biostudies-literatureZhao XThe research was funded by Tianjin Key Medical Discipline (Specialty) Construction ProjectTianjin Health Research Project167https://www.ebi.ac.uk/biostudies/studies/S-EPMC11654080biostudies-literatureUnknown24(1)<h4>Objective</h4>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.<h4>Methods</h4>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 mmCancer imaging : the official publication of the International Cancer Imaging SocietyPreoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.PMC11654080TJWJ2024QN063TJYXZDXK-018ALv JLi XDing YXu MRen JZhang HSun DXue MWang YWang ZWang KZhao XfalsePreoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.<h4>Objective</h4>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.<h4>Methods</h4>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 mm2024-01-01T00:00:00Z2024 Dec2025-04-04T01:52:05.551Z2025-04-04T01:52:05.551ZS-EPMC116540803969665910.1186/s40644-024-00813-5