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

Radiomic Detection of EGFR Mutations in NSCLC.

Cancer research - 1 Feb 2021

Rossi Giovanni, Barabino Emanuele, Fedeli Alessandro, Ficarra Gianluca, Coco Simona, Russo Alessandro, Adamo Vincenzo, Buemi Francesco, Zullo Lodovica, Dono Mariella, De Luca Giuseppa, Longo Luca, Dal Bello Maria Giovanna, Tagliamento Marco, Alama Angela, Cittadini Giuseppe, Pronzato Paolo, Genova Carlo

Abstract excerpt

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non-small cell lung cancer (NSCLC)....

Read the complete abstract on PubMed

Topics

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

Share to Adenocarcinoma
Share to Area Under Curve

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Carcinoma, Non-Small-Cell Lung

Share to ErbB Receptors

Share to Machine Learning

Share to Reproducibility of Results

Share to ROC Curve

Share to Tomography, X-Ray Computed

Source

Identifiers

PMID: 33148663
DOI: 10.1158/0008-5472.CAN-20-0999

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Radiomic Detection of EGFR Mutations in NSCLC.PMID 33148663Read ↗

Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.Semantic proximity Read ↗

Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.Semantic proximity Read ↗

Using CT image-based radiomics to predict EGFR mutations in lung adenocarcinomaSemantic proximity Read ↗

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

Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.Semantic proximity Read ↗

An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.Semantic proximity Read ↗

Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer.Semantic proximity Read ↗

CT-based non-invasive identification of the most common gene mutation status in patients with non-small cell lung cancer.Semantic proximity Read ↗

Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC.Semantic proximity Read ↗

Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-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 ↗

Development and Validation of Machine Learning Models to Predict Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer: A Multi-Center Retrospective Radiomics Study.Semantic proximity Read ↗

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