Diagnostic Performance of F-FDG PET(CT) in Bone-Bone Marrow Involvement in Pediatric Neuroblastoma: A Systemic Review and Meta-Analysis.
Sun Lixin,Zhang Bingye,Peng Ruchen
Contrast media & molecular imaging
Objective:We sought to perform a systemic review and meta-analysis of the diagnostic performance of F-fluorodeoxyglucose (F-FDG) positron emission tomography (computed tomography) (PET(CT)) in detection of bone and/or bone marrow involvement (BMI) in pediatric neuroblastoma (NB). Materials and Methods:We searched electronic databases Pubmed and Embase to retrieve relevant references. We calculated pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (DOR), and the area under the curve (AUC). Moreover, a summary receiver operating characteristic (SROC) curve and likelihood ratio dot plot were plotted. Study-between statistical heterogeneity was evaluated via -square index ( ). Subgroup analyses were used to explore heterogeneity. Results:Seven studies including 127 patients were involved in this meta-analysis. The overall sensitivity and specificity were 0.87 (95% CI: 0.65-0.96) with heterogeneity = 88.1% ( < 0.001) and 0.96 (95% CI: 0.67-1.00) with heterogeneity = 77.8% ( < 0.001), respectively. The pooled LR+, LR-, and DOR were 21.3 (95% CI: 2.1-213.9), 0.14 (95% CI: 0.05-0.40), and 157 (95% CI: 16-1532), respectively. The area under the SROC curve was 0.97 (95% CI: 0.95-0.98). Conclusions:Through a meta-analysis, this study suggested that F-FDG PET(CT) has a good overall diagnostic accuracy in the detection of bone/BMI in pediatric neuroblastoma.
Prognostic Value of Interim 18F-DOPA and 18F-FDG PET/CT Findings in Stage 3-4 Pediatric Neuroblastoma.
Ko Kuan-Yin,Yen Ruoh-Fang,Ko Chi-Lun,Chou Shu-Wei,Chang Hsiu-Hao,Yang Yung-Li,Jou Shiann-Tarng,Hsu Wen-Ming,Lu Meng-Yao
Clinical nuclear medicine
PURPOSE:This retrospective study aimed to determine the prognostic value of imaging parameters derived from midtherapy 18F-fluorodihydroxyphenylalanine (18F-DOPA) and 18F-FDG PET in pediatric patients with stage 3-4 neuroblastoma. METHODS:We enrolled 32 stage 3-4 pediatric neuroblastoma patients who underwent 18F-DOPA and 18F-FDG PET/CT scans before and after 3 chemotherapy cycles. We measured metabolic and volumetric parameters and applied a metabolic burden scoring system to evaluate the primary tumor extent and soft tissue metastases and that of bone/bone marrow involvement. The associations between these parameters and clinical outcomes were investigated. RESULTS:Over a median follow-up period of 47 months (range, 3-137 months), 16 patients experienced disease progression, and 13 died. After adjustment for clinical factors, multivariate Cox proportional hazard models showed that interim tumor FDG/FDOPA SUVmax (hazard ratio [HR], 5.94; 95% confidence interval [CI], 1.10-34.98) and interim FDOPA whole-body metabolic burden scores (WBMB) (HR, 7.30; 95% CI, 1.50-35.50) were significant prognostic factors for overall survival (OS). Only interim FDOPA WBMB scores (HR, 7.05; 95% CI, 1.02-48.7) were predictive of progression-free survival. Based on median cutoff values, prognosis (OS and progression-free survival) was significantly associated with an interim FDOPA WBMB score ≥21.92 (all P < 0.05) and interim tumor FDG/FDOPA (SUVmax) score ≥0.57 with poor OS (P < 0.05). CONCLUSIONS:Our results indicate that midtreatment FDG and FDOPA PET/CT could serve as prognostic markers in stage 3-4 neuroblastoma patients.
Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based F-FDG PET/CT Radiomics.
Feng Lijuan,Qian Luodan,Yang Shen,Ren Qinghua,Zhang Shuxin,Qin Hong,Wang Wei,Wang Chao,Zhang Hui,Yang Jigang
Diagnostics (Basel, Switzerland)
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 F-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.
Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma Pre-therapy 18F-FDG PET/CT Radiomics.
