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

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.

BioMed research international - 1 Jan 2022

Du Tianming, Zhao Haidong

Abstract excerpt

Objective: Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides...

Read the complete abstract on PubMed

Topics

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

Share to Breast Neoplasms
Share to ErbB Receptors
Share to Magnetic Resonance Imaging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Source

Open in PubMed Open full text

Identifiers

PMID: 35663041
DOI: 10.1155/2022/1376659
PMCID: PMC9162871

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

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.PMID 35663041Read ↗

Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data.Semantic proximity Read ↗

Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms.Semantic proximity Read ↗

TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer.Semantic proximity Read ↗

CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statusesSemantic proximity Read ↗

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data setSemantic proximity Read ↗

Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer.Semantic proximity Read ↗

Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.Semantic proximity Read ↗

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancerSemantic proximity Read ↗

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review.Semantic proximity Read ↗

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.Semantic proximity Read ↗

The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors StatusSemantic proximity Read ↗

FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma.Semantic proximity Read ↗

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