Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis.

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: 3 TECHNICAL EFFICACY: Stage 2.

A new finding for the obesity paradox? Evaluation of the relationship between muscle and adipose tissue in nuclear grade prediction in patients with clear cell renal cell carcinoma.

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.

Use of phase-contrast MRI to measure aortic stiffness in young-onset hypertension: a pilot study.

BACKGROUND: Young-onset hypertension is defined as hypertension diagnosed before the age of 40 years. Aortic pulse wave velocity is an indication of aortic stiffness. MRI assessment has been well verified compared to invasive pressure recordings for evaluating aortic pulse wave velocity. In this study, we aimed to determine whether aortic stiffness played a role in the aetiology of young-onset hypertension by calculating pulse wave velocity using MRI. METHODS: We enrolled 20 patients diagnosed with young-onset hypertension and 20 volunteers without hypertension. Aortic pulse wave velocity was measured by cardiac MRI and protocol for the pulse wave velocity measurement involved the use of a 1.5 T scanner to acquire velocity-encoded, phase-contrast transverse aortic cine images. Sagittal oblique images used to measure the distance (ΔX) between the ascending aorta and descending aorta for the calculation of pulse wave velocity. The aortic flow versus time curves of ascending aorta and descending aorta were automatically obtained from the phase-contrast MRI images. Using these curves, the temporal shift (ΔT) was measured by Segment Medviso. FINDINGS: The mean pulse wave velocity was 8.72 (SD 2.34) m/second (range: 7-12.8 m/second) for the patient group and 5.96 (standard deviation 1.86) m/second (range: 4.8-7.1 m/second) for the control group. The pulse wave velocity values were significantly higher in the patient group compared to the control group (p < 0.001). INTERPRETATION: Aortic stiffness may play a role in the aetiology of young-onset hypertension and serve as a non-invasive and reliable screening tool when measured by MRI.

Can automated CT body composition analysis predict high-grade Clavien-Dindo complications in patients with RCC undergoing partial and radical nephrectomy?

INTRODUCTION: This study investigated the relationship between body tissue composition analysis and complications according to the Clavien-Dindo classification in patients with renal cell carcinoma (RCC) who underwent partial (PN) or radical nephrectomies (RN). METHODS: We obtained all data of 210 patients with RCC from the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset and obtained radiological images from the cancer image archive. Body composition was assessed with automated artificial intelligence software using the convolutional network segmentation technique from abdominal computed tomography images. We included 125 PN and 63 RN in the study. The relationship between body fat and muscle tissue distribution and complications according to the Clavien-Dindo classification was evaluated between these two groups. RESULTS: Clavien-Dindo 3A and higher (high grade) complications were developed in 9 of 125 patients who underwent PN and 7 of 63 patients who underwent RN. There was no significant difference between all body composition values between patients with and without high-grade complications. CONCLUSION: This study showed that body muscle-fat tissue distribution did not affect patients with 3A and above complications according to the Clavien-Dindo classification in patients who underwent nephrectomy due to RCC.

Different chest CT scoring systems in patients with COVID-19: could baseline CT be a helpful tool in predicting survival in patients with matched ages and co-morbid conditions?

BACKGROUND: Computed tomography (CT) gives an idea about the prognosis in patients with COVID-19 lung infiltration. PURPOSE: To evaluate the success rates of various scoring methods utilized in order to predict survival periods, on the basis of the imaging findings of COVID-19. Another purpose, on the other hand, was to evaluate the agreements among the evaluating radiologists. MATERIAL AND METHODS: A total of 100 cases of known COVID-19 pneumonia, of which 50 were deceased and 50 were living, were included in the study. Pre-existing scoring systems, which were the Total Severity Score (TSS), Chest Computed Tomography Severity Score (CT-SS), and Total CT Score, were utilized, together with the Early Decision Severity Score (ED-SS), which was developed by our team, to evaluate the initial lung CT scans of the patients obtained at their initial admission to the hospital. The scans were evaluated retrospectively by two radiologists. Area under the curve (AUC) values were acquired for each scoring system, according to their performances in predicting survival times. RESULTS: The mean age of the patients was 61 ± 14.85 years (age range = 18-87 years). There was no difference in co-morbidities between the living and deceased patients. The survival predicted AUC values of ED-SS, CT-SS, TSS, and Total CT Score systems were 0.876, 0.823, 0.753, and 0.744, respectively. CONCLUSION: Algorithms based on lung infiltration patterns of COVID-19 may be utilized for both survival prediction and therapy planning.

