Evaluation of the frequency of left renal vein variations in computed tomography and its relationship with cancer development

Background: Left renal vein (LRV) variations occur in 0.8–10.2% of the population. The most common LRV variations are retroaortic left renal vein (RLRV) and circumaortic left renal vein (CLRV). The purpose of this study is to determine the frequency of LRV variations in a large series on computed tomography (CT) and to investigate the association between LRV and malignancy development.Materials and methods: Between January 2015 and January 2017, an abdominal CT examination of 12,341 (5505 female, 6836 male) patients was evaluated retrospectively in this study. Patients’ clinical and demographic data were recorded using the Hospital Information System.Results: Left renal vein variations were detected in 314 (2.54%) of the 12,341 patients within the study. Of the 314 cases found to have LRV variations, 227 (1.84%) had RLRV, and 87 (0.70%) had CLRV. There was no statistical difference in total LRV variations (p = 0.083) and CLRV variation (p = 0.96) groups in terms of gender. However, the RLRV variation was found to be 1.32 times higher in males than in females (p = 0.039). Of the 314 patients with LRV variations, 73 (23.2%) had any sort of concomitant malignancy.Conclusions: A high incidence of malignancy was detected in patients with LRV variations. Of the LRV variations, RLRV variation is more common than CLRV variation. The presence of total LRV variations and CLRV variations is not associated with gender; whereas the presence of RLRV variation is more common in males

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine