Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma

作者：Mengsi Li, Yaheng Fan, Huayu You, Chao Li, Ma Luo, Jing Zhou, Anqi Li, Lina Zhang, Yu Xiao, Weiwei Deng, Jinhui Zhou, Dingyue Zhang, Zhongping Zhang, Haimei Chen, Yuanqiang Xiao, Bingsheng Huang, Jin Wang · 发表于：Radiology · 年份：2023 · DOI：10.1148/radiol.230255 · 被引用次数：83 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging、Hepatocellular Carcinoma Treatment and Prognosis

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination–confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL rad...