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Background: Lymphoma is a common malignant tumor in children. The pathologic subtyping of lymphoma is high complex, and the treatment options vary. The different pathologic subtypes of lymphomas have no significant differences on computed tomography (CT) images. As it is a hematologic disease, patients with lymphoma often show abnormalities in the spleen, and so the aim of this study was to construct a model for differentiating Burkitt lymphoma (BL) from lymphoblastic lymphoma through the extraction of radiomic features of the spleen from CT images. This could provide an efficient, noninvasive method that can differentiate the common pathological subtypes in patients with pediatric lymphoma. Methods: The clinical data and imaging data of 48 patients with lymphoblastic lymphoma and 61 patients with BL were retrospectively analyzed. The dataset was divided into a training set (n=76) and a test set (n=33) through complete randomization. Radiomics features of the spleen were separately extracted from CT images in the noncontrast enhanced, arterial, and venous phases. These phase-specific features were integrated to construct fusion models. Three classifiers, quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM), were employed to build the models. Results: The fusion model exhibited superior performance compared to individual models. There was no significant difference between the fusion models constructed by QDA and LR in either the training set or the test set. Among the four fusion models constructed with the SVM classifier, SVM_4 emerged as the best performing model. The area under the curve, sensitivity, specificity, and F1-score of the SVM_4 model were 0.967 [95% confidence interval (CI): 0.935-0.998], 0.86, 0.97, and 0.913 in the training set, respectively, and 0.754 (95% CI: 0.584-0.924), 0.611, 0.867, and 0.71 in the test set, respectively. Conclusions: The radiomics features of the spleen demonstrated the capability to distinguish between the two most common lymphoma subtypes in pediatric patients. This noninvasive approach holds promise for efficient and accurate discrimination.
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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.
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Neoplasias Abdominais , Neuroblastoma , Tomografia Computadorizada por Raios X , Humanos , Neuroblastoma/diagnóstico por imagem , Neuroblastoma/tratamento farmacológico , Neuroblastoma/cirurgia , Neuroblastoma/patologia , Masculino , Feminino , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Pré-Escolar , Criança , Lactente , Neoplasias Abdominais/diagnóstico por imagem , Neoplasias Abdominais/tratamento farmacológico , Neoplasias Abdominais/patologia , Neoplasias Abdominais/cirurgia , Resultado do Tratamento , Curva ROC , Valor Preditivo dos Testes , Adolescente , Carga Tumoral/efeitos dos fármacos , Sensibilidade e Especificidade , Valores de Referência , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Reprodutibilidade dos TestesRESUMO
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.
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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.
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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.
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Meios de Contraste , Imageamento por Ressonância Magnética , Neuroblastoma , Tomografia Computadorizada por Raios X , Humanos , Imageamento por Ressonância Magnética/métodos , Masculino , Feminino , Estudos Retrospectivos , Neuroblastoma/diagnóstico por imagem , Neuroblastoma/patologia , Tomografia Computadorizada por Raios X/métodos , Pré-Escolar , Lactente , Criança , Adolescente , Ganglioneuroblastoma/diagnóstico por imagem , Ganglioneuroblastoma/patologia , RadiômicaRESUMO
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.
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Agamaglobulinemia , Cromossomos Humanos Par 19 , Fluoxetina , Dissomia Uniparental , Humanos , Fluoxetina/uso terapêutico , Cromossomos Humanos Par 19/genética , Agamaglobulinemia/genética , Agamaglobulinemia/tratamento farmacológico , Antígenos CD79/genética , Masculino , Infecções por Enterovirus/tratamento farmacológico , Infecções por Enterovirus/genética , Mutação/genética , Imunoglobulinas Intravenosas/uso terapêutico , FemininoRESUMO
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.
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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.
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Hepatoblastoma , Neoplasias Hepáticas , Terapia Neoadjuvante , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Masculino , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Quimioterapia Adjuvante/métodos , Meios de Contraste , Hepatoblastoma/tratamento farmacológico , Hepatoblastoma/diagnóstico por imagem , Hepatoblastoma/patologia , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/patologia , Terapia Neoadjuvante/métodos , Radiômica , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodosRESUMO
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.
