Background: The differentiation of surgical from nonsurgical adult intussusception may enable the appropriate selection of management strategies. Objective: This study aimed to investigate the diagnostic potential of multidetector computed tomography (MCDT) features to differentiate surgical from nonsurgical adult intussusception and develop a diagnostic model. Methods: A retrospective study was performed on 96 patients with intussusceptions at the University Medical Center Hospital between January 2014 and January 2020. Two radiologists reviewed all images, and intussusception characteristics were documented. The location of intussusception, length, diameter, interposed fat thickness, lead point, and complications were evaluated. Based on the results, a diagnostic tree model was developed to differentiate between surgical and nonsurgical adult intussusception. Results: A total of 99 intussusceptions in 96 patients (mean age: 53.0 ± 16.5 years), including 35 (35.3%) enteroenteric, 27 (27.3%) enterocolic, and 37 (37.4%) colocolic lesions, were evaluated. Of the enteroenteric intussusceptions, 22 (62.9%) were surgical, including 19 (79.2%) with lead points. Among colon intussusceptions, 63 (98.4%) were surgical, and 100% had lead points. The characteristics used to predict surgical intussusceptions included lead point presence, length ≥ 5.0 cm, diameter ≥ 3.2 cm, interposed fat thickness ≥ 0.5 cm, and complications (p < 0.001). Based on these features, we established a diagnostic tree model that correctly classified 96 (97%) of 99 lesions. Conclusion: Our study reinforces the importance of MDCT for the diagnosis and guided management of adult intussusceptions. The characteristics that predicted surgical intussusceptions included lead points, length, diameter, interposed fat thickness, and complications. A systematic approach using this diagnostic tree model could be used to distinguish surgical and nonsurgical adult intussusception.

Objectives This study aimed to assess the role of chest X-ray (CXR) scoring methods and their correlations with the clinical severity categories and the Quick COVID-19 Severity Index (qCSI). Methods We conducted a retrospective study of 159 COVID-19 patients who were diagnosed and treated at the University Medical Center between July and September 2021. Chest X-ray findings were evaluated, and severity scores were calculated using the modified CXR (mCXR), Radiographic Assessment of Lung Edema (RALE), and Brixia scoring systems. The three scores were then compared to the clinical severity categories and the qCSI using Spearman's correlation coefficient. Results Overall, 159 patients (63 males and 96 females) (mean age: 58.3 ± 15.7 years) were included. The correlation coefficients between the mCXR score and the Brixia and RALE scores were 0.9438 and 0.9450, respectively. The correlation coefficient between the RALE and Brixia scores was marginally higher, at 0.9625. The correlation coefficients between the qCSI and the Brixia, RALE, and mCXR scores were 0.7298, 0.7408, and 0.7156, respectively. The significant difference in the mean values of the three CXR scores between asymptomatic, mild, moderate, severe, and critical groups was also noted. Conclusions There were strong correlations between the three CXR scores and the clinical severity classification and the qCSI.

IntroductionHepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Early detection and accurate diagnosis of HCC play an important role in patient management. This study aimed to develop a convolutional neural network-based model to identify and segment HCC lesions utilizing dynamic contrast agent-enhanced computed tomography (CT). MethodsThis retrospective study used CT image sets of histopathology-confirmed hepatocellular carcinoma over three phases (arterial, venous, and delayed). The proposed convolutional neural network (CNN) segmentation method was based on the U-Net architecture and trained using the domain adaptation technique. The proposed method was evaluated using 115 liver masses of 110 patients (87 men and 23 women; mean age, 56.9 years ± 11.9 (SD); mean mass size, 6.0 cm ± 3.6). The sensitivity for identifying HCC of the model and Dice score for segmentation of liver masses between radiologists and the CNN model were calculated for the test set. ResultsThe sensitivity for HCC identification of the model was 100%. The median Dice score for HCC segmenting between radiologists and the CNN model was 0.81 for the test set. ConclusionDeep learning with CNN had high performance in the identification and segmentation of HCC on dynamic CT.