AIMC Topic: Breast Neoplasms

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation.

Computers in biology and medicine
Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies...

Multimodal deep learning for enhanced breast cancer diagnosis on sonography.

Computers in biology and medicine
This study introduces a novel multimodal deep learning model tailored for the differentiation of benign and malignant breast masses using dual-view breast ultrasound images (radial and anti-radial views) in conjunction with corresponding radiology re...

The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.

Clinical breast cancer
AIMS: Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be ...

Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Pathology, research and practice
Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurate and timely diagnosis for effective treatment. Though considered the gold standard, traditional his...

MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images.

Medical image analysis
Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its h...

Breast cancer neoadjuvant therapy outcome prediction based on clinical patient and tumor features: A cross-sectional study.

Current problems in cancer
INTRODUCTION: Breast cancer is the most common malignant disease in the female population and one of the most common diseases in developed countries. Many factors which may impact the development and outcome of this complex disease have been investig...

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland)
PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused ...

Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2-Stained Clinical Specimens in Breast Cancer.

Archives of pathology & laboratory medicine
CONTEXT.—: Precise determination of biomarker status is necessary for clinical trial enrollment and endpoint analyses, as well as for optimal treatment determination in real-world practice. However, variabilities may be introduced into this process d...

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Lab on a chip
Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine therapy (ET). Recent studies identify the tumor microenvironment (TME) as having a key role in endoc...

Dissecting Exosomal-Tumoral-Vascular Interactions of Single Tumor Cells and Clusters Using a Tumoral-Transendothelial Migration Chip.

ACS nano
The complex interplay between tumor cells and clusters with endothelial tissues during metastasis, in particular with regard to the exosomes in mediating intercellular communication, is still not well understood. Here, we develop a tumoral-transendot...