Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China.
Medical research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China.
Acad Radiol. 2024 May;31(5):1849-1861. doi: 10.1016/j.acra.2023.10.013. Epub 2023 Nov 24.
To evaluate the performance and clinical utility of CT radiomic features of visceral adipose tissue (VAT) in the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS).
This multi-center study was retrospectively designed. Patients with cirrhosis who underwent TIPS were recruited from January 2015 to December 2020. Pre-TIPS contrast-enhanced CT images were collected for VAT segmentation and radiomic feature extraction. Least absolute shrinkage and selection operator regression with ten-fold cross-validation was performed to reduce dimension. Logistic regression with regularization, support vector machine, and random forest were used for model construction.
A total of 130 patients (90 men; mean age, 54 ± 11 years) were finally enrolled. The cohort was split into 85 patients for the training set (58 men; mean age, 53 ± 12 years) with 19 HE, 21 patients for the internal test set (17 men; mean age, 53 ± 11 years) with 5 HE, and 24 patients for the external test set (15 men; mean age, 55 ± 11 years). Ten radiomic features and C-reactive protein constituted radiomic-clinical models with the best performance. The average area under the receiver operating characteristic curve is 0.97 in the training set and 0.84 in the test sets. For a fixed sensitivity of 0.90, the specificity and negative predictive value of the model is 0.63 and 1.00, respectively; while for a fixed specificity of 0.90, the sensitivity and positive predictive value is 0.60 and 0.75, respectively.
Machine learning models based on CT radiomic features extracted from VAT can predict post-TIPS HE with satisfactory performance.
Our machine learning models based on CT radiomic features of visceral adipose tissue in patients with cirrhosis may assist in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt, indicating its potential in patient selection and clinical decision-making.
Radiomics of visceral adipose tissue provide great help in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. The clinical-radiomic models showed satisfactory performance with an average area under the receiver operating characteristic curve of 0.84. The model can hypothetically provide 90% sensitivity and 100% negative predictive value for guiding patients who are considering transjugular intrahepatic portosystemic shunt.
评估 CT 内脏脂肪组织(VAT)放射组学特征在经颈静脉肝内门体分流术(TIPS)后预测肝性脑病(HE)中的性能和临床实用性。
本多中心研究采用回顾性设计。纳入 2015 年 1 月至 2020 年 12 月间因肝硬化行 TIPS 的患者。采集术前增强 CT 图像进行 VAT 分割和放射组学特征提取。采用 10 折交叉验证最小绝对值收缩和选择算子回归进行降维。采用正则化逻辑回归、支持向量机和随机森林进行模型构建。
共纳入 130 例患者(90 例男性;平均年龄 54±11 岁)。该队列分为训练集 85 例(58 例男性;平均年龄 53±12 岁),其中 19 例发生 HE,内部验证集 21 例(17 例男性;平均年龄 53±11 岁),其中 5 例发生 HE,外部验证集 24 例(15 例男性;平均年龄 55±11 岁)。10 个放射组学特征和 C 反应蛋白构成放射组学-临床模型,表现最佳。训练集的平均受试者工作特征曲线下面积为 0.97,验证集为 0.84。对于固定敏感性为 0.90,模型的特异性和阴性预测值分别为 0.63 和 1.00;而对于固定特异性为 0.90,敏感性和阳性预测值分别为 0.60 和 0.75。
基于 TIPS 后 VAT 提取的 CT 放射组学特征的机器学习模型可用于预测 TIPS 后 HE,具有良好的性能。
本研究基于肝硬化患者 CT 内脏脂肪组织放射组学特征构建的机器学习模型,可能有助于预测经颈静脉肝内门体分流术后肝性脑病,提示其在患者选择和临床决策中有一定的应用价值。
内脏脂肪组织的放射组学为预测经颈静脉肝内门体分流术后肝性脑病提供了很大帮助。临床放射组学模型的平均受试者工作特征曲线下面积为 0.84,表现良好。该模型对于指导考虑行经颈静脉肝内门体分流术的患者,假设可以达到 90%的敏感性和 100%的阴性预测值。