AIMC Topic: ErbB Receptors

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A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society Jan 20, 2023
BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with...
Humans Deep Learning Lung Neoplasms Disease-Free Survival ErbB Receptors Protein Kinase Inhibitors Tyrosine Kinase Inhibitors Carcinoma, Non-Small-Cell Lung Mutation Progression-Free Survival
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Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Radiology Jul 5, 2022
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To...
Adenocarcinoma of Lung Lung Neoplasms Adenocarcinoma Retrospective Studies ErbB Receptors Middle Aged Tomography, X-Ray Computed Humans Aged Deep Learning Female Male
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Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

PloS one May 18, 2022
In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related...
Neural Networks, Computer Drug Resistance Machine Learning Gene Regulatory Networks ErbB Receptors
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Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

Journal of neuro-oncology Feb 4, 2022
PURPOSE: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, d...
Humans Prospective Studies Retrospective Studies Carcinoma, Non-Small-Cell Lung Mutation ErbB Receptors Brain Neoplasms Deep Learning Lung Neoplasms
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Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

Medical physics Nov 7, 2021
PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.
Mutation ErbB Receptors Adenocarcinoma of Lung Retrospective Studies Deep Learning Lung Neoplasms Humans
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Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

International journal of molecular sciences Aug 26, 2021
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin...
Carcinoma, Non-Small-Cell Lung Neoplasm Staging Mutation Proto-Oncogene Proteins p21(ras) Supervised Machine Learning Middle Aged Humans ErbB Receptors ROC Curve Machine Learning Aged Lung Neoplasms Algorithms Aged, 80 and over Female Male Reproducibility of Results Biomarkers Tomography, X-Ray Computed
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Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

Acta cytologica Apr 30, 2021
This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (A...
Mutation DNA Mutational Analysis ErbB Receptors Adenocarcinoma of Lung Pathology, Molecular Image Processing, Computer-Assisted Lung Neoplasms Molecular Diagnostic Techniques Predictive Value of Tests Automation, Laboratory Reproducibility of Results Diagnosis, Computer-Assisted Humans Artificial Intelligence
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Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research Apr 22, 2021
PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice ...
Humans Artificial Intelligence Proto-Oncogene Proteins p21(ras) Amphiregulin Epiregulin Panitumumab Colorectal Neoplasms Antineoplastic Combined Chemotherapy Protocols ErbB Receptors
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Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

JAMA network open Dec 1, 2020
IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (...
Carcinoma, Non-Small-Cell Lung Predictive Value of Tests Neoplasm Staging Humans Mutation Protein Kinase Inhibitors Middle Aged Female Antineoplastic Protocols Survival Analysis Machine Learning Antineoplastic Agents Male Proportional Hazards Models Prognosis Lung Neoplasms ErbB Receptors
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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.

Nature communications Nov 17, 2020
Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we u...
Deep Learning ErbB Receptors Neural Networks, Computer Microscopy, Fluorescence Single Molecule Imaging Algorithms Fluorescence Resonance Energy Transfer Databases, Factual
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