Triple Negative Breast Neoplasms

G-protein coupled receptor-associated sorting protein 1 (GASP-1), a ubiquitous tumor marker, promotes proliferation and invasion of triple negative breast cancer.

Experimental and molecular pathology 35122807

We have identified the novel protein GASP-1 (G protein coupled receptor-associated sorting protein 1) that appears to be a universal cancer marker and the expression of which in tumor tissue and patient sera is predictive of cancer severity (Tuszynsk...

Biomarkers, Tumor Cell Line, Tumor Cell Proliferation Female Gene Expression Regulation, Neoplastic Humans Intercellular Signaling Peptides and Proteins Neoplasm Invasiveness Triple Negative Breast Neoplasms

View on PubMed DOI

Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

Computational and mathematical methods in medicine 34422088

PURPOSE: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (no...

Adult Aged Breast Neoplasms China Cohort Studies Computational Biology Contrast Media Deep Learning Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Middle Aged Principal Component Analysis Retrospective Studies ROC Curve Software Triple Negative Breast Neoplasms

View on PubMed DOI

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

Cancer medicine 34132495

OBJECTIVE: This retrospective study evaluated the model from populations with different breast densities and showed the model's performance on malignancy prediction.

Adult Aged Breast Density Breast Neoplasms Calcinosis Deep Learning Female Humans Mammography Middle Aged Retrospective Studies ROC Curve Sensitivity and Specificity Triple Negative Breast Neoplasms

View on PubMed DOI

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

PLoS computational biology 34297718

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 ...

Adaptation, Physiological Antigens, CD Biomarkers, Tumor Biophysical Phenomena Cadherins Cell Adhesion Cell Count Cell Line, Tumor Cell Proliferation Cell Shape Computational Biology Epithelial-Mesenchymal Transition Extracellular Matrix Female Humans Machine Learning Models, Biological Neoplasm Metastasis Triple Negative Breast Neoplasms Tumor Microenvironment Vimentin

View on PubMed DOI

Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Scientific reports 34650065

Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resn...

Adult Breast China Female Humans Middle Aged Neural Networks, Computer Retrospective Studies Sensitivity and Specificity Triple Negative Breast Neoplasms Ultrasonography, Mammary

View on PubMed DOI

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

Computational and mathematical methods in medicine 36092784

BACKGROUND: Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an importan...

Algorithms Deep Learning Humans Magnetic Resonance Imaging Neural Networks, Computer Triple Negative Breast Neoplasms

View on PubMed DOI

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

Journal of cancer research and clinical oncology 35771263

PURPOSE: To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category...

Artificial Intelligence Breast Neoplasms Contrast Media Deep Learning Female Fibroadenoma Humans Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Multiparametric Magnetic Resonance Imaging Retrospective Studies Sensitivity and Specificity Triple Negative Breast Neoplasms

View on PubMed DOI

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

Bioinformatics (Oxford, England) 35962988

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of ove...

Breast Neoplasms Deep Learning Female Humans Neoadjuvant Therapy Triple Negative Breast Neoplasms

View on PubMed DOI

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.

Breast (Edinburgh, Scotland) 36308926

INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic informatio...

Antineoplastic Combined Chemotherapy Protocols Biopsy Breast Neoplasms Deep Learning Female Humans Lymphocytes, Tumor-Infiltrating Neoadjuvant Therapy Retrospective Studies Triple Negative Breast Neoplasms

View on PubMed DOI

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images.

Medical & biological engineering & computing 36542320

To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April...

Breast Neoplasms Deep Learning Female Humans Retrospective Studies Triple Negative Breast Neoplasms Ultrasonography

View on PubMed DOI

AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

G-protein coupled receptor-associated sorting protein 1 (GASP-1), a ubiquitous tumor marker, promotes proliferation and invasion of triple negative breast cancer.

Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

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

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

Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images.

Popular Topics

Recent Journals