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. Thus, radiomics provides a new approach to noninvasive assessment of breast cancer prognosis. Ultrasound is one of the commonest clinical means of examining breast cancer. In recent years, some results of research into ultrasound radiomics for diagnosing breast cancer, predicting lymph node status, treatment response, recurrence and survival times, and other aspects, have been published. In this article, we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis. We aim to provide a reference for radiomics researchers, promote the development of ultrasound radiomics, and advance its clinical application.

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. The size of organic dyes is often larger than 1 nm, which is incompatible with the small pores of traditional MOFs. Therefore, we selected a flexible ligand, i.e., 1,3,5-tris(5-methoxy-1,3-benzene dicarboxylic acid)benzene (L), to enlarge the pore size of the MOF to 18 Å. Energy transfer may occur from the ligand and guest dye to the metal nodes; thus, we selected the Gd3+ ion because of its high excited state level. L and Gd3+ ions were used to form the Gd-L MOF with the pore size of 18 Å, as revealed by the single crystal result. Rhodamine B (RhB), as the guest dye with the size of 15.9 × 11.8 × 5.6 Å3, was encapsulated in Gd-L MOF, which was denoted as RhB@MOF. The matched size between RhB and the MOF pore and the breathing effect of the flexible MOF effectively prevented the leakage of RhB. Accordingly, dual emission was observed at 360 nm and 583 nm under the excitation of 290 nm from RhB@MOF. Alternatively, Cu2+ quenched the emission at 360 nm due to the electron transfer process, while Fe3+ interacted with both L and RhB, and thus quenched the two emissions simultaneously. However, other metal ions showed little effect on the two emissions. Consequently, the differentiation between Cu2+ and Fe3+ as well as them from other metal ions was realized with the dual-emission MOF. Thus, the guest-encapsulation strategy is simple and flexible ligands are efficient to encapsulate molecular dyes for dual-emission MOFs to improve their sensing performance, while flexible ligands are powerful to enhance the capacity and extend the applications of MOFs.

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). Transwell assay showed that the combined treatment inhibits TGF-ß1-induced cell migration and invasion. Immunofluorescence assays confirmed that resveratrol upregulated E-cadherin expression and downregulated vimentin expression. Western blot assay demonstrated that resveratrol combined with cisplatin significantly reduced the expression of fibronectin, vimentin, P-AKT, P-PI3K, P-JNK, P-ERK, Sma2, and Smad3 induced by TGF-ß1 (p < 0.05), and increased the expression of E-cadherin (p < 0.05), respectively. In vivo, resveratrol enhanced tumor growth inhibition and reduced body weight loss and kidney function impairment by cisplatin in MDA231 xenografts, and significantly reduced the expressions of P-AKT, P-PI3K, Smad2, Smad3, P-JNK, P-ERK, and NF-κB in tumor tissues (p < 0.05). These results indicated that resveratrol combined with cisplatin inhibits the viability of breast cancer MDA231 cells synergistically, and inhibits MDA231 cells invasion and migration through Epithelial-mesenchymal transition (EMT) approach, and resveratrol enhanced anti-tumor effect and reduced side of cisplatin in MDA231 xenografts. The mechanism may be involved in the regulations of PI3K/AKT, JNK, ERK and NF-κB expressions.

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. More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas they pay particular attention to. In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation. For each task, we systematically categorize different kinds of medical domain knowledge that have been utilized and their corresponding integrating methods. We also provide current challenges and directions for future research.