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

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.

Asia-Pacific journal of clinical oncology - 1 Dec 2022

Zhu Huiyuan, Song Yueqiang, Huang Zike, Zhang Lian, Chen Yanqing, Tao Guangyu, She Yunlang, Sun Xiwen, Yu Hong

Abstract excerpt

OBJECTIVES: To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early-stage lung adenocarcinomas. METHODS: A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to...

Read the complete abstract on PubMed

Topics

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

Share to Adenocarcinoma of Lung
Share to Aged

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Emphysema

Share to ErbB Receptors

Share to Lung Neoplasms

Share to Mutation

Share to Tomography, X-Ray Computed

Source

Identifiers

PMID: 35098682
DOI: 10.1111/ajco.13641

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features.PMID 35098682Read ↗

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung AdenocarcinomaSemantic proximity Read ↗

Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning.Semantic proximity Read ↗

A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma.Semantic proximity Read ↗

Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients.Semantic proximity Read ↗

CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR PathwaySemantic proximity Read ↗

Clinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer.Semantic proximity Read ↗

Prediction of EGFR mutation status in non-small cell lung cancer based on multiparametric MRI radiomics.Semantic proximity Read ↗

Prediction of EGFR Mutation Status in Non-small Cell Lung Cancer Based on Multiparametric MRI RadiomicsSemantic proximity Read ↗

Radiomic and clinical predictors of epidermal growth factor receptor mutation in stage IA non-small cell lung cancer.Semantic proximity Read ↗

The Predictive Value of 18F-FDG PET/CT Radiomics in EGFR Gene Mutation of Lung Adenocarcinoma.Semantic proximity Read ↗

A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.Semantic proximity Read ↗

Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma.Semantic proximity Read ↗

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