Qian Luodan,Yang Shen,Zhang Shuxin,Qin Hong,Wang Wei,Kan Ying,Liu Lei,Li Jixia,Zhang Hui,Yang Jigang
Frontiers in medicine
Purpose:This study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB. Method:One hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2-9.8 years) with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed. Results:The patients were classified into a training set ( = 86) and a test set ( = 36). Accordingly, 6, 8, and 7 radiomic features were selected to establish R_models for predicting MYCN, 1p and 11q status. The R_models showed a strong power for identifying these aberrations, with area under ROC curves (AUCs) of 0.96, 0.89, and 0.89 in the training set and 0.92, 0.85, and 0.84 in the test set. When combining clinical characteristics and radiomic signature, the AUCs increased to 0.98, 0.91, and 0.93 in the training set and 0.96, 0.88, and 0.89 in the test set. The CR_models had the greatest performance for MYCN, 1p and 11q predictions ( < 0.05). Conclusions:The pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice.
Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma.
BMC medical imaging
BACKGROUND: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. METHODS: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). RESULTS: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. CONCLUSIONS:The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.
The prognostic value of F-FDG PET/CT intra-tumoural metabolic heterogeneity in pretreatment neuroblastoma patients.
Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND:Neuroblastoma (NB) is the most common tumour in children younger than 5 years old and notable for highly heterogeneous. Our aim was to quantify the intra-tumoural metabolic heterogeneity of primary tumour lesions by using F-FDG PET/CT and evaluate the prognostic value of intra-tumoural metabolic heterogeneity in NB patients. METHODS:We retrospectively enrolled 38 pretreatment NB patients in our study. F-FDG PET/CT images were reviewed and analyzed using 3D slicer software. The semi-quantitative metabolic parameters of primary tumour were measured, including the maximum standard uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion glycolysis (TLG). The areas under the curve of cumulative SUV-volume histogram index (AUC-CSH index) was used to quantify intra-tumoural metabolic heterogeneity. The median follow-up was 21.3 months (range 3.6 - 33.4 months). The outcome endpoint was event-free survival (EFS), including progression-free survival and overall survival. Survival analysis was performed using Cox regression models and Kaplan Meier survival plots. RESULTS:In all 38 newly diagnosed NB patients, 2 patients died, and 17 patients experienced a relapse. The AUC-CSH (r=0.630, P<0.001) showed moderate correlation with the AUC-CSH. In univariate analysis, chromosome 11q deletion (P=0.033), Children's Oncology Group (COG) risk grouping (P=0.009), bone marrow involvement (BMI, P=0.015), and AUC-CSH (P=0.007) were associated with EFS. The AUC-CSH (P=0.036) and BMI (P=0.045) remained significant in multivariate analysis. The Kaplan Meier survival analyses demonstrated that patients with higher intra-tumoural metabolic heterogeneity and BMI had worse outcomes (log-rank P=0.002). CONCLUSION:The intra-tumoural metabolic heterogeneity of primary lesions in NB was an independent prognostic factor for EFS. The combined predictive effect of intra-tumoural metabolic heterogeneity and BMI provided prognostic survival information in NB patients.
An F-FDG PET/CT radiomics nomogram for differentiation of high-risk and non-high-risk patients of the International Neuroblastoma Risk Group Staging System.
European journal of radiology
PURPOSE:To develop and validate an F-FDG PET/CT radiomics nomogram for non-invasive differentiation of high-risk and non-high-risk patients of the International Neuroblastoma Risk Group (INRG) Staging System (INRGSS). METHOD:One hundred thirty-nine neuroblastoma patients were retrospectively enrolled and classified into a training set (n = 84) and validation set (n = 55). Radiomics features were extracted from F-FDG PET/CT images, a radiomics signature was constructed, and a radiomics score (Rad score) was calculated. Then, univariate and multivariate logistic regression analyses were used to screen out the independent clinical factors and construct the clinical model. A radiomics nomogram was developed based on the Rad score and independent clinical factors. The performance of the clinical model, Rad score, and nomogram was assessed by receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS:Seven radiomics features were selected to build the radiomics signature. The age at diagnosis, the INRG stage, neuron-specific enolase (NSE) and Rad score showed a significant difference between the high-risk and non-high-risk patients. The radiomics nomogram incorporating the Rad score and the above clinical factors demonstrated favorable predictive value for differentiating high-risk from non-high-risk, yielded AUCs of 0.988 and 0.971 in the training and validation sets, respectively. The calibration curves showed that the radiomics nomogram had the goodness of fit, and the DCA demonstrated that the radiomics nomogram was clinically useful. CONCLUSIONS:The radiomics nomogram, which incorporates the Rad score and clinical factors can well predict high-risk and non-high-risk patients of the INRGSS. It may help the disease follow-up and management in clinical practice and assist in personalized and precise treatment of neuroblastoma.
