The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma.

OBJECTIVE: Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS: 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS: The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS: CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

Distinguishing diffuse large B-cell lymphoma from Hodgkin's lymphoma in children using an enhanced computed tomography radiomics approach.

Background: Diffuse large B-cell lymphoma (DLBCL) and Hodgkin's lymphoma (HL) are two completely different pathologic subtypes of lymphoma with distinctly different clinical presentations and treatment options. Thus, accurately differentiating between the two subtypes has important clinical implications. This study aimed to construct a radiomics model capable of distinguishing between DLBCL and HL based on enhanced computed tomography (CT) for the non-invasive diagnosis of lymphoma subtypes. Methods: The clinical and imaging data of 16 patients confirmed to have DLBCL (33 lymphomas), and 50 patients confirmed to have HL (106 lymphomas) were retrospectively analyzed. The patients were completely randomized into a training set (n=107, DLBLC׃HL ratio: 23׃84) and a test set (n=32, DLBCL׃HL ratio: 10׃22). After multiple down-sampling, 2,264 radiomics features were automatically extracted by the application software. Feature selection was performed in the training set using Spearman's rank correlation coefficients, maximum correlation minimum redundancy, and the least absolute shrinkage and selection operator algorithm in that order. The features after selection were used to build radiomics models by logistic regression (LR) and quadratic discriminant analysis (QDA). We evaluated the model ability using receiver operating characteristic (ROC) curves and the DeLong test. Moreover, clinical indicators, such as gender, age, clinical stage, and lactate dehydrogenase (LDH), were collected and analyzed by univariate and multivariate LR analyses. The radiomics characteristics with clinical indicators that had independent influences on predicting the pathological subtypes were used to establish a comprehensive classification model. Results: The analysis of the clinical data revealed that LDH can serve as a clinical indicator that has an independent influence on the prediction of HL and DLBCL. The results of the radiomics models were as follows: Radiomics_LR: area under the curve (AUC) =0.814 [95% confidence interval (CI): 0.628-0.999]; and Radiomics_QDA: AUC =0.841 (95% CI: 0.691-0.991). Following the inclusion of LDH as a clinical indicator in the analysis, the results of the comprehensive models were as follows: Radiomics + LDH_LR: AUC =0.768 (95% CI: 0.580-0.956); and Radiomics + LDH_QDA: AUC was 0.845 (95% CI: 0.695-0.996). Conclusions: The models based on radiomics and clinical features were able to effectively distinguish DLBCL from HL. The model with the best overall performance was the Radiomics_LR model.

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics.

OBJECTIVE: To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma. METHODS: Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical-radiomics model incorporated both clinical and radiomics features. RESULTS: The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563-0.845), 0.830 (95% CI: 0.704-0.959), and 0.874 (95% CI: 0.768-0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616-0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively. CONCLUSION: CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

Fluoxetine Successfully Treats Intracranial Enterovirus E18 Infection in a Patient with CD79a Deficiency Arising from Segmental Uniparental Disomy of Chromosome 19.

The pre BCR complex plays a crucial role in B cell production, and its successful expression marks the B cell differentiation from the pro-B to pre-B. The CD79a and CD79b mutations, encoding Igα and Igß respectively, have been identified as the cause of autosomal recessive agammaglobulinemia (ARA). Here, we present a case of a patient with a homozygous CD79a mutation, exhibiting recurrent respiratory infections, diarrhea, growth and development delay, unique facial abnormalities and microcephaly, as well as neurological symptoms including tethered spinal cord, sacral canal cyst, and chronic enteroviral E18 meningitis. Complete blockade of the early B cell development in the bone marrow of the patient results in the absence of peripheral circulating mature B cells. Whole exome sequencing revealed a Loss of Heterozygosity (LOH) of approximately 19.20Mb containing CD79a on chromosome 19 in the patient. This is the first case of a homozygous CD79a mutation caused by segmental uniparental diploid (UPD). Another key outcome of this study is the effective management of long-term chronic enteroviral meningitis using a combination of intravenous immunoglobulin (IVIG) and fluoxetine. This approach offers compelling evidence of fluoxetine's utility in treating enteroviral meningitis, particularly in immunocompromised patients.

