18F-FDG PET/CT imaging of pediatric peripheral neuroblastic tumor: a combined model to predict the International Neuroblastoma Pathology Classification

Luo-Dan Qian; Li-Juan Feng; Shu-Xin Zhang; Jun Liu; Jia-Liang Ren; Lei Liu; Hui Zhang; Jigang Yang

doi:10.21037/qims-22-343

¹⁸F-FDG PET/CT imaging of pediatric peripheral neuroblastic tumor: a combined model to predict the International Neuroblastoma Pathology Classification

Quant Imaging Med Surg. 2023 Jan 1;13(1):94-107. doi: 10.21037/qims-22-343. Epub 2022 Oct 10.

Authors

Luo-Dan Qian^#¹, Li-Juan Feng^#¹, Shu-Xin Zhang¹, Jun Liu¹, Jia-Liang Ren², Lei Liu³, Hui Zhang⁴, Jigang Yang¹

Affiliations

¹ Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
² GE Healthcare, Beijing, China.
³ Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, China.
⁴ Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

^# Contributed equally.

Abstract

Background: The aim of this study was to evaluate the effect of a model combining a 18F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-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).

Methods: 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.

Results: 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 vs. 0.803; accuracy: 0.903 vs. 0.710; sensitivity: 0.895 vs. 0.789; specificity: 0.917 vs. 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.

Conclusions: In this preliminary single-center retrospective study, an R-model based on ¹⁸F-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.

Keywords: 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT); International Neuroblastoma Pathology Classification (INPC); Peripheral neuroblastic tumor (pNT); radiomics.