Artificial intelligence in liver ultrasound.

Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.

Ultrasound, CT, and MR Imaging for Evaluation of Cystic Renal Masses.

Cystic renal masses are often encountered during abdominal imaging. Although most of them are benign simple cysts, some cystic masses have malignant characteristics. The Bosniak classification system provides a useful way to classify cystic masses. The Bosniak classification is based on the results of a well-established computed tomography protocol. Over the past 30 years, the classification system has been refined and improved. This paper reviews the literature on this topic and compares the advantages and disadvantages of different screening and classification methods. Patients will benefit from multimodal diagnosis for lesions that are difficult to classify after a single examination.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

SOX7 is involved in aspirin-mediated growth inhibition of human colorectal cancer cells.

AIM: To confirm the role of sex-determining region Y-box 7 (Sox7) in aspirin-mediated growth inhibition of COX-independent human colorectal cancer cells. METHODS: The cell survival percentage was examined by MTT (Moto-nuclear cell direc cytotoxicity) assay. SOX7 expression was assessed by using reverse transcription-polymerase chain reaction and Western blotting. SB203580 was used to inhibit the p38MAPK signal pathway. SOX7 promoter activity was detected by Luciferase reporter assay. RESULTS: SOX7 was upregulated by aspirin and was involved in aspirin-mediated growth inhibition of SW480 human colorectal cancer cells. The p38MAPK pathway played a role in aspirin-induced SOX7 expression, during which the AP1 transcription factors c-Jun and c-Fos upregulated SOX7 promoter activities. RESULTS: SOX7 is upregulated by aspirin and is involved in aspirin-mediated growth inhibition of human colorectal cancer SW480 cells.

[Advances in diabetic erectile dysfunction].

Erectile dysfunction is common complication of diabetes mellitus. The incidence of diabetes mellitus induced erectile dysfunction (DMED) is 20% - 75%. DMED appears to be due to vascular-neuropathic and corpus cavernosum smooth muscular damage. To control blood glucose, blood pressure and blood lipids is the basis of DMED therapy. In 50% of the patients with DMED, the phosphodiesterase 5 inhibitors is effective, while intracavernous pharmacotherapy is effective for more than 90%. Penile prosthesis implantation continues to be the treatment of choice in case of other therapy failure.