AIMC Topic: Receptors, Progesterone
Clear Filters Showing 1 to 10 of 26 articles
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
Receptor, ErbB-2
Aromatase Inhibitors
Receptors, Estrogen
Protein Kinase Inhibitors
Purines
Benzimidazoles
Receptors, Progesterone
Antineoplastic Combined Chemotherapy Protocols
Cyclin-Dependent Kinase 4
Humans
Treatment Outcome
Female
Randomized Controlled Trials as Topic
Breast Neoplasms
Progression-Free Survival
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...
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...
Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.
Current medical research and opinion
Jan 6, 2025
Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) staining is a widely used technique for the evaluation of breast cancer markers, but manual scoring ...
Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.
Academic radiology
Nov 29, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (...
Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.
Computers in biology and medicine
Oct 25, 2024
Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive...
Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.
The American journal of pathology
Sep 4, 2024
In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. These two often occur concomitantly. The immunohistochemical staining of estrogen receptor (ER...
Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis.
PloS one
May 31, 2024
BACKGROUND: Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods.
A deep learning based holistic diagnosis system for immunohistochemistry interpretation and molecular subtyping.
Neoplasia (New York, N.Y.)
Feb 26, 2024
BACKGROUND: Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), and Ki67, exhibit distinct symptom char...
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