{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":[null],"pubmed_abstract":["<h4>Purpose</h4> This study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs). <h4>Methods</h4> A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) al"],"journal":["Frontiers in oncology"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9709341"],"repository":["biostudies-literature"],"pubmed_title":["A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules"],"pmcid":["PMC9709341"],"additional_accession":[]},"is_claimable":false,"name":"A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules","description":"<h4>Purpose</h4> This study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs). <h4>Methods</h4> A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) al","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-04T19:37:51.303Z","creation":"2025-02-19T04:33:56.922Z"},"accession":"S-EPMC9709341","cross_references":{}}