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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.
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Amplificação de Genes , Imageamento por Ressonância Magnética , Proteína Proto-Oncogênica N-Myc , Neuroblastoma , Tomografia Computadorizada por Raios X , Humanos , Neuroblastoma/genética , Neuroblastoma/diagnóstico por imagem , Masculino , Feminino , Estudos Retrospectivos , Proteína Proto-Oncogênica N-Myc/genética , Fatores de Risco , Pré-Escolar , Lactente , Imageamento por Ressonância Magnética/métodos , Criança , Tomografia Computadorizada por Raios X/métodos , Meios de Contraste , Estadiamento de Neoplasias , Taxa de SobrevidaRESUMO
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.
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Autoanticorpos , Interferon gama , Micoses , Talaromyces , Células Th1 , Humanos , Talaromyces/imunologia , Células Th1/imunologia , Interferon gama/imunologia , Autoanticorpos/imunologia , Autoanticorpos/sangue , Masculino , Adulto , Feminino , China , Micoses/imunologia , Micoses/microbiologia , Pessoa de Meia-Idade , Proteínas com Domínio T/genética , Proteínas com Domínio T/imunologia , Fator de Transcrição STAT1/imunologia , Fator de Transcrição STAT1/genéticaRESUMO
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.
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Ferroptose , Neoplasias , Humanos , Apoptose , Catálise , Corantes , Febre , Microambiente Tumoral , Neoplasias/terapia , Peróxido de HidrogênioRESUMO
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.
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Cardiomiopatia Hipertrófica , Insuficiência Cardíaca , Masculino , Humanos , Pessoa de Meia-Idade , Feminino , Radiômica , Estudos Retrospectivos , Cardiomiopatia Hipertrófica/diagnóstico por imagem , Insuficiência Cardíaca/diagnóstico , Imageamento por Ressonância MagnéticaRESUMO
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.
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Cistos , Neoplasias Renais , Sarcoma de Células Claras , Tumor de Wilms , Humanos , Criança , Radiômica , Estudos Retrospectivos , Sarcoma de Células Claras/diagnóstico por imagem , Neoplasias Renais/diagnóstico por imagem , Rim , Tomografia Computadorizada por Raios XRESUMO
Background: Children with primary immunodeficiency diseases (PIDs) are particularly vulnerable to infection of Mycobacterium tuberculosis (Mtb). Chest computed tomography (CT) is an important examination diagnosing pulmonary tuberculosis (PTB), and there are some differences between primary immunocompromised and immunocompetent cases with PTB. Therefore, this study aimed to use the radiomics analysis based on un-enhanced CT for identifying immunodeficiency status in children with PTB. Methods: This retrospective study enrolled a total of 173 patients with diagnosis of PTB and available immunodeficiency status. Based on their immunodeficiency status, the patients were divided into PIDs (n=72) and no-PIDs (n=101). The samplings were randomly divided into training and testing groups according to a ratio of 3:1. Regions of interest were obtained by segmenting lung lesions on un-enhanced CT images to extract radiomics features. The optimal radiomics features were identified after dimensionality reduction in the training group, and a logistic regression algorithm was used to establish radiomics model. The model was validated in the training and testing groups. Diagnostic efficiency of the model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, calibration curve, and decision curve. Results: The radiomics model was constructed using nine optimal features. In the training set, the model achieved an AUC of 0.837, sensitivity of 0.783, specificity of 0.780, and F1 score of 0.749. The cross-validation of the model in the training set showed an AUC of 0.774, sensitivity of 0.834, specificity of 0.720, and F1 score of 0.749. In the test set, the model achieved an AUC of 0.746, sensitivity of 0.722, specificity of 0.692, and F1 score of 0.823. Calibration curves indicated a strong predictive performance by the model, and decision curve analysis demonstrated its clinical utility. Conclusions: The CT-based radiomics model demonstrates good discriminative efficacy in identifying the presence of PIDs in children with PTB, and shows promise in accurately identifying the immunodeficiency status in this population.