Trends in Oncology (Print ISSN: 2717-5278 Online ISSN: 2810-9147 ) is an open access, peer-reviewed journal that publishes original articles, review articles, editorials, case report, letters to the editor, perspective and commentaries on all areas of oncology research, including the molecular biology, pathophysiology, prevention, diagnosis and treatment of tumor. In addition, special issues focusing on particular oncology disciplines will be also organized and stipulated. Trends in Oncology aims to publish high-quality academic articles, promote academic exchanges and scientific research progress in the field of oncology.
-
Immune Cell Infiltration Characteristics in the Tumor Microenvironment of Triple-Negative Breast Cancer and Their Prognostic Value
Yun Liu
Triple-negative breast cancer (TNBC) is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, rendering endocrine therapy and anti-HER2 targeted therapy inefective. As a result, therapeutic options for TNBC are limited, and the prognosis remains poor. In recent years, with the successful application of immunotherapy in various solid tumors, TNBC—recognized as the most immunogenic subtype of breast cancer—has attracted increasing attention, particularly regarding the immune cell infiltration patterns within its Tumor Microenvironment (TME). This review systematically summarizes the composition, functional states, spatial distribution, and interaction networks of key immune cell populations in the TNBC TME, including tumor-
PDF
infiltrating lymphocytes (TILs), myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), and regulatory T cells (Tregs). Furthermore, it comprehensively discusses the associations between these immune infiltration characteristics and clinicopathological features, therapeutic responses, and long-term survival outcomes of patients. This review aims to provide a theoretical basis and novel insights for the development of precision immunotherapy strategies in TNBC. -
Analysis of Prostate Contrast-Enhanced Ultrasonography Images Based on Deep Learning
De-Jun Hu; Kang He, Jin Li
Prostate diseases seriously affect men's health, and contrast-enhanced ultrasonography imaging technology plays an important role in the diagnosis of prostate diseases. However, traditional image analysis methods have certain limitations when dealing with prostate contrast-enhanced ultrasonography images. This study aims to utilize the powerful feature learning and pattern recognition capabilities of deep learning to conduct precise analysis on prostate contrast-enhanced ultrasonography images. By constructing an appropriate deep learning model architecture, collecting and sorting out a large number of prostate contrast-enhanced ultrasonography image datasets, and carrying out model training, verification and testing. The experimental results show that, compared with traditional methods, the proposed deep learning method demonstrates higher accuracy and efficiency in aspects such as the identification of prostate lesions, the delineation of boundaries and the analysis of contrast agent perfusion characteristics, and is expected to provide a powerful technical support for the early diagnosis and condition assessment of prostate diseases.
PDF
Jun Yan
Director of Tumor Immunobiology Program, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
All publications
Database Indexing
