Impact of pre-treatment extracellular volume fraction measured by computed tomography on response of primary lesion to preoperative chemotherapy in abdominal neuroblastoma.

OBJECTIVES: To retrospectively investigate the impact of pre-treatment Extracellular Volume Fraction (ECV) measured by Computed Tomography (CT) on the response of primary lesions to preoperative chemotherapy in abdominal neuroblastoma. METHODS: A total of seventy-five patients with abdominal neuroblastoma were retrospectively included in the study. The regions of interest for the primary lesion and aorta were determined on unenhanced and equilibrium phase CT images before treatment, and their average CT values were measured. Based on patient hematocrit and average CT values, the ECV was calculated. The correlation between ECV and the reduction in primary lesion volume was examined. A receiver operating characteristic curve was generated to assess the predictive performance of ECV for a very good partial response of the primary lesion. RESULTS: There was a negative correlation between primary lesion volume reduction and ECV (r = -0.351, p = 0.002), and primary lesions with very good partial response had lower ECV (p < 0.001). The area under the curve for ECV in predicting the very good partial response of primary lesion was 0.742 (p < 0.001), with a 95 % Confidence Interval of 0.628 to 0.836. The optimal cut-off value was 0.28, and the sensitivity and specificity were 62.07 % and 84.78 %, respectively. CONCLUSIONS: The measurement of pre-treatment ECV on CT images demonstrates a significant correlation with the response of the primary lesion to preoperative chemotherapy in abdominal neuroblastoma.

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.

A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.

PURPOSE: To compare the performance of radiomics from contrast-enhanced computed tomography (CECT) and non-contrast magnetic resonance imaging (MRI) in assessing cellular behavior in pediatric peripheral neuroblastic tumors (PNTs). MATERIALS AND METHODS: A retrospective analysis of 81 PNT patients who underwent venous phase CECT, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI) scans was conducted. The patients were classified into neuroblastoma and ganglioneuroblastoma/ganglioneuroma based on their pathological subtypes. Additionally, they were categorized into favorable histology and unfavorable histology according to the International Neuroblastoma Pathology Classification (INPC). Tumor regions of interest were segmented on CECT, axial T1WI, and axial T2WI images, and radiomics models were developed based on the selected radiomics features. Following five-fold cross-validation, the performance of the radiomics models derived from CECT and MRI was compared using the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: For discriminating pathological subtypes, the AUC for CECT radiomics models ranged from 0.765 to 0.870, with an accuracy range of 0.728 to 0.815. In contrast, the AUC for MRI radiomics models ranged from 0.549 to 0.748, with an accuracy range of 0.531 to 0.778. Regarding the discrimination of INPC subgroups, the AUC for CECT radiomics models ranged from 0.503 to 0.759, with an accuracy range of 0.432 to 0.741. Meanwhile, the AUC for MRI radiomics models ranged from 0.512 to 0.739, with an accuracy range of 0.605 to 0.815. CONCLUSIONS: CECT radiomics outperforms non-contrast MRI radiomics in evaluating pathological subtypes. When assessing INPC subgroups, CECT radiomics demonstrates comparability with non-contrast MRI radiomics.

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.

Diagnostic Value of Multiparameter MRI-Based Radiomics in Pediatric Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorders.

BACKGROUND AND PURPOSE: Myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) have a higher prevalence among children. For children undergoing the initial manifestation of MOGAD, prompt diagnosis has paramount importance. This study assessed the performance of multiparameter MRI-based radiomics in distinguishing patients with and without MOGAD with idiopathic inflammatory demyelinating diseases. MATERIALS AND METHODS: We enrolled a cohort of 121 patients diagnosed with idiopathic inflammatory demyelinating diseases, including 68 children with MOGAD and 53 children without MOGAD. Radiomics models (T1WI, T2WI, FLAIR, and compound model) using features extracted from demyelinating lesions within the brain parenchyma were developed in the training set. The performance of these models underwent validation within the internal testing set. Additionally, we gathered clinical factors and MRI features of brain parenchymal lesions at their initial presentation. Subsequently, these variables were used in the construction of a clinical prediction model through multivariate logistic regression analysis. RESULTS: The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the training set were 0.781 (95% CI, 0.689-0.864), 0.959 (95% CI, 0.924-0.987), 0.939 (95% CI, 0.898-0.979), and 0.989 (95% CI, 0.976-0.999), respectively. The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the testing set were 0.500 (95% CI, 0.304-0.652), 0.833 (95% CI, 0.697-0.944), 0.804 (95% CI, 0.664-0.918), and 0.905 (95% CI, 0.803-0.979), respectively. The areas under the curve of the clinical prediction model in the training set and testing set were 0.700 and 0.289, respectively. CONCLUSIONS: Multiparameter MRI-based radiomics helps distinguish MOGAD from non-MOGAD in patients with idiopathic inflammatory demyelinating diseases.

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.

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.

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.