Evidence map›Paper›PMID 42865858›Full record

ArticleMedComm2026

Transformer-Based Preoperative Invasiveness Identification and Postoperative Recurrence Risk Stratification in Stage T1N0M0 Lung Adenocarcinoma: A Multi-Center Cohort Study.

Xueyun Tan, Hui Xia, Delong Li, Meifang Wang, Na Zhan, Yan Huang, Mengyuan Liang, Pei Ma, Xiao Wang, Yaqi Cao and 7 more

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Article in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Xueyun Tan *Department of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Hui Xia *Department of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Delong Li *Hubei Key Laboratory of Optical Information and Pattern Recognition School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan China.
Meifang Wang *Department of Respiratory and Critical Care Medicine Shiyan Key Laboratory of Major Chronic Respiratory Disease, Taihe Hospital Hubei University of Medicine Shiyan China.
Na Zhan *Department of Pathology Renmin Hospital of Wuhan University Wuhan China.
Yan Huang *Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Xiangyang China.
Mengyuan Liang *Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital Zhengzhou University Zhengzhou China.
Pei Ma *Department of Respirology Guangzhou Institute of Respiratory Health The First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Diseases Guangzhou China.
Xiao WangDepartment of Respiratory and Critical Care Medicine Shiyan Key Laboratory of Major Chronic Respiratory Disease, Taihe Hospital Hubei University of Medicine Shiyan China.
Yaqi CaoDepartment of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Linpeng ShiDepartment of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Liang ZhangDepartment of Radiology Renmin Hospital of Wuhan University Wuhan China.
Xiaoling LiuInstitute of Research and Clinical Innovations Neusoft Medical Systems Co. Ltd Wuhan China.
Lian YangDepartment of Radiology Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Quan KongHubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China.
Sufei WangDepartment of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Yang JinDepartment of Respiratory and Critical Care Medicine Hubei Province Clinical Research Center for Major Respiratory Disease Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.

Funding

Neural Correlates of Risk Taking in Smokers before and after Smoking AbstinenceR21DA032022 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI RAO, HENGYI · 2012 to 2013
$410k
NIDA NIH HHS R21 DA032022
6 · The paper itself

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.

Indexed as

invasivenesslung adenocarcinomamultimodal informationrecurrence risktransformer

Identifiers

PMID42865858
PMCPMC13655376

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.