Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma

作者全名："Wang, Haoru; Qin, Jinjie; Chen, Xin; Zhang, Ting; Zhang, Li; Ding, Hao; Pan, Zhengxia; He, Ling"

作者地址："[Wang, Haoru; Qin, Jinjie; Chen, Xin; Zhang, Ting; Zhang, Li; Ding, Hao; He, Ling] Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Chongqing Key Lab Pediat, Minist Educ Key Lab Child Dev & Disorders, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China; [Pan, Zhengxia] Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Cardiothorac Surg,Chongqing Key Lab Pediat, Minist Educ Key Lab Child Dev & Disorders, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China"

通信作者："He, L (通讯作者)，Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Chongqing Key Lab Pediat, Minist Educ Key Lab Child Dev & Disorders, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China.; Pan, ZX (通讯作者)，Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Cardiothorac Surg,Chongqing Key Lab Pediat, Minist Educ Key Lab Child Dev & Disorders, 136 Zhongshan Rd 2, Chongqing 400014, Peoples R China."

来源：ABDOMINAL RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000904001900001

JCR分区：Q2

影响因子：2.3

年份：2023

卷号：48

期号：3

开始页：976

结束页：986

文献类型：Article

关键词：Neuroblastoma; Neoadjuvant chemotherapy; Radiomics; Computed tomography

摘要："Purpose To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma. Materials and methods Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal-Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset. Results The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (Z = 2.003, P = 0.045). The decision curve analysis showed that the combined model performs better than the clinical model. Conclusion The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved."

基金机构："Basic Research and Frontier Exploration Project (Yuzhong District, Chongqing, China) [20200155]"

基金资助正文："The project was funded by Basic Research and Frontier Exploration Project (Yuzhong District, Chongqing, China) (Grant No. 20200155)."