文献类型：期刊文献

英文题名：Predicting Microwave Ablation Early Efficacy in Pulmonary Malignancies via Δ Radiomics Models

作者：Yang, Jing[1];Yang, Chen[2];Feng, Jianju[3];Zhu, Fandong[2];Zhao, Zhenhua[2]

机构：[1]Shaoxing Univ, Sch Med, Shaoxing, Peoples R China;[2]Zhejiang Univ, Shaoxing Peoples Hosp, Dept Radiol, Shaoxing Hosp, 568 Zhongxing N Rd, Shaoxing, Zhejiang, Peoples R China;[3]Zhuji Peoples Hosp, Dept Radiol, Zhuji, Zhejiang, Peoples R China

年份：2024

卷号：48

期号：5

起止页码：794

外文期刊名：JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY

收录：SCI-EXPANDED(收录号：WOS:001313252000010)、、EI(收录号：20243917104828)、Scopus(收录号：2-s2.0-85204511303)、WOS

语种：英文

外文关键词：pulmonary malignancies; microwave ablation; radiomics; delta; early efficacy

外文摘要：Objective: This study aimed to explore the value of preoperative and postoperative computed tomography (CT)-based radiomic signatures and Delta radiomic signatures for evaluating the early efficacy of microwave ablation (MWA) for pulmonary malignancies. Methods: In total, 115 patients with pulmonary malignancies who underwent MWA treatment were categorized into response and nonresponse groups according to relevant guidelines and consensus. Quantitative image features of the largest pulmonary malignancies were extracted from CT noncontrast scan images preoperatively (time point 0, TP0) and immediately postoperatively (time point 1, TP1). Critical features were selected from TP0 and TP1 and as Delta radiomics signatures for building radiomics models. In addition, a combined radiomics model (C-RO) was developed by integrating radiomics parameters with clinical risk factors. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results: The radiomics model using Delta features outperformed the radiomics model using TP0 and TP1 features, with training and validation AUCs of 0.892, 0.808, and 0.787, and 0.705, 0.825, and 0.778, respectively. By combining the TP0, TP1, and Delta features, the logistic regression model exhibited the best performance, with training and validation AUCs of 0.945 and 0.744, respectively. The DCA confirmed the clinical utility of the Delta radiomics model. Conclusions: A combined prediction model, including TP0, TP1, and Delta radiometric features, can be used to evaluate the early efficacy of MWA in pulmonary malignancies.