{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Weng Q"],"pubmed_abstract":["<h4>Objectives</h4>To develop and validate a radiomic feature-based nomogram for preoperative discriminating the epidermal growth factor receptor (EGFR) activating mutation from wild-type EGFR in non-small cell lung cancer (NSCLC) patients.<h4>Material</h4>A group of 301 NSCLC patients were retrospectively reviewed. The EGFR mutation status was determined by ARMS PCR analysis. All patients underwent nonenhanced CT before surgery. Radiomic features were extracted (GE healthcare). The maximum relevance minimum redundancy (mRMR) and LASSO, were used to select features. We incorporated the independent clinical features into the radiomic feature model and formed a joint model (i.e., the radiomic feature-based nomogram). The performance of the joint model was compared with that of the other two "],"journal":["Frontiers in oncology"],"pagination":["590937"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8377542"],"repository":["biostudies-literature"],"pubmed_title":["Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy."],"pmcid":["PMC8377542"],"pubmed_authors":["Weng Q","Lan C","Xu M","Zhao C","Huang J","Chen M","Bao Y","Lu C","Ji J","Hui J","Wang H","Tu J","Pang P","Huang Y","Zheng L","Mao W","Fang S","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy.","description":"<h4>Objectives</h4>To develop and validate a radiomic feature-based nomogram for preoperative discriminating the epidermal growth factor receptor (EGFR) activating mutation from wild-type EGFR in non-small cell lung cancer (NSCLC) patients.<h4>Material</h4>A group of 301 NSCLC patients were retrospectively reviewed. The EGFR mutation status was determined by ARMS PCR analysis. All patients underwent nonenhanced CT before surgery. Radiomic features were extracted (GE healthcare). The maximum relevance minimum redundancy (mRMR) and LASSO, were used to select features. We incorporated the independent clinical features into the radiomic feature model and formed a joint model (i.e., the radiomic feature-based nomogram). The performance of the joint model was compared with that of the other two ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021","modification":"2025-06-01T00:56:20.407Z","creation":"2022-02-11T09:14:22.914Z"},"accession":"S-EPMC8377542","cross_references":{"pubmed":["34422624"],"doi":["10.3389/fonc.2021.590937"]}}