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

18F-FDG PET/CT volumetric parameter predicts prognosis for neuroblastoma with MYCN gain

PURPOSE

The aim of the study was to evaluate the value of 18F-FDG PET/CT metabolic parameters in neuroblastoma (NB) with MYCN gain.

METHODS

A retrospective analysis was conducted on 87 patients with NB (29 with MYCN gain and 58 with MYCN normal). The region of interest of primary tumors were manually delineated using 3D slicer™ software, and 18F-FDG PET/CT metabolic parameters, including SUVmax, SUVpeak, SUVmean, MTV and TLG were extracted. Logistic regression analyses were used to identify the relationship between 18F-FDG PET/CT metabolic parameters and MYCN gain. Cox proportional hazards regression models were used to assess the associations between 18F-FDG PET/CT metabolic parameters and EFS and OS. Survival curves were generated using the Kaplan-Meier method, and differences in survival between groups were compared using the log-rank test.

RESULTS

A total of 87 NB patients [median age: 40 (20-56) months; 48 girls and 39 boys] were evaluated. Logistic regression analyses revealed that MTV (>133.3 cm3) was an independent predictor of MYCN gain. During the follow-up period of 22 (2-70) months, 21 patients died and 37 patients experienced disease recurrence or progression. Cox proportional hazards regression analyses showed that MTV, in combination with PHOX2B, was an independent prognostic factor for EFS and OS in NB patients with MYCN-gain. Patients with high MTV exhibited significantly shorter EFS and OS compared to those with low MTV.

CONCLUSION

The volumetric parameter MTV derived from 18F-FDG PET/CT imaging can predict MYCN gain in NB patients and provide valuable prognostic information for patients with MYCN-gain NB.

Feasibility of ultra-low-activity 18F-FDG PET/CT imaging in children

OBJECTIVES

To investigate the feasibility of paediatric 18F-FDG total-body PET/CT imaging with an ultra-low activity and explore an optimized acquisition time range.

METHODS

A total of 38 paediatric patients were prospectively enrolled and underwent dynamic total-body PET/CT imaging using ultra-low 18F-FDG activity (0.37 MBq/kg). The 60-minute list-mode raw data were acquired and then reconstructed as static PET images by using 50-51, 50-52, 50-53, 50-54, 50-55, 50-58, 50-60, and 45-60 minutes data, which were noted as G1, G2, G3, G4, G5, G8, G10, and G15, respectively. Image qualities were subjectively evaluated using the Likert scale and were objectively evaluated by the quantitative metrics including standard uptake value (SUV), signal-to-noise ratio (SNR), target-to-background ratio (TBR), and contrast-to-noise ratio (CNR).

RESULTS

The injected activity of FDG was 13.38 ± 5.68 MBq (4.40-28.16 MBq) and produced 0.58 ± 0.19 mSv (0.29-1.04 mSv) of effective dose. The inter-reader agreement of subjective image quality was excellent (kappa = 0.878; 95% CI, 0.845-0.910). The average scores of image quality for G1-G15 were 1.10 ± 0.20, 2.03 ± 0.26, 2.66 ± 0.35, 3.00 ± 0.27, 3.32 ± 0.34, 4.25 ± 0.30, 4.49 ± 0.36, and 4.70 ± 0.37, respectively. All image scores are above 3, and all lesions are detectable starting from G8. SNRs of backgrounds, TBRs, and CNRs were significant differences from the control group before G8 (all P < 0.05).

CONCLUSION

The image quality of the 8 min acquisition for paediatric 18F-FDG total-body PET/CT with an ultra-low activity could meet the diagnostic requirements.

ADVANCES IN KNOWLEDGE

This study confirms the feasibility of ultra-low dose PET imaging in children, and its methods and findings may guide clinical practice. Paediatric patients will benefit from reduced radiation doses.

Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combining 123I-MIBG SPECT-CT radiomics and clinical factors

BACKGROUND

Accurately quantifying event-free survival after induction of remission in high-risk neuroblastoma can lead to better subsequent treatment decisions, including whether more aggressive therapy or milder treatment is needed to reduce unnecessary treatment side effects, thereby improving patient survival.

OBJECTIVE

To develop and validate a 123I-metaiodobenzylguanidine (MIBG) single-photon emission computed tomography-computed tomography (SPECT-CT)-based radiomics nomogram and evaluate its value in predicting event-free survival after induction of remission in high-risk neuroblastoma.

