列线图，并通过曲线下面积、校准曲线和决策曲线分析进行进一步评估。
总共144例连续的TAVR患者（42例LVMI回归不足患者和102例LVMI回归充分患者）被随机分配到训练组和验证组（分别为116例患者和28例患者）。每位患者的总共1130个放射组学特征在选择后产生了6个用于构建Radscore的特征，其中逻辑回归和支持向量机模型更受青睐。随后，构建了仅基于Radscore的列线图，验证组中的曲线下面积为0.743，同时具有良好的决策曲线分析和校准曲线。
所开发的放射组学列线图作为一种非侵入性工具，在术前对TAVR患者LVMI回归不足的预测方面取得了令人满意的结果，从而有助于临床管理。
