Qian Luo-Dan, Feng Li-Juan, Zhang Shu-Xin, Liu Jun, Ren Jia-Liang, Liu Lei, Zhang Hui, Yang Jigang
Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
GE Healthcare, Beijing, China.
Quant Imaging Med Surg. 2023 Jan 1;13(1):94-107. doi: 10.21037/qims-22-343. Epub 2022 Oct 10.
The aim of this study was to evaluate the effect of a model combining a 18F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT)-based radiomics signature with clinical factors in the preoperative prediction of the International Neuroblastoma Pathology Classification (INPC) type of pediatric peripheral neuroblastic tumor (pNT).
A total of 106 consecutive pediatric pNT patients confirmed by pathology were retrospectively analyzed. Significant features determined by multivariate logistic regression were retained to establish a clinical model (C-model), which included clinical parameters and PET/CT radiographic features. A radiomics model (R-model) was constructed on the basis of PET and CT images. A semiautomatic method was used for segmenting regions of interest. A total of 1,016 radiomics features were extracted. Univariate analysis and the least absolute shrinkage selection operator were then used to select significant features. The C-model was combined with the R-model to establish a combination model (RC-model). The predictive performance was validated by receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) in both the training cohort and validation cohort.
The radiomics signature was constructed using 5 selected radiomics features. The RC-model, which was based on the 5 radiomics features and 3 clinical factors, showed better predictive performance compared with the C-model alone [area under the curve in the validation cohort: 0.908 0.803; accuracy: 0.903 0.710; sensitivity: 0.895 0.789; specificity: 0.917 0.583; net reclassification improvement (NRI) 0.439, 95% confidence interval (CI): 0.1047-0.773; P=0.01]. The calibration curve showed that the RC-model had goodness of fit, and DCA confirmed its clinical utility.
In this preliminary single-center retrospective study, an R-model based on F-FDG PET/CT was shown to be promising in predicting INPC type in pediatric pNT, allowing for the noninvasive prediction of INPC and assisting in therapeutic strategies.
本研究的目的是评估一种将基于18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（F-FDG PET/CT）的放射组学特征与临床因素相结合的模型在术前预测儿童外周神经母细胞瘤（pNT）的国际神经母细胞瘤病理分类（INPC）类型中的作用。
对106例经病理确诊的连续儿童pNT患者进行回顾性分析。保留多因素逻辑回归确定的显著特征以建立临床模型（C模型），该模型包括临床参数和PET/CT影像学特征。基于PET和CT图像构建放射组学模型（R模型）。采用半自动方法分割感兴趣区域。共提取1016个放射组学特征。然后采用单因素分析和最小绝对收缩选择算子选择显著特征。将C模型与R模型相结合建立联合模型（RC模型）。通过受试者操作特征（ROC）曲线分析、校准曲线和决策曲线分析（DCA）在训练队列和验证队列中验证预测性能。
使用5个选定的放射组学特征构建放射组学特征。基于5个放射组学特征和3个临床因素的RC模型与单独的C模型相比显示出更好的预测性能[验证队列中的曲线下面积：0.908对0.803；准确性：0.903对0.710；敏感性：0.895对0.789；特异性：0.917对0.583；净重新分类改善（NRI）0.439，95%置信区间（CI）：0.1047 - 0.773；P = 0.01]。校准曲线显示RC模型具有良好的拟合度，DCA证实了其临床实用性。
在这项初步的单中心回顾性研究中，基于F-FDG PET/CT的R模型在预测儿童pNT的INPC类型方面显示出前景，能够对INPC进行无创预测并辅助治疗策略。
