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

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

Lung cancer (Amsterdam, Netherlands) - 1 Jun 2025

Nguyen Mai Hanh, Le Minh Huu Nhat, Bui Anh Tuan, Le Nguyen Quoc Khanh

Abstract excerpt

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 a promising tool for identifying EGFR mutation status from digital pathology images. This systematic review and meta-analysis evaluate the diagnostic accuracy of AI...

Read the complete abstract on PubMed

Topics

Humans
Lung Neoplasms
ErbB Receptors
Mutation
Artificial Intelligence

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40339270
DOI: 10.1016/j.lungcan.2025.108577

Genes

EGFR

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 20 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.PMID 40339270Read ↗

Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Semantic proximity Read ↗

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.Semantic proximity Read ↗

Prediction of oncogene mutation status in non-small cell lung cancer: A systematic review and meta-analysis with a special focus on artificial-intelligence-based methodsSemantic proximity Read ↗

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

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

AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic AlgorithmsSemantic proximity Read ↗

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.Semantic 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 ↗

AI-Driven Computational Models for Lung Cancer Diagnosis: A Systematic Review and Meta-AnalysisSemantic proximity Read ↗

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

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.Semantic proximity Read ↗

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

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