The review highlights the importance of integrating clinical datasets in breast cancer research to improve understanding of the disease and enhance patient outcomes. It emphasizes the need for collaborative efforts, policy changes, and technological advancements to facilitate this integration, which can lead to better evidence-based insights and personalized treatment strategies. Additionally, it advocates for regulatory processes that allow conditional approval of new treatments based on real-world performance.

This study evaluates the diagnostic accuracy of ultrasonography in assessing the response of clipped axillary lymph nodes to neoadjuvant chemotherapy in breast cancer patients. The results indicate that ultrasonography has an accuracy of 81.4%, with a significant correlation between ultrasound findings and pathology results, suggesting it may serve as a viable alternative to traditional axillary dissection.

The research presents a computer-aided diagnosis model using a windowing-based multi-view U-Net for breast tumor segmentation in cone-beam breast CT images, aiming to enhance diagnosis speed and accuracy. The method outperforms traditional single-window normalization techniques, achieving improved Dice coefficients and IoU values, thereby aiding radiologists in better distinguishing tumors from surrounding tissue.

This research introduces CITR-Net, a multi-modality fusion framework that integrates clinicopathologic information with tumor region characteristics to enhance the prediction of neoadjuvant chemotherapy (NACT) response in breast cancer. The framework utilizes advanced techniques to capture and merge features from both imaging and tabular data, demonstrating superior performance in experimental evaluations compared to existing methods, ultimately aiming to improve patient care and prognostic outcomes.

The study presents GeneXAI, a multi-modal framework that enhances breast cancer treatment prediction and prognosis by classifying cancer stages and identifying influential genes using explainable artificial intelligence models. The approach employs a hybrid feature selection method and achieves 5-7% higher accuracy than existing models, identifying key genes such as PLA2G10, MST1R, and F13B as significant in cancer staging.

This study identifies 12 key stromal risk genes associated with prognostic significance in invasive ductal breast carcinoma, revealing their roles in predicting high-risk patient groups and their correlation with drug resistance and sensitivity. The research utilized multiple datasets and algorithms to analyze gene expression and developed a prognostic-risk model, highlighting the potential for targeting these genes in therapeutic strategies.

The research developed a self-nanoemulsifying drug delivery system (SNEDDS) co-loaded with palbociclib and letrozole to enhance their anticancer efficacy by improving oral solubility and bioavailability. The optimized formulation demonstrated significant improvements in drug release, permeability, and cytotoxic effects against MCF-7 breast cancer cells, indicating its potential as an effective oral treatment for breast cancer.

This study focuses on the design, synthesis, and characterization of novel thiazolidinone derivatives linked to 2,4-dichlorobenzaldehyde for breast cancer therapy. Network pharmacology identified STAT3 as a key biological target, with compound 11 demonstrating strong binding affinity and stability in molecular docking and dynamics simulations.