Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of 18F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.
Background: The aim of this study was to evaluate the effect of a model combining a 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 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). 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 RCmodel had goodness of fit, and DCA confirmed its clinical utility. Conclusions: In this preliminary single-center retrospective study, an R-model based on 18 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.
Objectives. To explore the diagnostic value of 18F-FDG PET/CT bone marrow uptake pattern (BMUP) in detecting bone marrow involvement (BMI) in pediatric neuroblastoma (NB) patients. Methods. Ninety-eight NB patients were enrolled in BMI analysis. Four patterns of bone marrow uptake were categorized based on pretreatment cF-FDG PET/CT images. Some crucial inspection indexes and 18F-FDG PET/CT metabolic parameters were analyzed. The BMUP was divided into BMUP1, BMUP2, BMUP3, and BMUP4. Paired-like homeobox 2b (PHOX2B) of bone marrow and blood, bone marrow biopsy (BMB) result, and 18F-FDG PET/CT were compared to detect BMI. All patients were followed up for at least six months. Results. BMUP had excellent consistency among different physicians. Kappa coefficients of two residents and two attending physicians and between the resident and attending physician, were 0.857, 0.891, and 0.845, respectively. The optimal cut-off value of SUVmax-Bone/Liver was 2.08 to diagnose BMI for BMUP3 patients, and the area under curve (AUC) was 0.873. AUC of PHOX2B of bone marrow (PHOX2B of BM), PHOX2B of blood, BMB, and 18F-FDG PET/CT were 0.916, 0.811, 0.806, and 0.904, respectively. There was no significant difference between PHOX2B of BM and PET/CT. Positive predictive value, negative predictive value, sensitivity, and specificity in diagnosis of BMI were 92.9%, 92.9%, 97.0%, and 83.9% for PET/CT and 96.7%, 80.6%, 89.6%, and 93.5% for PHOX2B of BM, respectively. Conclusions. BMUP of pretreatment 18F-FDG PET/CT is a simple and practical method, which has a relatively high diagnostic efficiency in detecting BMI and might decrease unnecessary invasive inspections in some pediatric NB patients.
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