Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma.

BACKGROUND: This retrospective study aimed to develop and validate a combined model based [18F]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. [18F]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.

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes.

OBJECTIVE: To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC). METHODS: In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC. RESULTS: Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types. CONCLUSION: The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease. KEY POINTS: • The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category.

Background: The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods: A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results: The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions: The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.

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

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.

Evaluation of iodine-123-labeled metaiodobenzylguanidine single-photon emission computed tomography/computed tomography based on the International Society of Pediatric Oncology Europe Neuroblastoma score in children with neuroblastoma.

Background: This study sought to examine whether iodine-123-labeled metaiodobenzylguanidine (123I-MIBG) single-photon emission computed tomography/computed tomography (SPECT/CT), which is based on the International Society of Pediatric Oncology Europe Neuroblastoma (SIOPEN) score, could improve the diagnostic efficiency of children with neuroblastoma (NB), and to compare the diagnostic ability of minimal residual disease (MRD) detection and 123I-MIBG SPECT/CT. Methods: We retrospectively analyzed 238 scans of patients who underwent 123I-MIBG SPECT/CT at the Department of Nuclear Medicine, Beijing Friendship Hospital, from January 2021 to December 2021. The diagnostic study was not registered with a clinical trial platform, and the study protocol was not published. The standard was established based on pathology, other relevant imaging examinations, and follow-up. The SIOPEN scores were calculated separately based on planar and tomographic imaging. Results: In a comparison to the standard mentioned in the method, the diagnostic accuracy of planar and tomographic imaging was 151 of 238 (63.5%) and 228 of 238 (95.8%), respectively, and the κ values of the SIOPEN score were 0.468 and 0.855 (P<0.001), respectively. The SIOPEN scores differed significantly among the various subgroups. The polymerase chain reaction (PCR) method used to detect the bone marrow PHOX2B gene was able to find bone/bone marrow metastases (P=0.024, κ=0.282), while the flow cytometry (FCM) assay method was not statistically significant (P=0.417, κ=0.065). Conclusions: 123I-MIBG SPECT/CT, which relies on the semiquantitative assessment of the SIOPEN score, is of clinical importance in the management of pediatric NB. MRD detection can be used to detect early metastasis and recurrence in the bone or bone marrow; however, 123I-MIBG SPECT/CT has better diagnostic value. We intend to conduct further investigations on their prognostic value in the future.

Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature.

OBJECTIVES: To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients. METHODS: A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757-0.963) and 0.824 (95% CI, 0.657-0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility. CONCLUSION: Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy. CRITICAL RELEVANCE STATEMENT: Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. KEY POINTS: • Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification.

Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics.

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.

Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics.

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 (n = 86) and a test set (n = 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 (P < 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.

The Diagnostic Value of 18F-FDG PET/CT Bone Marrow Uptake Pattern in Detecting Bone Marrow Involvement in Pediatric Neuroblastoma Patients.

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.

The prognostic value of 18F-FDG PET/CT intra-tumoural metabolic heterogeneity in pretreatment neuroblastoma patients.

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 18F-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. 18F-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-CSHtotal (r=0.630, P<0.001) showed moderate correlation with the AUC-CSH40%. 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-CSHtotal (P=0.007) were associated with EFS. The AUC-CSHtotal (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.