Search

Abstract The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman’s correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model’s discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer–Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Abstract Objective To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. Materials and methods A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. Results The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. Conclusion The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

Abstract To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

PurposeThis study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.Materials and methodsIn a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p

Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.

ObjectiveTo retrospectively investigate the value of various MRI image menifestations in the hepatobiliary phase (HBP), DWI and T2WI sequences in predicting the pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC).Materials and MethodsForty-three patients of IMCCs confirmed by pathology were enrolled including 25 cases in well- or moderately-differentiated group and 18 cases in poorly-differentiated group. All patients underwent DWI, T2WI and HBP scan. The Chi square test was used to compare the differences in the general information. Logistic regression analysis was used to analyze the risk factors in predicting the pathological grade of IMCCs.ResultsThe maximal diameter of the IMCC lesion was < 3 cm in 11 patients, between 3 cm and 6 cm in 15, and > 6 cm in 17. Sixteen cases had intrahepatic metastasis, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. Seventeen (39.5%) patients presented with target signs in the DWI sequence, including 9 in the well- or moderately-differentiated group and 8 in the poorly-differentiated group. Twenty (46.5%) patients presented with target signs in the T2WI sequence, including 8 in the well- or moderately-differentiated group and 12 in the poorly-differentiated group. Nineteen cases (54.3%) had a complete hypointense signal ring, including 13 in the well- or moderately-differentiated group and 6 in the poorly-differentiated group. Sixteen (45.7%) cases had an incomplete hypointense signal ring, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. The lesion size, intrahepatic metastasis, T2WI signal, and integrity of a hypointense signal ring in HBP were statistically significantly different between two gourps. T2WI signal, presence or non-presence of intrahepatic metastasis, and integrity of hypointense signal ring were the independent influencing factors for pathological grade of IMCC.ConclusionTarget sign in T2WI sequence, presence of intrahepatic metastasis and an incomplete hypointense-signal ring in HBP are more likely to be present in poorly-differentiated IMCCs.

Abstract Background Pediatric COVID-19 is relatively mild and may vary from that in adults. This study was to investigate the epidemic, clinical, and imaging features of pediatric COVID-19 pneumonia for early diagnosis and treatment. Methods Forty-one children infected with COVID-19 were analyzed in the epidemic, clinical and imaging data. Results Among 30 children with mild COVID-19, seven had no symptoms, fifteen had low or mediate fever, and eight presented with cough, nasal congestion, diarrhea, headache, or fatigue. Among eleven children with moderate COVID-19, nine presented with low or mediate fever, accompanied with cough and runny nose, and two had no symptoms. Significantly (P

ObjectiveTo investigate the value of diffusion-weighted imaging (DWI) combined with the hepatobiliary phase (HBP) Gd-BOPTA enhancement in differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from atypical liver abscess.Materials and MethodsA retrospective analysis was performed on 43 patients with IMCCs (IMCC group) and 25 patients with atypical liver abscesses (liver abscess group). The DWI signal, the absolute value of the contrast noise ratio (│CNR│) at the HBP, and visibility were analyzed.ResultsA relatively high DWI signal and a relatively high peripheral signal were presented in 29 patients (67.5%) in the IMCC group, and a relatively high DWI signal was displayed in 15 patients (60.0%) in the atypical abscess group with a relatively high peripheral signal in only one (6.7%) patient and a relatively high central signal in 14 (93.3%, 14/15). A significant (P

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Objectives. The clinical and imaging features of asymptomatic carriers of severe acute respiratory syndrome coronavirus 2 and symptomatic COVID-19 patients. Methods. The clinical and chest computed tomography imaging data of 47 asymptomatic carriers and 36 symptomatic COVID-19 patients were derived. All patients underwent 4–6 CT scans over a period of 2–5 days. Results. The bulk of asymptomatic carriers who developed symptoms and most of the COVID-19 patients were older than 18 years of age with a decreased lymphocyte count, abnormal hepatic and renal function, and increased D-dimer and C-reactive protein. In the early stage, the pulmonary lesion involved mostly 1–2 lobes at the peripheral area in asymptomatic carriers but more than three lobes at both the central and peripheral areas in COVID-19 patients. In the progression stage, the lesion of asymptomatic carriers extended from the peripheral to the central area, and no significant difference was found in the lesion range compared with the symptomatic control group. In early improvement stage, the lesion was rapidly absorbed, and lesions were located primarily at the peripheral area in asymptomatic carriers; contrastingly, lesions were primarily located at both the central and peripheral areas in symptomatic patients. Asymptomatic carriers reflected a significantly shorter duration from disease onset to peak progression stage compared with the symptomatic. Conclusions. Asymptomatic carriers are a potential source of transmission and may become symptomatic COVID-19 patients despite indicating less severe pulmonary damage, earlier improvement, and better prognosis. Early isolation and intervention can eliminate such carriers as potential sources of transmission and improve their prognosis.

