AIMC Topic: Receptors, Progesterone

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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...
Humans Immunohistochemistry Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone Ki-67 Antigen Female Breast Neoplasms Biomarkers, Tumor Deep Learning
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The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin.

Breast (Edinburgh, Scotland) May 28, 2021
Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach...
Middle Aged Neoplasm Recurrence, Local Receptor, ErbB-2 Receptors, Progesterone Retrospective Studies Female Breast Neoplasms Biomarkers, Tumor Humans Artificial Intelligence Prognosis
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Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

European journal of cancer (Oxford, England : 1990) Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
Humans Machine Learning Prognosis Retrospective Studies Electronic Health Records Antineoplastic Combined Chemotherapy Protocols Follow-Up Studies Natural Language Processing Receptor, ErbB-2 Receptors, Estrogen Adult Receptors, Progesterone Survival Rate Female Breast Neoplasms Aged Middle Aged Aged, 80 and over Disease Progression Young Adult
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A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Scientific reports Jul 2, 2020
Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient...
ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Receptor, ErbB-2 Adult Breast Neoplasms Middle Aged Adolescent Carcinoma, Ductal, Breast Receptors, Estrogen Receptors, Progesterone Carcinoma, Lobular Humans Machine Learning Prognosis Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult
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Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67.

IEEE transactions on medical imaging Aug 7, 2019
Immunohistochemistry (IHC) of ER, PR, and Ki-67 are routinely used assays in breast cancer diagnostics. Determination of the proportion of stained cells (labeling index) should be restricted on malignant epithelial cells, carefully avoiding tumor inf...
Ki-67 Antigen Image Interpretation, Computer-Assisted Immunohistochemistry Receptors, Estrogen Humans Receptors, Progesterone Keratins Algorithms Female Breast Neoplasms Biomarkers, Tumor Deep Learning Epithelial Cells
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Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.

Scientific reports Jun 25, 2019
Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural langua...
Breast Neoplasms Humans Natural Language Processing Machine Learning Datasets as Topic Receptors, Estrogen Algorithms Receptor, ErbB-2 Receptors, Progesterone Electronic Health Records Female
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Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

Genes Mar 7, 2019
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets...
Humans Machine Learning Female Genomics RNA, Messenger Triple Negative Breast Neoplasms DNA Methylation DNA Copy Number Variations Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone
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Molecular and epigenetic profiles of BRCA1-like hormone-receptor-positive breast tumors identified with development and application of a copy-number-based classifier.

Breast cancer research : BCR Jan 25, 2019
BACKGROUND: BRCA1-mutated cancers exhibit deficient homologous recombination (HR) DNA repair, resulting in extensive copy number alterations and genome instability. HR deficiency can also arise in tumors without a BRCA1 mutation. Compared with other ...
BRCA1 Protein Homologous Recombination Adult DNA Methylation Datasets as Topic Breast Neoplasms DNA Copy Number Variations Middle Aged Humans Breast Epigenomics Survival Analysis Female Support Vector Machine Aged CpG Islands Promoter Regions, Genetic Receptors, Estrogen Receptors, Progesterone
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Efficient prediction of progesterone receptor interactome using a support vector machine model.

International journal of molecular sciences Mar 3, 2015
Protein-protein interaction (PPI) is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, p...
Cluster Analysis Area Under Curve Receptors, Androgen Receptors, Estrogen Receptors, Progesterone Protein Interaction Domains and Motifs Humans ROC Curve Proteome Support Vector Machine
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland) Aug 1, 2025
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 ...
Risk Assessment Gene Expression Profiling Neoplasm Recurrence, Local Practice Guidelines as Topic Humans Sweden Receptor, ErbB-2 Machine Learning Aged Postmenopause Prognosis Breast Neoplasms Receptors, Estrogen Female Middle Aged Chemotherapy, Adjuvant Receptors, Progesterone
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