Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100045, China.
Department of Surgical Oncology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China.
BMC Med Imaging. 2022 May 28;22(1):102. doi: 10.1186/s12880-022-00828-z.
This retrospective study aimed to develop and validate a combined model based [F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients.
Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595-0.874) and 0.750 (95% CI, 0.577-0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685-0.916) and 0.869 (95% CI, 0.715-0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794-0.963) and 0.892 (95% CI, 0.758-0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma.
The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.
本回顾性研究旨在开发和验证一种基于 [F]FDG PET/CT 放射组学和临床参数的联合模型，用于预测高危儿童神经母细胞瘤患者的复发。
回顾性纳入 84 例高危神经母细胞瘤患者，根据 3:2 的比例分为训练集和测试集。使用 3D Slicer 软件对肿瘤的 [F]FDG PET/CT 图像进行分割，并提取放射组学特征。通过最小绝对收缩和选择算子选择有效特征，构建放射组学评分（Rad_score）。基于 Rad_score 构建放射组学模型（R_model），用于预测复发。然后，进行单因素和多因素分析，筛选出独立的临床风险参数，并构建临床模型（C_model）。基于 Rad_score 和独立临床风险参数构建联合模型（RC_model），并表示为放射组学列线图。通过受试者工作特征曲线下面积（AUC）和决策曲线分析（DCA）评估上述三种模型的性能。
选择了 7 个放射组学特征来构建 R_model。C_model 在训练集和测试集中的 AUC 分别为 0.744（95%置信区间 [CI]，0.595-0.874）和 0.750（95% CI，0.577-0.904）。R_model 在训练集和测试集中的 AUC 分别为 0.813（95% CI，0.685-0.916）和 0.869（95% CI，0.715-0.985）。RC_model 在训练集和测试集中的 AUC 最大，分别为 0.889（95% CI，0.794-0.963）和 0.892（95% CI，0.758-0.992）。DCA 表明，RC_model 比 C_model 或 R_model 更能为预测高危儿童神经母细胞瘤的复发提供净效益。
该联合模型在预测高危儿童神经母细胞瘤复发方面表现良好，可在临床实践中辅助疾病随访和管理。