MATERIALS AND METHODS

One hundred and seventy-two patients with high-risk neuroblastoma who underwent an 123I-MIBG SPECT-CT examination were retrospectively reviewed. Eighty-seven patients with high-risk neuroblastoma met the final inclusion and exclusion criteria and were randomized into training and validation cohorts in a 7:3 ratio. The SPECT-CT images of patients were visually analyzed to assess the Curie score. The 3D Slicer software tool was used to outline the region of interest of the lumbar 3-5 vertebral bodies on the SPECT-CT images. Radiomics features were extracted and screened, and a radiomics model was constructed with the selected radiomics features. Univariate and multivariate Cox regression analyses were used to determine clinical risk factors and construct the clinical model. The radiomics nomogram was constructed using multivariate Cox regression analysis by incorporating radiomics features and clinical risk factors. C-index and time-dependent receiver operating characteristic curves were used to evaluate the performance of the different models.

RESULTS

The Curie score had the lowest efficacy for the assessment of event-free survival, with a C-index of 0.576 and 0.553 in the training and validation cohorts, respectively. The radiomics model, constructed from 11 radiomics features, outperformed the clinical model in predicting event-free survival in both the training cohort (C-index, 0.780 vs. 0.653) and validation cohort (C-index, 0.687 vs. 0.667). The nomogram predicted the best prognosis for event-free survival in both the training and validation cohorts, with C-indices of 0.819 and 0.712, and 1-year areas under the curve of 0.899 and 0.748, respectively.

CONCLUSION

123I-MIBG SPECT-CT-based radiomics can accurately predict the event-free survival of high-risk neuroblastoma after induction of remission The constructed nomogram may enable an individualized assessment of high-risk neuroblastoma prognosis and assist clinicians in optimizing patient treatment and follow-up plans, thereby potentially improving patient survival.

Differentiation of early relapse and late relapse in intermediate- and high-risk neuroblastoma with an 18F-FDG PET/CT-based radiomics nomogram

OBJECTIVES

To develop and validate an 18F-FDG PET/CT-based radiomics nomogram for differentiating early relapse and late relapse of intermediate- and high-risk neuroblastoma (NB).

METHODS

A total of eighty-five patients with relapsed NB who underwent 18F-FDG PET/CT were retrospectively evaluated. All selected patients were randomly assigned to the training set and the validation set in a 7:3 ratio. Tumors were segmented using the 3D slicer, followed by radiomics features extraction. Features selection was performed using random forest, and the radiomics score was constructed by logistic regression analysis. Clinical risk factors were identified, and the clinical model was constructed using logistic regression analysis. A radiomics nomogram was constructed by combining the radiomics score and clinical risk factors, and its performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

Finally, the 12 most important radiomics features were used for modeling, with an area under the curve (AUC) of 0.835 and 0.824 in the training and validation sets, respectively. Age at diagnosis and International Neuroblastoma Pathology Classification were determined as clinical risk factors to construct the clinical model. In addition, the nomogram achieved an AUC of 0.902 and 0.889 for identifying early relapse in the training and validation sets, respectively, which is higher than the clinical model (AUC of 0.712 and 0.588, respectively). The predicted early relapse and actual early relapse in the calibration curves were in good agreement. The DCA showed that the radiomics nomogram was clinically useful.

CONCLUSION

Our 18F-FDG PET/CT-based radiomics nomogram can well predict early relapse and late relapse of intermediate- and high-risk NB, which contributes to follow-up and management in clinical practice.

18F-FDG PET/CT-Based Radiomics Nomogram for Prediction of Bone Marrow Involvement in Pediatric Neuroblastoma: A Two-Center Study

RATIONALE AND OBJECTIVES

To assess the predictive ability of an 18F-FDG PET/CT-based radiomics nomogram for bone marrow involvement in pediatric neuroblastoma.

MATERIALS AND METHODS

A total of 241 neuroblastoma patients who underwent 18F-FDG PET/CT at two medical centers were retrospectively evaluated. Data from center A (n = 200) were randomized into a training cohort (n = 140) and an internal validation cohort (n = 60), while data from center B (n = 41) constituted the external validation cohort. For each patient, two regions of interest were defined using the tumor and axial skeleton. The clinical factors and radiomics features were derived to construct the clinical and radiomics models. The radiomics nomogram was built by combining clinical factors and radiomics features. The area under the receiver operating characteristic curves (AUCs) were used to assess the performance of the models.

RESULTS

Radiomics models created from tumor and axial skeleton achieved AUCs of 0.773 and 0.900, and the clinical model had an AUC of 0.858 in the training cohort. By incorporating clinical risk factors and axial skeleton-based radiomics features, the AUC of the radiomics nomogram in the training cohort, internal validation cohort, and external validation cohort was 0.932, 0.887, and 0.733, respectively.

