AIMC Topic: Triple Negative Breast Neoplasms

Clear Filters Showing 31 to 40 of 52 articles

Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

PLoS computational biology Jul 23, 2021
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Cell Proliferation Antigens, CD Cell Count Epithelial-Mesenchymal Transition Biomarkers, Tumor Cell Line, Tumor Computational Biology Triple Negative Breast Neoplasms Extracellular Matrix Biophysical Phenomena Cell Adhesion Cell Shape Adaptation, Physiological Cadherins Models, Biological Vimentin Neoplasm Metastasis Machine Learning Female Tumor Microenvironment Humans
View on PubMed DOI

Application of deep learning in the detection of breast lesions with four different breast densities.

Cancer medicine Jun 16, 2021
OBJECTIVE: This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction.
Humans ROC Curve Triple Negative Breast Neoplasms Calcinosis Mammography Sensitivity and Specificity Retrospective Studies Breast Density Breast Neoplasms Female Aged Middle Aged Adult Deep Learning
View on PubMed DOI

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Scientific reports Oct 8, 2020
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we perf...
Antineoplastic Agents Deep Learning Triple Negative Breast Neoplasms Receptors, G-Protein-Coupled Humans Neural Networks, Computer Algorithms Drug Discovery
View on PubMed DOI

Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies.

International journal of environmental research and public health Mar 20, 2020
Triple-negative breast cancer (TNBC) cells are deficient in estrogen, progesterone and ERBB2 receptor expression, presenting a particularly challenging therapeutic target due to their highly invasive nature and relatively low response to therapeutics...
Neoplasm Recurrence, Local Poly(ADP-ribose) Polymerases Poly(ADP-ribose) Polymerase Inhibitors Receptors, Cell Surface Artificial Intelligence Female Triple Negative Breast Neoplasms Signal Transduction Humans
View on PubMed DOI

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands) Apr 15, 2019
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...
Middle Aged Deep Learning Triple Negative Breast Neoplasms Disease-Free Survival Proportional Hazards Models Multivariate Analysis Mitosis Humans Algorithms Prognosis Female
View on PubMed DOI

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 RNA, Messenger DNA Copy Number Variations Triple Negative Breast Neoplasms Receptor, ErbB-2 DNA Methylation Receptors, Estrogen Female Receptors, Progesterone Genomics
View on PubMed DOI

Plasmonic MoO nanoparticles incorporated in Prussian blue frameworks exhibit highly efficient dual photothermal/photodynamic therapy.

Journal of materials chemistry. B Feb 21, 2019
Development of near infrared (NIR) light-responsive nanomaterials for high performance multimodal phototherapy within a single nanoplatform is still challenging in technology and biomedicine. Herein, a new phototherapeutic nanoagent based on FDA-appr...
Oxides Nanocomposites Nanoparticles Humans Molybdenum Triple Negative Breast Neoplasms Coloring Agents Phototherapy Female Mice, Inbred BALB C Ferrocyanides Animals Reactive Oxygen Species Lasers Cell Line, Tumor
View on PubMed DOI

Machine learning for diagnostic ultrasound of triple-negative breast cancer.

Breast cancer research and treatment Oct 20, 2018
PURPOSE: Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine lear...
Ultrasonography, Mammary Breast Ultrasonography, Doppler, Color ROC Curve Image Processing, Computer-Assisted Machine Learning Humans Triple Negative Breast Neoplasms Retrospective Studies Female Early Detection of Cancer Adult Middle Aged
View on PubMed DOI

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Breast cancer research and treatment Oct 16, 2018
PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant ...
Machine Learning Retrospective Studies Female Aged Adult Middle Aged ROC Curve Receptor, ErbB-2 Image Processing, Computer-Assisted Mastectomy, Segmental Humans Magnetic Resonance Imaging Triple Negative Breast Neoplasms Neoplasm Staging Antineoplastic Combined Chemotherapy Protocols Breast Treatment Outcome Feasibility Studies Neoadjuvant Therapy
View on PubMed DOI

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer.

IEEE transactions on nanobioscience Jul 2, 2018
This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 b...
Single-Cell Analysis Genomics Gene Expression Regulation, Neoplastic Humans Triple Negative Breast Neoplasms Cluster Analysis Female Unsupervised Machine Learning Antineoplastic Agents Metformin Gene Expression Profiling Cell Line, Tumor
View on PubMed DOI
Popular Topics
Recent Journals