Image-defined risk factors associated with MYCN oncogene amplification in neuroblastoma and their association with overall survival.

OBJECTIVE: The MYCN oncogene is a critical factor in the development and progression of neuroblastoma, and image-defined risk factors (IDRFs) are radiological findings used for the preoperative staging of neuroblastoma. This study aimed to investigate the specific categories of IDRFs associated with MYCN amplification in neuroblastoma and their association with overall survival. METHOD: A retrospective analysis was conducted on a cohort of 280 pediatric patients diagnosed with neuroblastoma, utilizing a combination of clinical and radiological data. MYCN amplification status was ascertained through molecular testing, and the assessment of IDRFs was conducted using either contrast-enhanced computed tomography or magnetic resonance imaging. The specific categories of IDRFs associated with MYCN amplification and their association with overall survival were analyzed. RESULTS: MYCN amplification was identified in 19.6% (55/280) of patients, with the majority of primary lesions located in the abdomen (53/55, 96.4%). Lesions accompanied by MYCN amplification exhibited significantly larger tumor volume and a greater number of IDRFs compared with those without MYCN amplification (P < 0.001). Both univariate and multivariate analyses revealed that coeliac axis/superior mesenteric artery encasement and infiltration of adjacent organs/structures were independently associated with MYCN amplification in abdominal neuroblastoma (P < 0.05). Patients presenting with more than four IDRFs experienced a worse prognosis (P = 0.017), and infiltration of adjacent organs/structures independently correlated with overall survival in abdominal neuroblastoma (P = 0.009). CONCLUSION: The IDRFs are closely correlated with the MYCN amplification status and overall survival in neuroblastoma.

Defect engineering to tailor structure-activity relationship in biodegradable nanozymes for tumor therapy by dual-channel death strategies.

Pursuing biodegradable nanozymes capable of equipping structure-activity relationship provides new perspectives for tumor-specific therapy. A rapidly degradable nanozymes can address biosecurity concerns. However, it may also reduce the functional stability required for sustaining therapeutic activity. Herein, the defect engineering strategy is employed to fabricate Pt-doping MoOx (PMO) redox nanozymes with rapidly degradable characteristics, and then the PLGA-assembled PMO (PLGA@PMO) by microfluidics chip can settle the conflict between sustaining therapeutic activity and rapid degradability. Density functional theory describes that Pt-doping enables PMO nanozymes to exhibit an excellent multienzyme-mimicking catalytic activity originating from synergistic catalysis center construction with the interaction of Pt substitution and oxygen vacancy defects. The peroxidase- (POD), oxidase- (OXD), glutathione peroxidase- (GSH-Px), and catalase- (CAT) mimicking activities can induce robust ROS output and endogenous glutathione depletion under tumor microenvironment (TME) response, thereby causing ferroptosis in tumor cells by the accumulation of lipid peroxide and inactivation of glutathione peroxidase 4. Due to the activated surface plasmon resonance effect, the PMO nanozymes can cause hyperthermia-induced apoptosis through 1064 nm laser irradiation, and augment multienzyme-mimicking catalytic activity. This work represents a potential biological application for the development of therapeutic strategy for dual-channel death via hyperthermia-augmented enzyme-mimicking nanocatalytic therapy.

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.

Identification of an Ultra-High-Risk Subgroup of Neuroblastoma Patients within the High-Risk Cohort Using a Computed Tomography-Based Radiomics Approach.