Quantitative computed tomography analysis of emphysema severity and distribution in chronic obstructive lung disease, correlation with clinical findings.

INTRODUCTION: This study aimed to evaluate the correlation between different densitometric parameters on chest computed tomography (CT) and pulmonary function tests (PFT) of patients with emphysema-predominant chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: A retrospective evaluation was made of 56 patients with COPD. The lungs were segmented into nine sections. Normal and emphysematous parenchyma were measured using low attenuation area percentage (%LAA), percentile density (PD), and mean lung density (MLD) parameters. The effects of emphysema distribution on PFT were evaluated using %LAA, PD, MLD, kurtosis and skewness methods. RESULT: Fifty-six patients, all diagnosed with COPD, were evaluated. The %LAA-910 method showed significant correlation with forced expiratory volume in one second (FEV1) compared to other densitometric parameters (p= <0.001, r= -0.556). Other densitometric parameters differed between segments in terms of sensitivity for FEV1. It was determined that right lung mid-zone segment involvement affected FEV1 more than other segments when the %LAA -910 method was used (p= <0.001, r= -0.569). %LAA -950 had the largest area under the curve in ROC curve analysis and was determined to have 2.2% diagnostic property for predicting Global Initiative for Obstructive Lung Disease (GOLD) AB-CD staging (sensitivity 81%, specificity 74%). CONCLUSIONS: Quantitative analysis is an objective method for determining the distribution and severity of emphysema. There was a significant correlation between densitometry and PFT values. Quantitative analysis may be considered suitable for use in evaluating the severity of emphysema and predicting the clinical findings of patients.

Relationship of mesenteric panniculitis with visceral and subcutaneous adipose tissue

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 (cm2) 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 cm2, and VAT was 203.9 ± 72.8 cm2 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 cm2 (sensitivity 71%, specificity 69%) and 117.5 cm2 (sensitivity 78%, specificity 51 %), respectively. Conclusion: Increased VAT and SAT were associated with MP, suggesting their role in the etiology of MP.

Role of aortic stiffness and inflammation in the etiology of young-onset hypertension

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.

Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Scoring: Diagnostic Accuracy, Interobserver Agreement, and Applicability to Machine Learning.

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. MATERIAL AND METHODS: Dynamic contrast-enhanced pelvic MRI examinations 471 lesions were retrospectively analyzed and assessed by three 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 < 0.001). 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.

Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis.

OBJECTIVE: This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation. METHODS: Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters. RESULTS: Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters. CONCLUSION: This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.

Visual and Quantitative Assessment of COVID-19 Pneumonia on Chest CT: The Relationship with Disease Severity and Clinical Findings.

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.

Schwannomas of Uncommon Peripheral Locations: Analysis of Imaging Findings of 21 Cases.

BACKGROUND: Schwannomas are benign slow-growing tumors most often associated with the cranial nerves. Schwannomas often originate from the eighth cranial nerve. They may also originate from the peripheral nervous system of the neck and extremities. However extracranial peripheral schwannomas are considered a rare entity. OBJECTIVES: The knowledge of rare localizations and typical imaging findings will lead to a successfulradiological diagnosis. Therefore, in this study, we present the clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions. MATERIALS AND METHODS: The hospital database was screened for patients with an extracranial soft tissue mass. Twenty-one cases of schwannomas were found in rare localization. We analyzed the MR images of these patients retrospectively. The MR images were evaluated in terms of tumor location, signal intensity, and enhancement pattern. The histological examination of all the patients confirmed the diagnosis of schwannoma. RESULTS: In 21 patients, the schwannomas were peripheral, localized to upper (n = 6) and lower extremities (n = 11). The remaining four patients had intramuscular schwannomas. The patients diagnosed with intramuscular schwannomas had schwannomas in sternocleidomastoid, gastrocnemius, triceps muscle and lateral wall of the abdomen. The average long-axis diameter of the tumor was 27.7 mm and the average short-axis diameter was 16.4 mm. The contrast pattern was diffused in eight tumors and peripheral in 13. CONCLUSION: In this study, we present clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions.

Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis

SUMMARY OBJECTIVE: This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation. METHODS: Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters. RESULTS: Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters. CONCLUSION: This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.