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ErbB Receptors

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Efficacy according to blind independent central review: Post-hoc analyses from the phase III, randomized, multicenter, IPASS study of first-line gefitinib versus carboplatin/paclitaxel in Asian patients with EGFR mutation-positive advanced NSCLC.

Lung cancer (Amsterdam, Netherlands) Nov 30, 2016
OBJECTIVE: The Phase III, randomized, open-label IPASS study (NCT00322452) of first-line epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) gefitinib versus carboplatin/paclitaxel for Asian patients with advanced non-small-cell lun...
Disease-Free Survival Smoking ErbB Receptors Protein Kinase Inhibitors Lung Neoplasms Antineoplastic Combined Chemotherapy Protocols Asian People Adenocarcinoma Carboplatin Carcinoma, Non-Small-Cell Lung Paclitaxel Humans Randomized Controlled Trials as Topic Female Neoplasm Staging Gefitinib Aged Mutation Aged, 80 and over Quinazolines Male Middle Aged Antineoplastic Agents
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Surface Adsorbed Antibody Characterization Using ToF-SIMS with Principal Component Analysis and Artificial Neural Networks.

Langmuir : the ACS journal of surfaces and colloids Aug 19, 2016
Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality...
Multivariate Analysis ErbB Receptors Immunoglobulin Fragments Spectrometry, Mass, Secondary Ion Adsorption Humans Principal Component Analysis Amino Acids Antibodies Neural Networks, Computer Immunoglobulin G Animals
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Label-free and dynamic evaluation of cell-surface epidermal growth factor receptor expression via an electrochemiluminescence cytosensor.

Talanta Dec 18, 2015
A label-free electrochemiluminescence (ECL) cytosensor was developed for dynamically evaluating of epidermal growth factor receptor (EGFR) expression on MCF-7 cancer cells based on the specific recognition of epidermal growth factor (EGF) with its re...
Optical Imaging Biosensing Techniques Flow Cytometry ErbB Receptors MCF-7 Cells Sulfides Metal Nanoparticles Epidermal Growth Factor Quantum Dots Humans Gene Expression Regulation Luminescent Measurements Gold Electrochemistry Cadmium Compounds
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Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.

Mutation research Jul 20, 2015
Cancer is one of the most life-threatening diseases and mutations in several genes are the vital cause in tumorigenesis. Protein kinases play essential roles in cancer progression and specifically, epidermal growth factor receptor (EGFR) is an import...
Neoplasms Polymorphism, Single Nucleotide Support Vector Machine Models, Genetic Humans ErbB Receptors Mutation, Missense
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Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.

TheScientificWorldJournal Feb 23, 2015
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung c...
Humans Neural Networks, Computer Biomarkers, Tumor Lung Neoplasms Tumor Suppressor Protein p53 ErbB Receptors Proto-Oncogene Proteins p21(ras) Support Vector Machine Carcinoma, Non-Small-Cell Lung Mutation
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[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Zhongguo fei ai za zhi = Chinese journal of lung cancer Feb 20, 2025
BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic facto...
Retrospective Studies Female Aged Adult Humans Machine Learning Prognosis Lung Neoplasms Carcinoma, Non-Small-Cell Lung Mutation ErbB Receptors Biomarkers, Tumor Male Middle Aged
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Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

The journal of pathology. Clinical research Nov 1, 2024
EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitati...
Deep Learning Lung Neoplasms Mutation Image Interpretation, Computer-Assisted DNA Mutational Analysis ErbB Receptors Adenocarcinoma of Lung Humans Female
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D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

Briefings in bioinformatics Mar 27, 2024
As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recomm...
Humans Deep Learning Information Storage and Retrieval Lung Neoplasms ErbB Receptors Carcinoma, Non-Small-Cell Lung Mutation
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Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England) Dec 22, 2021
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...
Humans Female Breast Neoplasms Epithelial-Mesenchymal Transition ErbB Receptors Deep Learning Cell Line, Tumor MCF-7 Cells
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Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Cell reports Jan 19, 2021
It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode s...
ErbB Receptors Deep Learning Signal Transduction Drug Resistance, Neoplasm Humans Neural Networks, Computer Machine Learning
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