RATIONALE AND OBJECTIVES: To identify ultra-high-risk (UHR) neuroblastoma patients who experienced disease-related mortality within 18 months of diagnosis within the high-risk cohort using computed tomography (CT)-based radiomics analysis. MATERIALS AND METHODS: A retrospective analysis was conducted on 105 high-risk neuroblastoma patients, divided into a training set (n = 74) and a test set (n = 31). Radiomics features were extracted and selected from arterial phase CT images, and an optimal radiomics signature was established using the support vector machine algorithm. Evaluation metrics, including area under the curve (AUC) and 95% confidence interval (CI), were calculated. Furthermore, the fit and clinical benefit of the signature, along with its correlation with overall survival (OS), were analyzed. RESULTS: The optimal radiomics signature comprised 11 features. In the training set, AUC and accuracy were 0.911 (95% CI: 0.840-0.982) and 0.892, respectively. In the test set, AUC and accuracy were 0.828 (95% CI: 0.669-0.987) and 0.839, respectively. There was no significant difference between predicted probability and actual probability, and the signature demonstrated net benefit. The concordance index of this signature for predicting OS was 0.743 (95% CI: 0.672-0.814) in the training set and 0.688 (95% CI: 0.566-0.810) in the test set. Moreover, the signature achieved AUC values of 0.832, 0.863, and 0.721 for 1-year, 2-year, and 3-year OS in the training set, and 0.870, 0.836, and 0.638 in the test set for the respective time periods. CONCLUSION: The utilization of CT-based radiomics signature to identify an UHR subgroup of neuroblastoma patients within the high-risk cohort can help aid in predicting early disease progression.

Application of dual-energy CT with prospective ECG-gating in cardiac CT angiography for children: Radiation and contrast agent dose.

OBJECTIVE: This research aimed to investigate the feasibility of utilizing dual-energy CT virtual monoenergetic images (VMI1) with prospective electrocardiogram (ECG2) gating for reducing radiation and contrast agent doses in pediatric patients with congenital heart disease (CHD3). METHODS: There were 100 pediatric patients with CHD included in this study. Group A (n = 50) underwent dual-energy scanning with prospective ECG-gating, and group B (n = 50) underwent conventional scanning with retrospective ECG-gating. Comparative analysis of CT values of lumen, objective image quality assessment, subjective image quality evaluations, and diagnostic efficacy were performed. RESULTS: CT values, image noise, signal-to-noise ratio (SNR4), and contrast-to-noise ratio (CNR5) were significantly affected by the VMI energy level, and they all increased with decreasing energy levels (P > 0.05). Combining subjective evaluation, the 45 keV VMI was considered the optimum image in group A. The 45 keV VMI exhibited higher CT values of lumen compared to conventional scanning images (P < 0.003 âˆ¼ 0.836), but meanwhile, the image noise was also higher in the 45 keV VMI (P = 0.004). Differences between the two groups in SNR, CNR, and diagnostic accuracy were not statistically significant. Compared to group B, the 45 keV VMI showed fewer contrast-induced artifacts (P < 0.001) and higher image quality score (P = 0.037). Group A had a 64 % reduction in radiation dose and a 40 % decrease in iodine dose compared to group B. CONCLUSION: The combination of dual-energy CT with prospective ECG-gating reduces radiation and iodine doses in pediatric patients with CHD. The 45 keV VMI can provide clinically acceptable image quality while declining contrast agent artifacts.

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.

Computed Tomography-Based Radiomics Signature for Predicting Segmental Chromosomal Aberrations at 1p36 and 11q23 in Pediatric Neuroblastoma.

OBJECTIVE: This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma. METHODS: Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI). RESULTS: Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated. CONCLUSIONS: Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.

Correlations between contrast-enhanced CT-measured extracellular volume fraction, histopathological features, and MYCN amplification status in abdominal neuroblastoma: a retrospective study.

