AIMC Topic: ErbB Receptors

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External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons Learned.

Academic radiology Sep 1, 2025
RATIONALE AND OBJECTIVES: Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features. Previously, we developed a radiogenomics model to predict epidermal growth factor receptor (EGFR) ...
Mutation ErbB Receptors Female Aged Middle Aged Genomics Male Humans Prevalence Carcinoma, Non-Small-Cell Lung Tomography, X-Ray Computed Lung Neoplasms Contrast Media
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Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Molecular diversity Aug 1, 2025
Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; ...
Deep Learning United States Angiogenesis Inhibitors Humans ErbB Receptors Receptors, Vascular Endothelial Growth Factor Receptors, Fibroblast Growth Factor Angiogenesis Drug Repositioning United States Food and Drug Administration Drug Approval
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Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

Lung cancer (Amsterdam, Netherlands) Jun 1, 2025
BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate detection essential for personalized treatment. Recently, artificial intelligence (AI) has emerged as...
Mutation Humans ErbB Receptors Artificial Intelligence Lung Neoplasms
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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...
Female Aged Adult Machine Learning Prognosis Retrospective Studies Lung Neoplasms Carcinoma, Non-Small-Cell Lung Mutation Biomarkers, Tumor Male Middle Aged Humans ErbB Receptors
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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...
Mutation Image Interpretation, Computer-Assisted DNA Mutational Analysis ErbB Receptors Adenocarcinoma of Lung Female Deep Learning Lung Neoplasms Humans
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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...
Information Storage and Retrieval ErbB Receptors Humans Deep Learning Lung Neoplasms 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 ErbB Receptors Cell Line, Tumor MCF-7 Cells Epithelial-Mesenchymal Transition Female Breast Neoplasms Deep Learning
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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...
Machine Learning Deep Learning Signal Transduction Drug Resistance, Neoplasm ErbB Receptors Humans Neural Networks, Computer
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TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Chemical biology & drug design Sep 1, 2020
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant muta...
ErbB Receptors Models, Theoretical Mutation Humans Datasets as Topic Machine Learning
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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology Aug 1, 2020
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...
Area Under Curve Machine Learning Mutation Proto-Oncogene Proteins p21(ras) Algorithms Positron-Emission Tomography Humans High-Throughput Nucleotide Sequencing Female Genomics Multivariate Analysis Aged Carcinoma, Non-Small-Cell Lung ErbB Receptors Male Multimodal Imaging Lung Neoplasms
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