Depression,arterial stiffness,cardiovascular risk,elastic modulus,intimamedia thickness

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes.

Aslı Kazgan Kılıçaslan, Burcu Sırlıer Emir, Sevler Yıldız, Gülhan Kılıçaslan, Osman Kurt. Clin Psychopharmacol Neurosci 2023;21:516-25. https://doi.org/10.9758/cpn.22.1009

Objective: Abdominal involvement of COVID-19 is a current issue. We aimed to evaluate hepatic and pancreatic density alterations on computed tomography (CT) and to analyze whether these alterations had a relationship with chest CT score and laboratory findings. Methods: Patients with reverse transcription-polymerase chain reaction-confirmed COVID-19 from March 11, 2020, to February 6, 2021, were retrospectively analyzed. Patients were divided into nonprogressive and progressive groups according to their chest CT scores. Liver and pancreas density, and liver-to-spleen (L/S) ratio were calculated. Laboratory findings, medication, intensive care unit stay, and survival were noted. Results: There were 51 patients in the nonprogressive group and 123 patients in the progressive group. The median (minimum to maximum) L/S value of the nonprogressive group was 1 (0.28–1.53) at admission and 1.06 (0.33–1.83) at follow-up (P < 0.001). In the progressive group, the median L/S value was 1.08 (0.35–1.51) at admission and 0.92 (0.33–1.75) at follow-up (P < 0.001). A significant difference was found between the 2 groups at admission and follow-up (P = 0.010 and P < 0.001, respectively). Pancreatic density measured at follow-up was significantly lower in the progressive group (P = 0.045). In the progressive group, aspartate aminotransferase, total bilirubin, creatinine, urea, C-reactive protein, D-dimer, and white blood cell values were higher; albumin and lymphocyte values were lower (P < 0.05). Conclusions: Patients with COVID-19 with progressive CT scores may have a decrease in L/S values, and their pancreatic density is lower than nonprogressives. Aspartate aminotransferase, total bilirubin, creatinine, urea, C-reactive protein, D-dimer, and white blood cell values tend to be higher in patients with a high chest CT score.