Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.

BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. METHODS: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. FINDINGS: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886-0·936) in the internal dataset, 0·870 (95% CI 0·822-0·918) in external validation dataset 1, and 0·831 (95% CI 0·793-0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0-90·2] vs 78·3% [72·1-84·5], p<0·0001; 82·7% [78·5-86·9] vs 70·4% [59·1-81·7], p<0·0001). The average accuracy of DCNN-assisted evaluations for six radiologists reached 0·876 and were significantly augmented when they were DCNN-assisted (p<0·05). INTERPRETATION: The performance of DCNN-enabled ultrasound exceeded the average diagnostic level of radiologists matched the level of expert ultrasound image readers, and augmented radiologists' accuracy. However, these observations warrant further investigations in prospective studies or randomised clinical trials. FUNDING: National Key Basic Research Program of China, National Sci-Tech Support Projects, and National Natural Science Foundation of China.

Qiliqiangxin Rescues Mouse Cardiac Function by Regulating AGTR1/TRPV1-Mediated Autophagy in STZ-Induced Diabetes Mellitus.

BACKGROUND/AIMS: To explore the potential role of qiliqiangxin (QLQX) A traditional Chinese medicine and the involvement of angiotensin II receptor type 1 (AGTR1) and transient receptor potential vanilloid 1 (TRPV1) in diabetic mouse cardiac function. METHODS: Intragastric QLQX was administered for 5 weeks after streptozotocin (STZ) treatment. Additionally, Intraperitoneal injections of angiotensin II (Ang II) or intragastric losartan (Los) were administered to assess the activities of AGTR1 and TRPV1. Two-dimensional echocardiography and tissue histopathology were used to assess cardiac function Western blot was used to detect the autophagic biomarkers Such as light chain 3 P62 and lysosomal-associated membrane protein 2 And transmission electron microscopy was used to count the number of autophagosomes. RESULTS: Decreased expression of TRPV1 and autophagic hallmarks and reduced numbers of autophagolysosomes as well as increased expression of angiotensin converting enzyme 1 and AGTR1 were observed in diabetic hearts. Blocking AGTR1 with Los mimicked the QLQX-mediated improvements in cardiac function Alleviated myocardial fibrosis and enabled autophagy Whereas Ang II abolished the beneficial effects of QLQX in wild type diabetic mice but not in TRPV1-/- diabetic mice. CONCLUSIONS: QLQX may improve diabetic cardiac function by regulating AGTR1/ TRPV1-mediated autophagy in STZ-induced diabetic mice.