Abstract Background This study aims to investigate the application value of three-dimensional arterial spin labeling (3DASL) in investigating cerebral blood flow dynamics in full-term neonates. Methods A total of 60 full-term neonates without known intracranial pathology were recruited for 3DASL examination. These neonates were divided into three groups: 1–3 day group, 4–7 day group, and 8–15 day group. On the cerebral blood flow (CBF) images, regions of interest (ROI) were selected from the frontal white matter, parietal white matter, basal ganglia, corona radiata, thalamus and brainstem, and the CBF values of each ROI were recorded. The CBF values of ROIs at bilaterally symmetric locations, the values of each ROI between males and females, and the values of each ROI among these three different age groups were compared. Results The difference in CBF values of the frontal white matter, parietal white matter, basal ganglia, corona radiata and thalamus at the bilateral symmetric positions were not statistically significant. There was no statistical difference in the CBF values of each brain region between the male and female groups. The CBF values at the basal ganglia region, corona radiata and parietal white matter were higher in the 8–15 day group, when compared to the 1–3 day and 4–7 day groups (P

PurposeThis study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.Materials and MethodsCT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.ResultsA total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.ConclusionRadiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.

LncRNA TUG1 has not yet been reported in cerebral ischemia/reperfusion (I/R) injury. Methylcytosine dioxygenase TET2 is involved in ischemic damage. This study aimed to investigate the effects of TUG1 demethylated by TET2 on I/R-induced inflammatory response and identified its possible mechanisms.We found that TUG1 expression was significantly upregulated in oxygen-glucose deprivation and reoxygenation (OGD/R)-induced SH-SY5Y and SK-N-SH cells. Using the middle cerebral artery occlusion (MCAO) mice, we observed a similar effect. We also found that I/R injury could downregulate miR-200a-3p and upregulate NLRP3 and TET2. The knockdown of TUG1 could alleviate OGD/R-induced inflammatory response through upregulating miR-200a-3p and downregulating NLRP3 and other pro-inflammatory molecules. miR-200a-3p inhibition can partially reverse the effects of TUG1 silencing. Further experiments confirmed that TUG1 sponged miR-200a-3p to diminish miR-200a-3p and promote NLRP3 dependent inflammatory responses. Mechanically, knockdown of TET2 induced low levels of TUG1 and high levels of miR-200a-3p in both SK-N-SH and SH-SY5Y cells. IL-18, IL-1β, NLRP3, Caspase-1, and GSDMD-N were highly downregulated in OGD/R-induced SK-N-SH and SH-SY5Y cells after TET2 knockdown. TUG1 overexpression could reverse this effect. All the data indicated that TET2 could demethylate TUG1 and contribute to the inflammatory response. In additional experiments using the MCAO mice model, we confirmed knockdown of TET2 attenuated I/R-induced inflammatory response and brain injuries via decreasing TUG1 and increasing miR-200a-3p to inhibit NLRP3 expression. The demethylation of TUG1 by TET2 might aggravate I/R-induced inflammatory injury via modulating NLRP3 by miR-200a-3p. Our data confirmed that TET2 contributed to I/R-induced inflammatory response via the demethylation of TUG1 and regulated TUG1/miR-200a-3p/NLRP3 pathway.

Background/Aims: Unlike other organs, which only have one set of capillary network, the renal microvasculature consists of two sets of capillary network series connected by efferent arterioles. Angiotensin II constricts the efferent glomerular artery. Hence, renal tumor blood flow (BF) distribution may be different from tumors in other organs. This study aims to investigate the effects of angiotensin II on the hemodynamics of intrarenal VX2 tumors using perfusion computed tomography(CT). Methods: Twenty-four male New Zealand white rabbits were randomly divided into three groups: groups A (blank controls), group B (negative controls), and group C (angiotensin II-treated animals). Group B and C were established to the model of intrarenal VX2 tumors. Furthermore, perfusion CT of the kidney was performed in each group. Prior to perfusion CT scan in group C, the mean arterial blood was elevated to 150-160 mmHg by angiotensin II. The BF, blood volume (BV), mean transit time (MTT), capillary permeability-surface area product (PS), and relative permeability-surface area product (RPS) of tumors and renal tissues were calculated. Results: Compared with normal renal cortex tissues in group A, the BF, BV and PS values of tumors in group B were significantly lower, MTT was prolonged and RPS increased. Compared with group B, only the RPS of these tumors increased from 83.23 ± 29.17% to 120.94 ± 31.84% by angiotensin II infusion. Angiotensin II significantly increased the RPS value of the renal cortex distant from the tumor (CDT) and the right renal cortex (RRC). Conclusions: Perfusion CT can accurately observe the influence of angiotensin II on normal and tumor BF in kidneys. This clarifies the effect of angiotensin II on intrarenal tumor hemodynamics.