Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

The British journal of radiology - 1 Dec 2018

Xiong Jun-Feng, Jia Tian-Ying, Li Xiao-Yang, Yu Wen, Xu Zhi-Yong, Cai Xu-Wei, Fu Ling, Zhang Jie, Qin Bin-Jie, Fu Xiao-Long, Zhao Jun

Abstract excerpt

OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimensional convolutional neural networks (CNN) to analyze underlying patterns in CT images that could indicate that EGFR gene mutation status but are invisible to human eyes....

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adenocarcinoma
Share to Area Under Curve

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to ErbB Receptors

Share to Image Processing, Computer-Assisted

Share to Imaging, Three-Dimensional

Share to Neural Networks, Computer

Share to Sensitivity and Specificity

Share to Tomography, X-Ray Computed

Source

Open in PubMed Open full text

Identifiers

PMID: 30059241
DOI: 10.1259/bjr.20180334
PMCID: PMC6319832

Genes

EGFR

Variants

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.PMID 30059241Read ↗

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.Semantic proximity Read ↗

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learningSemantic proximity Read ↗

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.Semantic proximity Read ↗

Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.Semantic proximity Read ↗

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography ImagesSemantic proximity Read ↗

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.Semantic proximity Read ↗

Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide imagesSemantic proximity Read ↗

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung AdenocarcinomaSemantic proximity Read ↗

Predicting Oncogene Mutations of Lung Cancer Using Deep Learning and Histopathologic Features on Whole-Slide ImagesSemantic proximity Read ↗

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.Semantic proximity Read ↗

Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep LearningSemantic proximity Read ↗

Predicting EGFR gene mutation status in lung adenocarcinoma based on multifeature fusionSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.