CONCLUSION

The axial skeleton-based radiomics model performed better than the tumor-based radiomics model in predicting bone marrow involvement. Moreover, the radiomics nomogram showed that combining axial skeleton-based radiomics features with clinical risk factors improved their performance.

CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children

BACKGROUND

To investigate the role of CT radiomics in distinguishing Wilms tumor (WT) from clear cell sarcoma of the kidney (CCSK) in pediatric patients.

METHODS

We retrospectively enrolled 83 cases of WT and 33 cases of CCSK. These cases were randomly stratified into a training set (n = 81) and a test set (n = 35). Several imaging features from the nephrographic phase were analyzed, including the maximum tumor diameter, the ratio of the maximum CT value of the tumor solid portion to the mean CT value of the contralateral renal vein (CTmax/CT renal vein), and the presence of dilated peritumoral cysts. Radiomics features from corticomedullary phase were extracted, selected, and subsequently integrated into a logistic regression model. We evaluated the model's performance using the area under the curve (AUC), 95% confidence interval (CI), and accuracy.

RESULTS

In the training set, there were statistically significant differences in the maximum tumor diameter (P = 0.021) and the presence of dilated peritumoral cysts (P = 0.005) between WT and CCSK, whereas in the test set, no statistically significant differences were observed (P > 0.05). The radiomics model, constructed using four radiomics features, demonstrated strong performance in the training set with an AUC of 0.889 (95% CI: 0.811-0.967) and an accuracy of 0.864. Upon evaluation using fivefold cross-validation in the training set, the AUC remained high at 0.863 (95% CI: 0.774-0.952), with an accuracy of 0.852. In the test set, the radiomics model achieved an AUC of 0.792 (95% CI: 0.616-0.968) and an accuracy of 0.857.

CONCLUSION

CT radiomics proves to be diagnostically valuable for distinguishing between WT and CCSK in pediatric cases.

A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges

Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various imaging modalities, including computed tomography, magnetic resonance imaging, and positron emission tomography, are utilized to assess neuroblastoma. Nevertheless, these conventional imaging modalities have limitations in providing quantitative information for accurate diagnosis and prognosis. Radiomics, an emerging technique, can extract intricate medical imaging information that is imperceptible to the human eye and transform it into quantitative data. In conjunction with deep learning algorithms, radiomics holds great promise in complementing existing imaging modalities. The aim of this review is to showcase the potential of radiomics and deep learning advancements to enhance the diagnostic capabilities of current imaging modalities for neuroblastoma.

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.

Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors

This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy and select features. A risk model was built to stratify low-, intermediate-, and high-risk groups. An image-defined risk factor (IDRFs) model was developed to classify 266 patients with malignant PNTs and one or more IDRFs into high-risk and non-high-risk groups. The performance of the predictive models was evaluated with respect to accuracy (Acc) and receiver operating characteristic (ROC) curve, including the area under the ROC curve (AUC). The risk model demonstrated good discrimination capability, with an area under the curve (AUC) of 0.903 to distinguish high-risk from non-high-risk groups, and 0.747 to classify intermediate- and low-risk groups. In the IDRF-based risk model with the number of IDRFs, the AUC was 0.876 for classifying the high-risk and non-high-risk groups. Radiomic analysis based on preoperative CT images has the potential to stratify the risk of pediatric malignant PNTs. It had outstanding efficiency in distinguishing patients in the high-risk group, and this predictive model of risk stratification could assist in selecting optimal aggressive treatment options.

Axial Skeleton Radiomics of 18F-FDG PET/CT: Impact on Event-Free Survival Prediction in High-Risk Pediatric Neuroblastoma

OBJECTIVES

To construct and validate a combined model based on axial skeleton radiomics of 18F-FDG PET/CT for predicting event-free survival in high-risk pediatric neuroblastoma patients.

MATERIALS AND METHODS

Eighty-seven high-risk neuroblastoma patients were retrospectively enrolled in this study and randomized in a 7:3 ratio to the training and validation cohorts. The radiomics model was constructed using radiomics features that were extracted from the axial skeleton. A univariate Cox regression analysis was then performed to screen clinical risk factors associated with event-free survival for building clinical model. Radiomics features and clinical risk factors were incorporated to construct the combined model for predicting the event-free survival in high-risk neuroblastoma patients. The performance of the models was evaluated by the C-index.

