文献类型：期刊文献

英文题名：Prediction of Local Tumor Progression After Thermal Ablation of Colorectal Cancer Liver Metastases Based on Magnetic Resonance Imaging Δ-Radiomics

作者：Zhu, Xiucong[1];Zhu, Jinke[1];Sun, Chenwen[2];Zhu, Fandong[3];Wu, Bing[1];Mao, Jiaying[1];Zhao, Zhenhua[3]

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

年份：2025

卷号：49

期号：3

起止页码：377

外文期刊名：JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY

收录：SCI-EXPANDED(收录号：WOS:001487911300003)、、EI(收录号：20245017503709)、Scopus(收录号：2-s2.0-85211171097)、WOS

语种：英文

外文关键词：Delta-radiomics; colorectal cancer; liver metastasis; ablation; local tumor progression

外文摘要：Purpose:This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Delta-radiomics and clinical feature-based modeling was employed.Materials and Methods:In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Delta-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Delta model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP.Results:LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size (P = 0.010), and Delta CEA (P = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Delta model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences (P = 0.6087).Conclusions:Combined models incorporating clinical data and Delta-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.