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

Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.

Cancer research communications - 1 Dec 2025

Park Jongchan, Shin Sangwon, Hwang Woochan, Keum Seongho, Brattoli Biagio, Rawson Jack H, Lee Taebum, Pereira Sérgio, Ahn Chang Ho, Senior Michael J T, Qaiser Talha, Kim Seokhwi, Kim Hyojin, Chung Jin-Haeng, Choi Yoon-La, Lee Se-Hoon, Bannister Huw, Riboni-Verri Elia, Ock Chan-Young, Hill Ross J, Ali Siraj, Moore Luiza

Abstract excerpt

EGFR mutation screening in non-small cell lung cancer (NSCLC) remains variable globally and represents a significant care gap despite international recommendations and molecular testing guidelines. Recently, the use of deep learning (DL) methods to extract clinically actionable features from routine histology images has gained regulatory approval for multiple clinical applications. Therefore, the integration of...

Read the complete abstract on PubMed

Topics

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

Share to Biomarkers, Tumor
Share to Carcinoma, Non-Small-Cell Lung

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to ErbB Receptors

Share to EGFR gene

Share to Lung Neoplasms

Share to Mutation

Source

Open in PubMed Open full text

Identifiers

PMID: 41211715
DOI: 10.1158/2767-9764.CRC-25-0155
PMCID: PMC12682618

Genes

EGFR

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.PMID 41211715Read ↗

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

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

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

Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.Semantic proximity Read ↗

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.Semantic proximity Read ↗

Pan-cancer image-based detection of clinically actionable genetic alterations.Semantic proximity Read ↗

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep LearningSemantic proximity Read ↗

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.Semantic proximity Read ↗

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

Predicting EGFR mutational status from pathology images using a real-world dataset.Semantic proximity Read ↗

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.Semantic proximity Read ↗

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