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.

Chest Computed Tomography Radiomics for Determining Macrolide Resistance-Associated Gene Mutation Status in Children with Mycoplasma pneumoniae Pneumonia: A Two-Center Study.

RATIONALE AND OBJECTIVES: The mutations in the 23S ribosomal RNA (rRNA) gene are associated with an increase in resistance to macrolides in children with Mycoplasma pneumoniae pneumonia (MPP). This study aimed to develop and validate a chest computed tomography (CT) radiomics model for determining macrolide resistance-associated gene mutation status in MPP. MATERIALS AND METHODS: A total of 258 MPP patients were retrospectively included from two institutions (training set: 194 patients from the first institution; external test set: 64 patients from the second). The 23S rRNA gene mutation status was tested by nasopharyngeal swab polymerase chain reaction. Radiomics features were extracted from chest CT images of pulmonary lesions segmented with semi-automatic delineation. Subsequently, radiomics feature reduction was applied to identify the most relevant features. Logistic regression and random forest algorithms were employed to establish the radiomics models, which were five-fold cross-validated in the training set and validated in the external test set. RESULTS: The radiomics feature selection resulted in eight features. After five-fold cross-validation in the training set, the mean areas under the receiver operating characteristic curve (AUCs) of the logistic regression and random forest models were 0.868 (95% confidence interval (CI): 0.813-0.923) and 0.941 (95% CI: 0.907-0.975), respectively. In the external test set, the corresponding AUCs were 0.855 (95% CI: 0.758-0.952) and 0.815 (95% CI: 0.705-0.925). CONCLUSION: Chest CT radiomics is a promising diagnostic tool for determining macrolide resistance gene mutation status in MPP. AVAILABILITY OF DATA AND MATERIAL: The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

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.

Th1 cell immune response in Talaromyces marneffei infection with anti-interferon-γ autoantibody syndrome.

Anti-interferon-γ autoantibody (AIGA) syndrome may be the basis of disseminated Talaromyces marneffei infection in human immunodeficiency virus (HIV)-negative adults. However, the pathogenesis of Th1 cell immunity in T. marneffei infection with AIGA syndrome is unknown. A multicenter study of HIV-negative individuals with T. marneffei infection was conducted between September 2018 and September 2020 in Guangdong and Guangxi, China. Patients were divided into AIGA-positive (AP) and AIGA-negative (AN) groups according to the AIGA titer and neutralizing activity. The relationship between AIGA syndrome and Th1 immune deficiency was investigated by using AP patient serum and purification of AIGA. Fifty-five HIV-negative adults with disseminated T. marneffei infection who were otherwise healthy were included. The prevalence of AIGA positivity was 83.6%. Based on their AIGA status, 46 and 9 patients were assigned to the AP and AN groups, respectively. The levels of Th1 cells, IFN-γ, and T-bet were higher in T. marneffei-infected patients than in healthy controls. However, the levels of CD4+ T-cell STAT-1 phosphorylation (pSTAT1) and Th1 cells were lower in the AP group than in the AN group. Both the serum of patients with AIGA syndrome and the AIGA purified from the serum of patients with AIGA syndrome could reduce CD4+ T-cell pSTAT1, Th1 cell differentiation and T-bet mRNA, and protein expression. The Th1 cell immune response plays a pivotal role in defense against T. marneffei infection in HIV-negative patients. Inhibition of the Th1 cell immune response may be an important pathological effect of AIGA syndrome.IMPORTANCEThe pathogenesis of Th1 cell immunity in Talaromyces marneffei infection with anti-interferon-γ autoantibody (AIGA) syndrome is unknown. This is an interesting study addressing an important knowledge gap regarding the pathogenesis of T. marneffei in non-HIV positive patients; in particular patients with AIGA. The finding of the Th1 cell immune response plays a pivotal role in defense against T. marneffei infection in HIV-negative patients, and inhibition of the Th1 cell immune response may be an important pathological effect of AIGA syndrome, which presented in this research could help bridge the current knowledge gap.