PURPOSE: To retrospectively investigate the correlations between contrast-enhanced CT (CECT)-measured extracellular volume fraction (fECV) and histopathological features, as well as MYCN amplification status, in abdominal neuroblastoma. MATERIALS AND METHODS: One hundred and forty-one patients with abdominal neuroblastoma who underwent CECT scanning were retrospectively enrolled. Calculation of fECV involved the measurement of CT values within regions of interest located within the neuroblastoma and aorta on both non-contrast-enhanced CT and equilibrium CECT. The correlations between fECV and various factors, including pathological subtype, mitosis karyorrhexis index (MKI), Shimada classification, MYCN amplification status, International Neuroblastoma Risk Group (INRG) stage, and risk group were analyzed using either the Mann-Whitney U test or Kruskal-Wallis test. RESULTS: Neuroblastoma and ganglioneuroblastoma exhibited fECV values of 0.349 (0.252, 0.424) and 0.438 (0.327, 0.508), respectively, indicating a statistically significant difference (Z = 2.200, P = 0.028). Additionally, the fECV decreased significantly in neuroblastoma with high MKI (H = 8.314, P = 0.016) or unfavorable histology (Z = 3.880, P < 0.001), as well as in those with MYCN amplification (Z = 5.486, P < 0.001). Notably, a significant variation in fECV was observed among different INRG stages (H = 16.881, P <0.001) and risk groups (H = 29.014, P < 0.001). CONCLUSION: CECT-derived fECV is associated with histopathological features, MYCN amplification status, INRG stage, and risk stratification of abdominal neuroblastoma, reflecting a potential correlation between the extracellular matrix and the biological behavior of neuroblastoma.

Case report: The CT features of pediatric retroperitoneal extrarenal Wilms tumor: a report of two cases and literature review.

Retroperitoneal extrarenal Wilms tumor is a rare condition in children that can be easily misdiagnosed as other retroperitoneal malignancies unrelated to the renal origin. Computerized tomography scan plays a crucial role in diagnosing and distinguishing retroperitoneal malignancies. In this report, we present two cases of retroperitoneal extrarenal Wilms tumor in children who were admitted due to abdominal mass. Laboratory examination did not reveal any significant abnormality. However, a computerized tomography scan revealed a solid or cystic-solid mass in the retroperitoneum accompanied by a bone spur extending from the anterior edge of the vertebral body to the back of the mass, while the origin of the tumor remained unclear. By analyzing these two cases and reviewing previous studies on retroperitoneal extrarenal Wilms tumor in children, we summarized the clinical and imaging characteristics of this rare condition. We also found that the presence of a spinal deformity adjacent to the mass might indicate the possibility of a retroperitoneal extrarenal Wilms tumor.

Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma.

PURPOSE: To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. METHODS: We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n = 89). To balance the classes in the training group, a Synthetic Minority Over-sampling Technique was applied. A logistic regression radiomics model based on the radiomics features after dimensionality reduction was then constructed and validated in both the training and testing groups. To evaluate the diagnostic performance of the radiomics model, the receiver operating characteristic curve and calibration curve were utilized. Moreover, the decision curve analysis to assess the net benefits of the radiomics model at different high-risk thresholds was employed. RESULTS: Seventeen radiomics features were used to construct radiomics model. In the training group, radiomics model achieved an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.851 (95% confidence interval (CI) 0.805-0.897), 0.770, 0.694, and 0.847, respectively. In the testing group, radiomics model achieved an AUC, accuracy, sensitivity, and specificity of 0.816 (95% CI 0.725-0.906), 0.787, 0.793, and 0.778, respectively. The calibration curve indicated that the radiomics model was well fitted in both the training and testing groups (p > 0.05). Decision curve analysis further confirmed that the radiomics model performed well at different high-risk thresholds. CONCLUSION: Radiomics analysis of contrast-enhanced CT demonstrates favorable diagnostic capabilities in distinguishing the INPC subgroups of neuroblastoma. CRITICAL RELEVANCE STATEMENT: Radiomics features of contrast-enhanced CT images correlate with the International Neuroblastoma Pathology Classification (INPC) of neuroblastoma.

Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system.

