Objectives-To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques.Methods-A total of 240 participants were recruited and divided into 4 groups (normal, mild, moderate, and severe NAFLD groups), according to the definition and the ultrasound scoring system for NAFLD. Two-dimensional hepatic imaging was analyzed by the envelope signal, grayscale signal, and deep-learning index obtained by 3 image-processing techniques. The values of the 3 methods ranged from 0 to 65,535, 0 to 255, and 0 to 4, respectively. We compared the values among the 4 groups, draw receiver operating characteristic curves, and compared the area under the curve (AUC) values to identify the best imageprocessing technique.Results-The envelope signal value, grayscale value, and deep-learning index had a significant difference between groups and increased with the severity of NAFLD (P < .05). The 3 methods showed good ability (AUC > 0.7) to identify NAFLD. Meanwhile, the deep-learning index showed the superior diagnostic ability in distinguishing moderate and severe NAFLD (AUC = 0.958). Conclusions-The envelope signal and grayscale values were vital parameters in the diagnosis of NAFLD. Furthermore, deep learning had the best sensitivity and specificity in assessing the severity of NAFLD.

Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. Conclusions The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

This research aimed to discover chemical markers for discriminating radix Angelica sinensis (RAS) from different regions and to explore the differences of RAS in the content of four active compounds and anti-inflammatory activities on lipopolysacchride (LPS)-induced RAW264.7 cells and calcium antagonists on the HEK 293T cells of RAS. Nine compounds were selected as characteristic chemical markers by ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS), based on metabolomics, in order to rapidly discriminate RAS from geoherb and non-geoherb regions. The contents of senkyunolide I and butylidenephthalide in geoherb samples were higher than those in non-geoherb samples, but the contents of ferulic acid and levistolide A were lower in the geoherb samples. Furthermore, the geoherbs showed better nitric oxide (NO) inhibitory and calcium antagonistic activities than the non-geoherbs. These results demonstrate the diversity in quality of RAS between geoherbs and non-geoherbs.

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Objective: Esophageal carcinosarcoma (ECS) is a rare malignant tumor that accounts for only 0.5%-2.8% of all esophageal malignancies. As most current studies are case reports, the relationship between clinical features and prognosis remains controversial. Methods: We investigated the clinical features and prognosis of 24 patients with ECS in a single center from 2006 to 2018. There were 18 male and 6 female patients aged 52-82 years with a median age of 62.5 years. In addition, we included 9 studies on ECS from PubMed and a literature review. Results: The median follow-up time of the 24 patients was 70.5 (range, 10-156)months. The 3-year and 5-year survival rates were 83.3% and 70.8%, respectively. Among the 24 patients, none of the 10 (41.7%) stage T1 cancer patients had lymph node metastasis; however, lymph node metastasis was noted in 8 (57.1%) stage T2-4 cancer patients. The literature review revealed that 211 patients had a 5-year survival rate of 11.8%-68.2%, and 54.5%-95.8% study participants had early stage ECS. Although the information provided in the literature review is limited, it appears to be a characteristic of the early stage of the disease and predicts better prognosis when ECS is diagnosed, which is similar to the result of the current study. Conclusion: Our results indicate that ECS has a favorable prognosis, even among patients with early stage ECS who undergo radical esophagectomy with lymph node dissection. Because of the low incidence of ECS, further studies with more cases need to investigate this rare malignancy.