Back to search

Article

Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation

2022-02-23

Abstract excerpt

<h4>Objective: </h4> This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination model...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
85d2d637-8f3c-56ff-b42a-447b0c5f591b
DOI
10.21203/rs.3.rs-1100268/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutationDOI 10.21203/rs.3.rs-1100268/v1
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation.Semantic proximity
CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR PathwaySemantic proximity
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
The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung AdenocarcinomaSemantic proximity
Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLCSemantic proximity
A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.Semantic proximity
Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients.Semantic proximity
Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinomaSemantic proximity
Establishment of prognostic nomograms for predicting the progression free survival of EGFR-sensitizing mutation, advanced lung cancer patients treated with EGFR-TKIs.Semantic proximity
Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients.Semantic proximity
Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma.Semantic proximity
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signaturesSemantic proximity
Select a neighboring publication to make it the new centre.