AIMC Topic: Receptors, Estrogen
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A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.
Clinical epigenetics
Jul 2, 2025
DNA methylation in breast tumours has been extensively studied and has provided valuable insights into the clinical heterogeneity of breast cancer. In this review, we summarise the current literature that has used DNA methylation markers to subtype b...
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.
Nature communications
Jul 1, 2025
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients w...
Efficacy and safety of first-line CDK4/6 inhibitors plus AI therapy for patients with HR +/HER2- advanced breast cancer: a network meta-analysis.
BMC cancer
May 8, 2025
BACKGROUND: CDK4/6 inhibitors plus aromatase inhibitors (AI) significantly improve the therapeutic effect of initial treatment for HR + /HER2- advanced breast cancer. However, there is a lack of head-to-head randomized controlled trials involving the...
Cyclin-Dependent Kinase 6
Network Meta-Analysis as Topic
Aminopyridines
Antineoplastic Combined Chemotherapy Protocols
Treatment Outcome
Receptors, Estrogen
Humans
Receptor, ErbB-2
Randomized Controlled Trials as Topic
Purines
Female
Aromatase Inhibitors
Progression-Free Survival
Receptors, Progesterone
Breast Neoplasms
Protein Kinase Inhibitors
Cyclin-Dependent Kinase 4
Benzimidazoles
From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.
Environmental science & technology
Apr 30, 2025
Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducin...
Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.
Applied immunohistochemistry & molecular morphology : AIMM
Mar 27, 2025
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...
Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.
Environmental pollution (Barking, Essex : 1987)
Feb 11, 2025
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have e...
An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.
Scientific reports
Feb 9, 2025
Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strateg...
Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.
Cell reports. Medicine
Jan 22, 2025
Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue. Current relapse predictive models in H...
Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach.
Breast (Edinburgh, Scotland)
Jan 13, 2025
BACKGROUND: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-...
Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.
Breast cancer research : BCR
Jan 12, 2025
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a m...
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