RESULTS

Eighteen radiomics features were selected to build the radiomics model. The radiomics model achieved better event-free survival prediction than the clinical model in the training cohort (C-index: 0.846 vs. 0.612) and validation cohort (C-index: 0.754 vs. 0.579). The combined model achieved the best prognostic prediction performance with a C-index of 0.863 and 0.799 in the training and validation cohorts, respectively.

CONCLUSION

The combined model integrating radiomics features and clinical risk factors showed more accurate predictive performance for event-free survival in high-risk pediatric neuroblastoma patients, which helps to design individualized treatment strategies and regular follow-ups.

Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas

OBJECTIVES

We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas.

METHODS

A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set.

RESULTS

The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%.

CONCLUSION

The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models.

KEY POINTS

• MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.

Clinical value of combined serum CA125, NSE and 24-hour urine VMA for the prediction of recurrence in children with neuroblastoma

BACKGROUND

In this study, we intend to retrospectively analyze the clinical data of postoperative neuroblastoma children, including the results of follow-up examinations and laboratory tests, to explore the clinical value of combined serum Carbohydrate antigen 125 (CA125), neuron-specific enolase (NSE) and 24-hour urine vanillylmandelic acid (VMA) levels at baseline for the prediction of recurrence in children with neuroblastoma.

METHODS

265 children with neuroblastoma were successfully followed up, including 163 cases without recurrence (non-recurrence group) and 102 cases with recurrence (recurrence group). The levels of 24-hour urine VMA were determined using spectrophotometric methods. Additionally, the serum levels of CA125 and NSE were measured using electrochemiluminescence immunoassay.

RESULTS

The serum CA125, NSE and 24-hour urine VMA levels were significantly higher in the recurrence group than in the non-recurrence group. It demonstrated a significant positive correlation between the levels of serum CA125, NSE, and 24-hour urine VMA in all children with neuroblastoma. All children in stage IV of neuroblastoma had the highest level of serum CA125, NSE and 24-hour urine VMA and vice versa. The combined CA125, NSE and VMA had significantly better sensitivity and specificity than an individual marker.

CONCLUSIONS

Combined serum CA125, NSE and 24-hour urine VMA had the potential to predict neuroblastoma recurrence more effectively.

Current Status and Future Perspective on Molecular Imaging and Treatment of Neuroblastoma

Neuroblastoma is the most common extracranial solid tumor in children and arises from anywhere along the sympathetic nervous system. It is a highly heterogeneous disease with a wide range of prognosis, from spontaneous regression or maturing to highly aggressive. About half of pediatric neuroblastoma patients develop the metastatic disease at diagnosis, which carries a poor prognosis. Nuclear medicine plays a pivotal role in the diagnosis, staging, response assessment, and long-term follow-up of neuroblastoma. And it has also played a prominent role in the treatment of neuroblastoma. Because the structure of metaiodobenzylguanidine (MIBG) is similar to that of norepinephrine, 90% of neuroblastomas are MIBG-avid. 123I-MIBG whole-body scintigraphy is the standard nuclear imaging technique for neuroblastoma, usually in combination with SPECT/CT. However, approximately 10% of neuroblastomas are MIBG nonavid. PET imaging has many technical advantages over SPECT imaging, such as higher spatial and temporal resolution, higher sensitivity, superior quantitative capability, and whole-body tomographic imaging. In recent years, various tracers have been used for imaging neuroblastoma with PET. The importance of patient-specific targeted radionuclide therapy for neuroblastoma therapy has also increased. 131I-MIBG therapy is part of the front-line treatment for children with high-risk neuroblastoma. And peptide receptor radionuclide therapy with radionuclide-labeled somatostatin analogues has been successfully used in the therapy of neuroblastoma. Moreover, radioimmunoimaging has important applications in the diagnosis of neuroblastoma, and radioimmunotherapy may provide a novel treatment modality against neuroblastoma. This review discusses the use of current and novel radiopharmaceuticals in nuclear medicine imaging and therapy of neuroblastoma.

Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma

PURPOSE

To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.

MATERIALS AND METHODS

Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal-Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.

RESULTS

The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (Z = 2.003, P = 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.

CONCLUSION

The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.

Diagnostic Value of 18F-FDG PET/CT-Based Radiomics Nomogram in Bone Marrow Involvement of Pediatric Neuroblastoma

OBJECTIVES

To develop and validate an ¹⁸F-FDG PET/CT-based radiomics nomogram and evaluate the value of the ¹⁸F-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 ¹⁸F-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.

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Objective

To develop and validate an ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma.

Methods

A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).

Results

Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility.

Conclusion

The ¹⁸F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01283-8.