Evidence map›Paper›PMID 41249835›Full record

ArticleNPJ digital medicine2025

Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory.

Zhen-Bin Qiu, Jiaqi Li, Shihua Dou, Qiuchen Meng, Meng-Min Wang, Hong-Ji Li, Chao Zhang, Hongsheng Xie, Ben-Yuan Jiang, Jun-Tao Lin and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Zhen-Bin Qiu *Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Jiaqi Li *Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China.
Shihua Dou *Department of Thoracic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, the First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases), Shenzhen, China.
Qiuchen MengBioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China.
Meng-Min WangGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Hong-Ji LiGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Chao ZhangGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Hongsheng XieDepartment of Thoracic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, the First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases), Shenzhen, China.
Ben-Yuan JiangGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Jun-Tao LinGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Jia-Tao ZhangGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Fang-Ping XuDepartment of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Jin-Hai YanDepartment of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Lei WeiBioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China.
Yi-Long WuGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Haibo WangClinical Trial Unit, Precision Medicine Institute, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Lin YangDepartment of Thoracic Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, the First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases), Shenzhen, China. 13798314779@163.com.
Xuegong ZhangBioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China. zhangxg@tsinghua.edu.cn.
Wen-Zhao ZhongGuangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. syzhongwenzhao@scut.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2019B1515130002Guangdong Provincial Key Laboratory of Lung Cancer Translational Medicine 2017B030314120High-level Hospital Construction Project of Guangdong Provincial People's Hospital DFJH201801National Key R&D Program of China 2021YFF1200901Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0529400the National Science Foundation of China 82241235the National Science Foundation of China 92470105Tsinghua-Fuzhou Institute of Data Technology Project JIDT2024022
6 · The paper itself

Abstract

Determining tumor progression status is critical for early-stage lung adenocarcinoma (esLUAD) diagnosis and treatment, yet histopathology-based grading often overlooks heterogeneity within grades. We propose RadioTrace, a deep contrastive learning framework integrating radiomic and pathological information to learn a radiomic trajectory for quantifying esLUAD progression. Across four multi-institutional cohorts, RadioTrace well predicted tumor phenotypes including spread through air spaces (STAS) and lymph node metastasis (LNM). Survival analyses demonstrated it as an independent prognostic factor (log-rank test p < 0.004 across all cohorts). Within the same pathological grade, it revealed significant survival heterogeneity (p < 0.02 across all cohorts), underscoring the limitations of current grading criteria. Genomic and transcriptomic analyses confirmed associations with progression-related molecular features. Longitudinal analysis of patients with multiple CT follow-ups further showed consistency with continuous progression. These findings demonstrate that RadioTrace enables quantitative, interpretable assessment of esLUAD progression, providing insights beyond histopathology and assisting clinical decision-making.

Identifiers

PMID41249835
PMCPMC12623993

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.