Abstract
ABSTRACT
Surgery is the primary curative treatment for early‐stage lung adenocarcinoma (LUAD), with accurate identification of tumor invasiveness critical for determining pulmonary resection extent. Due to significant variability in postoperative recurrence among early‐stage LUAD patients with aggressive tumors, a more effective risk stratification system is urgently needed to improve prognosis. This study developed the T1LUT system, a transformer‐based tool for T1N0M0 LUAD management, covering preoperative tumor invasiveness identification and postoperative recurrence risk stratification. Preoperatively, a 3D Vision Transformer (ViT) with extra learnable tokens analyzes CT images. It outperforms 3D ViT with only CLS tokens, 2D ViT, and 3D ResNet across three independent test centers (Center D: 0.896 vs. 0.892, 0.879, 0.836; Center E: 0.906 vs. 0.837, 0.837, 0.835; Center F: 0.884 vs. 0.884, 0.881, 0.861), achieving higher mean AUROC to accurately assess invasiveness and guide surgical resection extent. Postoperatively, the multimodal IAC‐PR model integrates CT images, reports, pathological data, and clinical/demographic information. It stratifies invasive adenocarcinoma (IAC) patients into low‐, medium‐, or high‐risk groups with an AUROC of 0.807 in testing—significantly better than single/partial‐modal models. Validated via large‐scale multicenter research, this system overcomes traditional assessment limitations, offering a robust tool for precise early LUAD treatment and improved prognosis.