Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

作者全名：Yang, Yanlin; Wang, Haoru; Si, Jiajun; Zhang, Li; Ding, Hao; Wang, Fang; He, Ling; Chen, Xin

作者地址：[Yang, Yanlin; Wang, Haoru; Si, Jiajun; Zhang, Li; Ding, Hao; He, Ling; Chen, Xin] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Minist Educ,Key Lab Child Dev & Disorders, Chongqing, Peoples R China; [Wang, Fang] Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China

通信作者：He, L; Chen, X (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Minist Educ,Key Lab Child Dev & Disorders, Chongqing, Peoples R China.

来源：JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001233358600001

JCR分区：Q3

影响因子：2.7

年份：2024

卷号：150

期号：5

开始页：　

结束页：　

文献类型：Article

关键词：Children; Computed tomography; Hepatoblastoma; Radiomics; Neoadjuvant chemotherapy

摘要：Objective To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma.Methods Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical-radiomics model incorporated both clinical and radiomics features.Results The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563-0.845), 0.830 (95% CI: 0.704-0.959), and 0.874 (95% CI: 0.768-0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616-0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively.Conclusion CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

基金机构：National Natural Science Foundation of Chongqing

基金资助正文：No Statement Available