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Receptor, ErbB-2

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Simulation and machine learning modelling based comparative study of InAlGaN and AlGaN high electron mobility transistors for the detection of HER-2.

Analytical methods : advancing methods and applications
The detection of the cancer biomarker human epidermal growth factor receptor 2 (HER-2) has always been challenging at the early stages of cancer due to its very small presence. A systematic study of biosensors to achieve optimum sensitivity is of par...

Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From Mammograms.

JCO precision oncology
PURPOSE: The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using d...

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Bulletin du cancer
HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negativ...

Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT.

Radiology
Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Case reports have suggested an association between T-DM1 and portal hypertension. Purpo...

Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry.

Applied immunohistochemistry & molecular morphology : AIMM
Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this st...

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.

Breast disease
OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted th...

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.

Scientific reports
Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridiza...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Scientific reports
Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endocrine therapy. However, ER positivity might not directly correlate with activated ER signaling activi...

A deep learning based holistic diagnosis system for immunohistochemistry interpretation and molecular subtyping.

Neoplasia (New York, N.Y.)
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...