Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing 400014, China.
Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing 400014, China.
Acad Radiol. 2024 Apr;31(4):1655-1665. doi: 10.1016/j.acra.2023.08.022. Epub 2023 Sep 14.
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.
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.
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.
The utilization of CT-based radiomics signature to identify an UHR subgroup of neuroblastoma patients within the high-risk cohort can help aid in predicting early disease progression.
使用基于计算机断层扫描（CT）的放射组学分析，在高危队列中确定在诊断后 18 个月内发生疾病相关死亡的超高风险（UHR）神经母细胞瘤患者。
对 105 例高危神经母细胞瘤患者进行回顾性分析，分为训练集（n=74）和测试集（n=31）。从动脉期 CT 图像中提取并选择放射组学特征，并使用支持向量机算法建立最佳放射组学特征。计算评估指标，包括曲线下面积（AUC）和 95%置信区间（CI）。此外，分析特征的拟合度和临床获益及其与总生存（OS）的相关性。
最佳放射组学特征由 11 个特征组成。在训练集中，AUC 和准确性分别为 0.911（95%CI：0.840-0.982）和 0.892。在测试集中，AUC 和准确性分别为 0.828（95%CI：0.669-0.987）和 0.839。预测概率与实际概率之间无显著差异，且特征具有净获益。在训练集中，该特征预测 OS 的一致性指数为 0.743（95%CI：0.672-0.814），在测试集中为 0.688（95%CI：0.566-0.810）。此外，该特征在训练集中 1 年、2 年和 3 年 OS 的 AUC 值分别为 0.832、0.863 和 0.721，在测试集中相应时间段的 AUC 值分别为 0.870、0.836 和 0.638。
利用基于 CT 的放射组学特征在高危队列中识别神经母细胞瘤患者的 UHR 亚组有助于预测早期疾病进展。
