{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhao X"],"funding":["The research was funded by Tianjin Key Medical Discipline (Specialty) Construction Project","Tianjin Health Research Project"],"pagination":["167"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11654080"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["Objective
To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment.Methods
The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm"],"journal":["Cancer imaging : the official publication of the International Cancer Imaging Society"],"pubmed_title":["Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study."],"pmcid":["PMC11654080"],"funding_grant_id":["TJWJ2024QN063","TJYXZDXK-018A"],"pubmed_authors":["Lv J","Li X","Ding Y","Xu M","Ren J","Zhang H","Sun D","Xue M","Wang Y","Wang Z","Wang K","Zhao X"],"additional_accession":[]},"is_claimable":false,"name":"Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.","description":"Objective
To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment.Methods
The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-04T01:52:05.551Z","creation":"2025-04-04T01:52:05.551Z"},"accession":"S-EPMC11654080","cross_references":{"pubmed":["39696659"],"doi":["10.1186/s40644-024-00813-5"]}}