Identification of an Ultra-High-Risk Subgroup of Neuroblastoma Patients within the High-Risk Cohort Using a Computed Tomography-Based Radiomics Approach

作者全名：Wang, Haoru; Chen, Xin; Li, Ting; Xie, Mingye; Qin, Jinjie; Zhang, Li; Ding, Hao; He, Ling

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

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

来源：ACADEMIC RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001227727500001

JCR分区：Q1

影响因子：3.8

年份：2024

卷号：31

期号：4

开始页：1655

结束页：1665

文献类型：Article

关键词：Neuroblastoma; Prognosis; Radiomics; Risk stratification; Computed tomography

摘要：Rationale and Objectives: To identify ultra -high -risk (UHR) neuroblastoma patients who experienced disease -related mortality within 18 months of diagnosis within the high -risk cohort using computed tomography (CT) -based radiomics analysis. Materials and Methods: A retrospective analysis was conducted on 105 high -risk neuroblastoma patients, divided into a training set ( n = 74) and a test set ( n = 31). Radiomics features were extracted and selected from arterial phase CT images, and an optimal radiomics signature was established using the support vector machine algorithm. Evaluation metrics, including area under the curve (AUC) and 95% confidence interval (CI), were calculated. Furthermore, the fit and clinical benefit of the signature, along with its correlation with overall survival (OS), were analyzed. Results: The optimal radiomics signature comprised 11 features. In the training set, AUC and accuracy were 0.911 (95% CI: 0.840-0.982) and 0.892, respectively. In the test set, AUC and accuracy were 0.828 (95% CI: 0.669-0.987) and 0.839, respectively. There was no significant difference between predicted probability and actual probability, and the signature demonstrated net benefit. The concordance index of this signature for predicting OS was 0.743 (95% CI: 0.672-0.814) in the training set and 0.688 (95% CI: 0.566-0.810) in the test set. Moreover, the signature achieved AUC values of 0.832, 0.863, and 0.721 for 1 -year, 2 -year, and 3 -year OS in the training set, and 0.870, 0.836, and 0.638 in the test set for the respective time periods. Conclusion: The utilization of CT -based radiomics signature to identify an UHR subgroup of neuroblastoma patients within the highrisk cohort can help aid in predicting early disease progression.

基金机构：FUNDING; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN202000440]

基金资助正文：<B>FUNDING</B> This study was supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202000440) .