肿瘤外科档案

Breast cancer is a global health concern affecting millions of women worldwide. While the importance of early detection and awareness campaigns is well-established, recent studies have shed light on the disparities in symptom recognition among women at higher breast cancer risk. Additionally, education has emerged as a crucial factor in improving awareness of breast cancer symptoms. This article aims to explore the relationship between breast cancer risk, education, and the recognition of non-lump symptoms among women, highlighting the implications for early detection and improved outcomes. Research indicates that women at higher risk of breast cancer often display poorer recognition of non-lump symptoms associated with the disease. While lumps remain the most widely recognized symptom, there is a concerning lack of awareness regarding other signs, such as breast pain, nipple changes, skin dimpling, and discharge. This knowledge gap poses a significant challenge to early detection and timely intervention, as these non-lump symptoms can be indicative of underlying breast cancer.

Many people who have cancerous tumors can get better with surgery. Since multimodality treatment has been linked to promising outcomes in some types of cancer, more attention has been paid to the combination of surgery and chemotherapy. Despite these findings, there is still clinical disagreement regarding the ideal patient selection and timing for neo-adjuvant or adjuvant strategies. By assisting in the prediction of tumor behavior and response to therapy, the emerging field of radiomics, which involves the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and advance personalized therapy. Predicting prognosis, recurrence, survival, and therapeutic response for various cancer types using radiomics and machine learning in patients who have received neo-adjuvant and/or adjuvant chemotherapy is the focus of this review. Although neoadjuvant and adjuvant studies show above average accuracy in predicting progression free survival and overall survival, widespread application of this technology faces numerous obstacles. The inclusion and rapid adoption of radiomics in prospective clinical studies has been hampered by the absence of autosegmentation, limited data sharing, and standardization of common procedures for analyzing radiomics.

One of the most common types of cancer is breast cancer. Pathological image processing of the breast has emerged as a significant method for early breast cancer diagnosis. In the field of medical image diagnosis, the use of medical image processing to help doctors detect potential breast cancer as soon as possible has always been a hot topic. In this paper, a bosom disease acknowledgment strategy in light of picture handling is efficiently explained from four perspectives: Image fusion, image segmentation, image registration, and breast cancer detection in the context of breast cancer examination, the accomplishments and application scope of supervised learning, unsupervised learning, deep learning, CNN, and other methods are discussed. The possibility of unaided learning and move learning for bosom malignant growth conclusion is prospected. Finally, patients with breast cancer should have their privacy protected.

Neovascular-associated retinal diseases, including age-related macular degeneration and diabetic retinopathy, are leading causes of vision loss worldwide. Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) plays a critical role in angiogenesis, making it an attractive therapeutic target for these diseases. In this study, we developed an Adeno-Associated Virus (AAV)-based fusion protein specifically targeting human VEGFR-2 domains to evaluate its efficacy in treating neovascular-associated retinal diseases in mice. The AAV-based fusion protein was designed to consist of a Single-Chain Antibody Fragment (scFv) derived from a high-affinity VEGFR-2 antibody, fused with a potent anti-angiogenic peptide. The scFv component enabled specific binding to VEGFR-2, while the anti-angiogenic peptide aimed to inhibit downstream signaling pathways involved in angiogenesis. The fusion protein was packaged into an AAV vector for efficient delivery to retinal cells.