Diagnostic Value of F-FDG PET/CT-Based Radiomics Nomogram in Bone Marrow Involvement of Pediatric Neuroblastoma.
OBJECTIVES:To develop and validate an F-FDG PET/CT-based radiomics nomogram and evaluate the value of the F-FDG PET/CT-based radiomics nomogram for the diagnosis of bone marrow involvement (BMI) in pediatric neuroblastoma. MATERIALS AND METHODS:A total of 144 patients with neuroblastoma (100 in the training cohort and 44 in the validation cohort) were retrospectively included. The PET/CT images of patients were visually assessed. The results of bone marrow aspirates or biopsies were used as the gold standard for BMI. Radiomics features and conventional PET parameters were extracted using the 3D slicer. Features were selected by the least absolute shrinkage and selection operator regression, and radiomics signature was constructed. Univariate and multivariate logistic regression analyses were applied to identify the independent clinical risk factors and construct the clinical model. Other different models, including the conventional PET model, combined PET-clinical model and combined radiomics model, were built using logistic regression. The combined radiomics model was based on clinical factors, conventional PET parameters and radiomics signature, which was presented as a radiomics nomogram. The diagnostic performance of the different models was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS:By visual assessment, BMI was observed in 80 patients. Four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume, and total lesion glycolysis) were extracted. And 15 radiomics features were selected to build the radiomics signature. The 11q aberration, neuron-specific enolase and vanillylmandelic acid were identified as the independent clinical risk factors to establish the clinical model. The radiomics nomogram incorporating the radiomics signature, the independent clinical risk factors and SUVmean demonstrated the best diagnostic value for identifying BMI, with an area under the curve (AUC) of 0.963 and 0.931 in the training and validation cohorts, respectively. And the DCA demonstrated that the radiomics nomogram was clinically useful. CONCLUSION:The F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature, independent clinical risk factors and conventional PET parameters could improve the diagnostic performance for BMI of pediatric neuroblastoma without additional medical costs and radiation exposure.
Improved risk stratification by PET-based intratumor heterogeneity in children with high-risk neuroblastoma.
Frontiers in oncology
Purpose:The substratification of high-risk neuroblastoma is challenging, and new predictive imaging biomarkers are warranted for better patient selection. The aim of the study was to evaluate the prognostic role of PET-based intratumor heterogeneity and its potential ability to improve risk stratification in neuroblastoma. Methods:Pretreatment F-FDG PET/CT scans from 112 consecutive children with newly diagnosed neuroblastoma were retrospectively analyzed. The primary tumor was segmented in the PET images. SUVs, volumetric parameters including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and texture features were extracted. After the exclusion of imaging features with poor and moderate reproducibility, the relationships between the imaging indices and clinicopathological factors, as well as event-free survival (EFS), were assessed. Results:The median follow-up duration was 33 months. Multivariate analysis showed that PET-based intratumor heterogeneity outperformed clinicopathological features, including age, stage, and MYCN, and remained the most robust independent predictor for EFS [training set, hazard ratio (HR): 6.4, 95% CI: 3.1-13.2, < 0.001; test set, HR: 5.0, 95% CI: 1.8-13.6, = 0.002]. Within the clinical high-risk group, patients with a high metabolic heterogeneity showed significantly poorer outcomes (HR: 3.3, 95% CI: 1.6-6.8, = 0.002 in the training set; HR: 4.4, 95% CI: 1.5-12.9, = 0.008 in the test set) compared to those with relatively homogeneous tumors. Furthermore, intratumor heterogeneity outran the volumetric indices (MTVs and TLGs) and yielded the best performance of distinguishing high-risk patients with different outcomes with a 3-year EFS of 6% vs. 47% ( = 0.001) in the training set and 9% vs. 51% ( = 0.004) in the test set. Conclusion:PET-based intratumor heterogeneity was a strong independent prognostic factor in neuroblastoma. In the clinical high-risk group, intratumor heterogeneity further stratified patients with distinct outcomes.