BACKGROUND: To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas. PROCEDURE: This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated. RESULTS: The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively. CONCLUSIONS: CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.

Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.

PURPOSE: To examine the potential of whole-tumor radiomics analysis of T2-weighted imaging (T2WI) in differentiating neuroblastoma (NB) from ganglioneuroblastoma/ganglioneuroma (GNB/GN) in children. MATERIALS AND METHODS: This study included 102 children with peripheral neuroblastic tumors, comprising 47 NB patients and 55 GNB/GN patients, which were randomly divided into a training group (n = 72) and a test group (n = 30). Radiomics features were extracted from T2WI images, and feature dimensionality reduction was applied. Linear discriminant analysis was used to construct radiomics models, and one-standard error role combined with leave-one-out cross-validation was used to choose the optimal radiomics model with the least predictive error. Subsequently, the patient age at initial diagnosis and the selected radiomics features were incorporated to construct a combined model. The receiver operator characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC) were applied to evaluate the diagnostic performance and clinical utility of the models. RESULTS: Fifteen radiomics features were eventually chosen to construct the optimal radiomics model. The area under the curve (AUC) of the radiomics model in the training group and test group was 0.940 [95% confidence interval (CI) 0.886, 0.995] and 0.799 (95%CI 0.632, 0.966), respectively. The combined model, which incorporated patient age and radiomics features, achieved an AUC of 0.963 (95%CI 0.925, 1.000) in the training group and 0.871 (95%CI 0.744, 0.997) in the test group. DCA and CIC demonstrated that the radiomics model and combined model could provide benefits at various thresholds, with the combined model being superior to the radiomics model. CONCLUSION: Radiomics features derived from T2WI, in combination with the age of the patient at initial diagnosis, may offer a quantitative method for distinguishing NB from GNB/GN, thus aiding in the pathological differentiation of peripheral neuroblastic tumors in children.

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.

Correlations Between Preoperative Radiographic Vascular Involvement of Abdominal/Pelvic Neuroblastomas on Computed Tomography and Intraoperative Vascular Injuries: Experience From a Tertiary Children's Hospital.

RATIONALE AND OBJECTIVES: This research examines the prevalence and occurrence of intraoperative vascular injuries in abdominal or pelvic neuroblastomas. It also investigates the correlations between preoperative radiographic vascular involvement on computed tomography (CT) and intraoperative vascular injuries in abdominal or pelvic neuroblastomas. MATERIALS AND METHODS: This study enrolled 297 patients with abdominal or pelvic neuroblastomas. The relationships between neuroblastomas and adjacent arteries on preoperative CT were categorized as no contact, contact (less than 50% of vessel circumference involved), partial encasement (less than 100% of vessel circumference involved), and total encasement (100% of vessel circumference involved). Similarly, the relationships between neuroblastomas and adjacent veins on preoperative CT were categorized as no compression, flattened with a visible lumen, and flattened with an invisible lumen. Furthermore, the correlations between preoperative radiographic vascular involvement of neuroblastomas and intraoperative vascular injuries were analyzed. RESULTS: A total of 61 patients had intraoperative vascular injuries, among which 76 vessels suffered injuries. Venous injuries (66/76, 86.84%) were more common than arterial injuries (10/76, 13.16%). Moreover, venous injuries frequently occurred in the inferior vena cava (32/66, 48.48%), renal veins (19/66, 28.79%), and iliac veins (8/66, 12.12%). All the injured arteries exhibited a total encasement on preoperative CT, and no injury occurred when the arteries were contacted or partially encased. In total, 87.88% (58/66) of injured veins were flattened with a visible lumen on preoperative CT, whereas only 12.12% (8/66) of the injured veins were flattened with an invisible lumen. CONCLUSION: Intraoperative injuries to veins occur more frequently than that to arteries in abdominal or pelvic neuroblastomas. Importantly, intraoperative injuries to veins may occur even if the veins are flattened with a visible lumen.