Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

Background: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast lesions on static images. To better exploit the real-time advantages of ultrasound and more conducive to clinical application, we proposed a whole-lesion-aware network based on freehand ultrasound video (WAUVE) scanning in an arbitrary direction for predicting overall breast cancer risk score.

Methods: The WAUVE was developed using 2912 videos (2912 lesions) of 2771 patients retrospectively collected from May 2020 to August 2022 in two hospitals.

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer.

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients' survival. Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis.

Flexible ligand-Gd dye-encapsulated dual-emission metal-organic framework.

Multiple emission metal-organic frameworks (MOFs) are superior materials for ratiometric fluorescence sensing and visible detection applications. The guest-encapsulation strategy is a simple method for the integration of the open structure of MOFs and the abundant choice of emissive guests. Herein, we reveal the factors that affect the performance of host-guest multi-emission MOFs based on the choice of ligands, metal nodes, and guest dyes.

Resveratrol Enhances Inhibition Effects of Cisplatin on Cell Migration and Invasion and Tumor Growth in Breast Cancer MDA-MB-231 Cell Models In Vivo and In Vitro.

Triple-negative breast cancer (TNBC) is a refractory type of breast cancer that does not yet have clinically effective drugs. The aim of this study is to investigate the synergistic effects and mechanisms of resveratrol combined with cisplatin on human breast cancer MDA-MB-231 (MDA231) cell viability, migration, and invasion in vivo and in vitro. In vitro, MTS assays showed that resveratrol combined with cisplatin inhibits cell viability as a concentration-dependent manner, and produced synergistic effects (CI < 1).

A survey on incorporating domain knowledge into deep learning for medical image analysis.

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external information beyond current available medical datasets. Traditional approaches generally leverage the information from natural images via transfer learning.