Evaluation of the olfactory bulb volume and morphology in patients with coronavirus disease 2019: can differences create predisposition to anosmia?

OBJECTIVE: This study aimed to investigate whether the volume and morphology of the olfactory bulb are effective in the occurrence of anosmia in patients after COVID-19 infection. METHODS: The olfactory bulbus volume was calculated by examining the brain magnetic resonance imaging of cases with positive (+) COVID-19 polymerase chain reaction test with and without anosmia. Evaluated magnetic resonance imaging images were the scans of patients before they were infected with COVID-19. The olfactory bulbus and olfactory nerve morphology of these patients were examined. The brain magnetic resonance imaging of 59 patients with anosmia and 64 controls without anosmia was evaluated. The olfactory bulb volumes of both groups were calculated. The olfactory bulb morphology and olfactory nerve types were examined and compared between the two groups. RESULTS: The left and right olfactory bulb volumes were calculated for the anosmia group and control group as 47.8±15/49.3±14.3 and 50.5±9.9/50.9±9.6, respectively. There was no statistically significant difference between the two groups. When the olfactory bulb morphology was compared between the two groups, it was observed that types D and R were dominant in the anosmia group (p<0.05). Concerning olfactory nerve morphology, type N was significantly more common in the control group (p<0.05). CONCLUSIONS: According to our results, the olfactory bulb volume does not affect the development of anosmia after COVID-19. However, it is striking that the bulb morphology significantly differs between the patients with and without anosmia. It is clear that the evaluation of COVID-19-associated smell disorders requires studies with a larger number of patients and a clinicoradiological approach.

Diagnostic performance and interobserver agreement of CO-RADS: evaluation of classification in radiology practice.

PURPOSE: We aimed to evaluate the use of the COVID-19 reporting and data system (CO-RADS) among radiologists and the diagnostic performance of this system. METHODS: Four radiologists retrospectively evaluated the chest CT examinations of 178 patients. The study included 143 patients with positive reverse transcriptase-polymerase chain reaction (RT-PCR) test results and 35 patients whose RT-PCR tests were negative but whose clinical and/or radiological findings were consistent with COVID-19. Fleiss' kappa (κ) values were calculated, and individual observers' scores were compared. To investigate diagnostic efficiency, receiver operating characteristic (ROC) curves were calculated for each interpreter. RESULTS: The interpreters were in full agreement on 574 of 712 (80.6%) evaluations. The common Fleiss' κ value of all the radiologists combined was 0.712 (95% confidence interval [CI] 0.692-0.769). A reliable prediction on the basis of RT-PCR and clinical findings indicated the mean area under the curve (AUC) of Fleiss' κ value as 0.89 (95% CI 0.708-0.990). General interpreter agreement was found to range from moderate to good. CONCLUSION: The interpreter agreement for CO-RADS categories 1 and 5 was reasonably good. We conclude that this scoring system will make a valuable contribution to efforts in COVID-19 diagnosis. CO-RADS can also be of significant value for the diagnosis and treatment of the disease in cases with false-negative PCR results.

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.