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PurposeThe most important prognostic variable for early stage breast cancer is the status of axillary lymph nodes. The aim of this study was to evaluate the diagnostic accuracy of preoperative magnetic resonance imaging (MRI) for metastatic axillary lymph node in breast cancer cases with post-operative sentinel lymph node biopsy (SLNB) results.Materials and methodsWomen aged between 21 and 73 years who were diagnosed with malignant mass lesion of the breast between 2013 and 2015 were included in this study. The preoperative MR images of patients with diagnosis of breast cancer was evaluated to determine axillary lymph node status. Axillary lymph node size, long axis to short axis ratio, lymph node contours, cortical thickness to anteroposterior diameter ratio, the presence of a fatty hilum and contrast enhancement patterns (homogenous or heterogenous) was noted. Additionally, the presence of comet tail sign which a tail extending from an enhancing breast lesion into the parenchyma and might represent ductal infiltration on post-contrast series was also noted. All data obtained from this evaluation was compared with postoperative SLNB results.ResultsMetastatic nodes were found to have a longer short axis when compared to reactive nodes (p = 0.042; p < 0.05). The long axis to short axis ratio was notably lower in metastatic nodes when compared to reactive nodes. Cortical thickness was higher in metastatic nodes when compared to reactive nodes (p = 0.024; p < 0.05). Comet sign was observed in 15 of metastatic nodes (73.3 %) and in one (5 %) reactive node. This difference was statistically significant (p = 0.001; p < 0.01). While fatty hilum was seen in 40 % of metastatic nodes (n = 6), it was seen in all (n = 20) reactive nodes. This difference was statistically significant (p = 0.001; p < 0.01).ConclusionsMRI is a non invasive sensitive and specific imaging modality for evaluating the axilla. We have shown that with the help of comet tail sign and status of fatty hilum contrast enhanced MRI has the highest sensitivity of 84.7 % for detecting axillary lymph node metastases (Singletary et al. in Semin Surg Oncol 21(1):53–59, 2003).

Objective: To describe the radiological features, diagnostic accuracy and features of imaging studies and their relation with clinical course of Coronavirus disease-2019 (COVID-19) pneumonia in pregnant women. Material and Methods: The clinical, laboratory and radiological features of symptomatic pregnant women suspected of COVID-19 were retrospectively reviewed. Chest radiography (CXR) and chest computed tomography (CT) findings of COVID-19 in pregnant women were identified. Results: Fifty-five of eighty-one pregnant women were included in the final analysis. The most common admission symptoms were dry cough (45.4%), fever (29.1%) and dyspnea (34.5%). Radiological imaging studies were performed in 34 (61.8%) patients. Fourteen (66.7%) of the laboratory-confirmed COVID-19 patients had parenchymal abnormalities on CXR, and most common abnormalities were airspace opacities (61.9%) and prominent bronchovascular shadows (28.6%). Seventeen (85.0%) of the patients had parenchymal abnormalities consistent with COVID-19 on their chest CT. Chest CT most commonly showed bilateral (88.2%), multilobe (100%) involvement; peripheral and central distribution (70.6%); patchy-shape (94.1%) and ground-glass opacity (94.1%). The sensitivity of CXR and chest CT was calculated as 66.7% and 83.3%, respectively. Preterm birth rate was 41.2% (n=7/17). Five (9.1%) of the 55 pregnant women admitted to the intensive care unit, three of those developed acute respiratory distress syndrome and one died. Conclusion: This study describes the main radiological features of symptomatic pregnant women infected with COVID-19. The refusal rate among pregnant women for the imaging modalities involving ionizing radiation was high but these had high sensitivity for COVID-19 diagnosis. The preterm birth and cesarean section rates were observed as remarkably increased.

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.