Background: Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. Aim: We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. Methods: We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis. Results: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. Conclusion: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

Background/aim: Young-onset hypertension is a form of condition diagnosed in patients aged below 40. Cytokines such as interleukin (IL)-6 and also MCP-1 may play a role in the development of arterial hypertension. Aortic stiffness can be detected by measuring pulse wave velocity (PWV). We aimed to explore the relationship between inflammation and aortic stiffness and investigate their roles in the etiology of young-onset hypertension. Materials and methods: We enrolled 16 patients diagnosed with young-onset hypertension and 16 volunteers without hypertension. The plasma levels of MCP-1 and IL-6 were determined using an enzyme-linked immunosorbent assay and quantitative enzyme-linked immunoassay, respectively. Carotid-femoral PWV was measured using an arteriograph device. Results: Compared with those in normotensive controls, the plasma levels of IL-6 and MCP-1 and the PWV values were significantly higher in patients with young-onset hypertension (P < 0.001). PWV values were also positively correlated with the levels of MCP-1 and IL-6. However, no statistically significant difference was noted in intima-media thickness between the two groups (P = 0.224). Conclusion: In this study, increased PWVs and the levels of inflammation markers were associated with aortic stiffness and inflammation in patients with young-onset hypertension, suggesting they have a role in the etiology of hypertension.

Background: Differentiating high‐grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context.Purpose: To differentiate high‐grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2‐FLAIR digital subtraction images and the peritumoral edema area.Study Type: Retrospective.Population: The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses).Field Strength/Sequence: Axial T2‐weighted fast spin‐echo sequence (T2WI) and T2‐weighted fluid‐attenuated inversion recovery sequence (T2‐FLAIR), using 1.5‐T and 3.0‐T scanners.Assessment: Radiomic features were extracted from digitally subtracted T2‐FLAIR images in the area of peritumoral edema. The maximum relevance‐minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP).Statistical Tests: Chi‐square test, one‐way analysis of variance, and Kruskal–Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC).Results: The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922.Data Conclusion: The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2‐FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG.Evidence Level: 3Technical Efficacy: Stage 2

Background: Obesity is associated with an increased risk of developing clear cell renal cell carcinoma (ccRCC), but paradoxically there is a positive association between obesity and surveillance.Purpose: To investigate the relationship between nucleus grade classification and body composition in patients with matched co-morbid conditions with non-metastatic ccRCC.Materials and Methods: A total of 253 patients with non-metastatic ccRCC were included in the study. Body composition was assessed with abdominal computed tomography (CT) using an automated artificial intelligence software. Both adipose and muscle tissue parameters of the patients were calculated. In order to investigate the net effect of body composition, propensity score matching (PSM) procedure was applied over age, sex, and T stage parameters. In this way, selection bias and imbalance between groups were minimized. Univariate and multivariate logistic regression analyses were performed to identify the association between body composition and WHO/ISUP grade (I–IV).Result: When the body composition of the patients was examined without matching the conditions, it was found that the subcutaneous adipose tissue (SAT) values were higher in patients with low grades ( P = 0.001). Normal attenuation muscle area (NAMA) was higher in high-grade patients than low-grade patients ( P < 0.05). In the post-matching evaluation, only SAT/NAMA was found to be associated with high-grade ccRCC (univariate analysis: odds ratio [OR]=0.899, 95% confidence interval [CI]=0.817−0.988, P = 0.028; multivariate analysis: OR=0.922, 95% CI=0.901−0.974, P = 0.042).Conclusion: CT-based body composition parameters can be used as a prognostic marker in predicting nuclear grade when age, sex, and T stage match conditions. This finding offers a new perspective on the obesity paradox.

Background/aim: Mesenteric panniculitis (MP) is an idiopathic benign disease characterized by fat necrosis, chronic inflammation, and fibrosis. The relationship between obesity and chronic low-grade inflammation has been reported. This study investigated the relationship of MP diagnosed using multidetector computed tomography (MDCT) with visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas. Materials and methods: We retrospectively enrolled 104 patients with no radiological findings other than MP. Additionally, 76 individuals without any indicative radiological findings were included as controls. VAT and SAT were separately calculated (cm(2)) using a 3-dimensional workstation. The abdominal circumference was measured (cm). Results: The mean abdominal circumference was 99.9 +/- 7.9 cm, SAT was 195.3 +/- 89.1 cm(2), and VAT was 203.9 +/- 72.8 cm(2) in the MP group. The abdominal circumference, VAT, and SAT were significantly higher in the MP group than in the control group (P < 0.001). According to the receiver operating characteristic (ROC) analysis, cut-off level VAT and SAT were 167.5 cm(2) (sensitivity 71%, specificity 69%) and 117.5 cm(2) (sensitivity 78%, specificity 51 %), respectively. Conclusion: Increased VAT and SAT were associated with MP, suggesting their role in the etiology of MP.

Objectives: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning. Methods: Dynamic contrast-enhanced pelvic MRI examinations of 471 lesions were retrospectively analysed and assessed by 3 radiologists according to O-RADS MRI criteria. Radiomic data were extracted from T2 and post-contrast fat-suppressed T1-weighted images. Using these data, an artificial neural network (ANN), support vector machine, random forest, and naive Bayes models were constructed. Results: Among all readers, the lowest agreement was found for the O-RADS 4 group (kappa: 0.669; 95% confidence interval [CI] 0.634-0.733), followed by the O-RADS 5 group (kappa: 0.709; 95% CI 0.678-0.754). O-RADS 4 predicted a malignancy with an area under the curve (AUC) value of 74.3% (95% CI 0.701-0.782), and O-RADS 5 with an AUC of 95.5% (95% CI 0.932-0.972) (P < .001). Among the machine learning models, ANN achieved the highest success, distinguishing O-RADS groups with an AUC of 0.948, a precision of 0.861, and a recall of 0.824. Conclusion: The interobserver agreement and diagnostic sensitivity of the O-RADS MRI in assigning O-RADS 4-5 were not perfect, indicating a need for structural improvement. Integrating artificial intelligence into MRI protocols may enhance their performance. Advances in knowledge: Machine learning can achieve high accuracy in the correct classification of O-RADS MRI. Malignancy prediction rates were 74% for O-RADS 4 and 95% for O-RADS 5.