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

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

Laboratory investigation; a journal of technical methods and pathology - 1 Aug 2024

Zhang Wanqiu, Wang Wei, Xu Yao, Wu Kun, Shi Jun, Li Ming, Feng Zhengzhong, Liu Yinhua, Zheng Yushan, Wu Haibo

Abstract excerpt

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-small cell lung...

Read the complete abstract on PubMed

Topics

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

Share to Aged
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 Lung Neoplasms

Share to Mutation

Source

Identifiers

PMID: 38871058
DOI: 10.1016/j.labinv.2024.102094

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.PMID 38871058Read ↗

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

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

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

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

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

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

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.Semantic proximity Read ↗

Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural NetworksSemantic proximity Read ↗

Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networksSemantic proximity Read ↗

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

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