Discrimination models with radiomics features derived from cardiovascular magnetic resonance images for distinguishing hypertensive heart disease from hypertrophic cardiomyopathy.

Background: Discriminating hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) is challenging, because both are characterized by left ventricular hypertrophy (LVH). Radiomics might be effective to differentiate HHD from HCM. Therefore, this study aimed to investigate discriminators and build discrimination models between HHD and HCM using multiparametric cardiac magnetic resonance (CMR) findings and radiomics score (radscore) derived from late gadolinium enhancement (LGE) and cine images. Methods: In this single center, retrospective study, 421 HCM patients [median and interquartile range (IQR), 50.0 (38.0-59.0) years; male, 70.5%] from January 2017 to September 2021 and 200 HHD patients [median and IQR, 44.5 (35.0-57.0) years; male, 88.5%] from September 2015 to July 2022 were consecutively included and randomly stratified into a training group and a validation group at a ratio of 6:4. Multiparametric CMR findings were obtained using cvi42 software and radiomics features using Python software. After dimensional reduction, the radscore was calculated by summing the remaining radiomics features weighted by their coefficients. Multiparametric CMR findings and radscore that were statistically significant in univariate logistic regression were used to build combined discrimination models via multivariate logistic regression. Results: After multivariate logistic regression, the maximal left ventricular end diastolic wall thickness (LVEDWT), left ventricular ejection fraction (LVEF), presence of LGE, cine radscore and LGE radscore were identified as significant characteristics and used to build a combined discrimination model. This model achieved an area under the receiver operator characteristic curve (AUC) of 0.979 (0.968-0.990) in the training group and 0.981 (0.967-0.995) in the validation group, significantly better than the model using multiparametric CMR findings alone (P<0.001). Conclusions: Radiomics features derived from cardiac cine and LGE images can effectively discriminate HHD from HCM.

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.

Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure.

Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.

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.

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.

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.

Using computed tomography angiography and computational fluid dynamics to study aortic coarctation in different arch morphologies.

Objective: To study the differences in computed tomography angiography (CTA) imaging of gothic arches, crenel arches, and romanesque arches in children with Aortic Coarctation (CoA), and to apply computational fluid dynamics (CFD) to study hemodynamic changes in CoA children with gothic arch aorta. Methods: The case data and CTA data of children diagnosed with CoA (95 cases) in our hospital were retrospectively collected, and the morphology of the aortic arch in the children was defined as gothic arch (n = 27), crenel arch (n = 25) and romanesque arch (n = 43). The three groups were compared with D1/AOA, D2/AOA, D3/AOA, D4/AOA, D5/AOA, and AAO-DAO angle, TAO-DAO angle, and aortic arch height to width ratio (A/T). Computational fluid dynamics was applied to assess hemodynamic changes in children with gothic arches. Results: There were no significant differences between D1/AOA and D2/AOA among gothic arch, crenel arch, and romanesque arch (P > 0.05). The differences in D3/AOA, D4/AOA, and D5/AOA among the three groups were statistically significant (P < 0.05), D4/AOA, D5/AOA of the gothic arch group were smaller than the crenel arch group, and the D3/AOA and D5/AOA of the gothic arch group were smaller than the romanesque arch group (P < 0.05). The difference in AAO-DAO angle among the three groups was statistically significant (P < 0.05), and the AAO-DAO angle of gothic arch was smaller than that of romanesque arch and crenel arch group (P < 0.05). There was no significant difference in the TAO-DAO angle between the three groups (P > 0.05). The difference in A/T values among the three groups was statistically significant (P < 0.05), and the A/T values: gothic arch > romanesque arch > crenel arch (P < 0.05). The CFD calculation of children with gothic arch showed that the pressure drop between the distal stenosis and the descending aorta was 58 mmHg, and the flow rate at the isthmus and descending aorta was high and turbulent. Conclusion: Gothic aortic arch is common in CoA, it may put adverse effects on the development of the aortic isthmus and descending aorta, and its A/T value and AAO-DAO angle are high. CFD could assess hemodynamic changes in CoA.

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.