Feng Lijuan, Zhang Shuxin, Wang Chaoran, Li Siqi, Kan Ying, Wang Chao, Zhang Hui, Wang Wei, Yang Jigang
Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China.
SinoUnion Healthcare Inc., Beijing, China.
Acad Radiol. 2023 Nov;30(11):2487-2496. doi: 10.1016/j.acra.2023.01.030. Epub 2023 Feb 23.
To construct and validate a combined model based on axial skeleton radiomics of F-FDG PET/CT for predicting event-free survival in high-risk pediatric neuroblastoma patients.
Eighty-seven high-risk neuroblastoma patients were retrospectively enrolled in this study and randomized in a 7:3 ratio to the training and validation cohorts. The radiomics model was constructed using radiomics features that were extracted from the axial skeleton. A univariate Cox regression analysis was then performed to screen clinical risk factors associated with event-free survival for building clinical model. Radiomics features and clinical risk factors were incorporated to construct the combined model for predicting the event-free survival in high-risk neuroblastoma patients. The performance of the models was evaluated by the C-index.
Eighteen radiomics features were selected to build the radiomics model. The radiomics model achieved better event-free survival prediction than the clinical model in the training cohort (C-index: 0.846 vs. 0.612) and validation cohort (C-index: 0.754 vs. 0.579). The combined model achieved the best prognostic prediction performance with a C-index of 0.863 and 0.799 in the training and validation cohorts, respectively.
The combined model integrating radiomics features and clinical risk factors showed more accurate predictive performance for event-free survival in high-risk pediatric neuroblastoma patients, which helps to design individualized treatment strategies and regular follow-ups.
构建并验证基于¹⁸F-FDG PET/CT轴向骨骼影像组学的联合模型,用于预测高危儿童神经母细胞瘤患者的无事件生存期。
本研究回顾性纳入87例高危神经母细胞瘤患者,并按7:3的比例随机分为训练组和验证组。利用从轴向骨骼提取的影像组学特征构建影像组学模型。然后进行单变量Cox回归分析,筛选与无事件生存期相关的临床危险因素,以构建临床模型。将影像组学特征和临床危险因素纳入,构建预测高危神经母细胞瘤患者无事件生存期的联合模型。通过C指数评估模型的性能。
选择18个影像组学特征构建影像组学模型。在训练组(C指数:0.846对0.612)和验证组(C指数:0.754对0.579)中,影像组学模型在无事件生存期预测方面比临床模型表现更好。联合模型在训练组和验证组中分别取得了最佳的预后预测性能,C指数分别为0.863和0.799。
整合影像组学特征和临床危险因素的联合模型对高危儿童神经母细胞瘤患者的无事件生存期显示出更准确的预测性能,有助于设计个体化治疗策略和定期随访。