The purpose of this study was to use the Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) to evaluate the chest computed tomography (CT) images of patients suspected of having COVID-19, and to investigate its diagnostic performance and interobserver agreement. The Dutch Radiological Society developed CO-RADS as a diagnostic indicator for assessing suspicion of lung involvement of COVID-19 on a scale of 1 (very low) to 5 (very high). We investigated retrospectively 154 adult patients with clinically suspected COVID-19, between April and June 2020, who underwent chest CT and reverse transcription-polymerase chain reaction (RT-PCR). The patients’ average age was 61.3 years (range, 21–93), 101 were male, and 76 were RT-PCR positive. Using CO-RADS, four radiologists evaluated the chest CT images. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC) by comparing the individual reader’s score to the median of the remaining three radiologists. The average sensitivity was 87.8% (range, 80.2–93.4%), specificity was 66.4% (range, 51.3–84.5%), and AUC was 0.859 (range, 0.847–0.881); there was no significant difference between the readers (p > 0.200). In 325 (52.8%) of 616 observations, there was absolute agreement among observers. The average ICC of readers was 0.840 (range, 0.800–0.874; p < 0.001). CO-RADS is a categorical taxonomic evaluation scheme for COVID-19 pneumonia, using chest CT images, that provides outstanding performance and from substantial to almost perfect interobserver agreement for predicting COVID-19.

Non-ampullary duodenal adenoma with activation of Wnt/β-catenin signalling is common in familial adenomatous polyposis (FAP) patients, whereas sporadic non-ampullary adenoma is uncommon. The adenoma-carcinoma sequence similar to colon cancer is associated with duodenal tumors in FAP, but not always in sporadic tumors. We obtained 37 non-ampullary duodenal tumors, including 25 adenomas and 12 adenocarcinomas, were obtained from biopsies and endoscopic resections. We performed immunohistochemistry for β-catenin, the hallmark of Wnt activation, and aldehyde dehydrogenase 1 (ALDH1), a putative cancer stem cell marker. In non-ampullary lesions, abnormal nuclear localization of β-catenin was observed in 21 (84.0%) of 25 adenomas and 4 (33.3%) of 12 adenocarcinomas. In the proximal duodenum, nuclear β-catenin was less frequent in both adenomas and adenocarcinomas. Gastric duodenal metaplasia (GDM) was observed only in the proximal duodenum. All adenomas with GDM were the gastric foveolar and pyloric gland types, and showed only membranous β-catenin. The intestinal-type adenomas had nuclear β-catenin in the proximal and distal duodenum. ALDH1-positive cells were more frequent in adenocarcinomas than adenomas. Nuclear β-catenin accumulation frequently occurred in ALDH1-positive cells in adenoma, but not in adenocarcinoma. In the non-ampullary proximal duodenum, Wnt/β-catenin pathway activation was more closely associated with adenomas than adenocarcinomas, and while it might cooperate with ALDH1 in adenoma, it does not in adenocarcinoma. The pathogenesis thus may differ between sporadic adenoma and adenocarcinoma of non-ampullary duodenal lesions, especially in the proximal and distal duodenum.

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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