Evidence map›Paper›PMID 41360981›Full record

ArticleNature communications2025

Precise diagnosis of small invasive pulmonary nodules driven by single-cell immune signatures in peripheral blood.

Yang Xia, Yin Zhu, Sai Zhang, Mo Zhou, Jiaqi Zhou, Guanxin Xu, Baiqin Zhao, Guofei Zhang, Qiyuan Wang, Bin Wang and 8 more

Abstract readMulticenter Study
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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. Article
  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

18 authors.

Yang Xia *Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. yxia@zju.edu.cn.ORCID http://orcid.org/0000-0003-2487-2244
Yin Zhu *Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Sai Zhang *Department of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Mo ZhouKey Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jiaqi ZhouKey Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Guanxin XuDepartment of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Baiqin ZhaoDepartment of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-3097-3893
Guofei ZhangDepartment of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Qiyuan WangDepartment of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Bin WangDepartment of Respiratory and Critical Care Medicine, Huzhou Central Hospital, Huzhou, Zhejiang, China.
Zexin ChenCenter of Clinical Epidemiology & Biostatistics, Department of Scientific Research, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Hongyu ShiDepartment of Biological Testing, Zhejiang Puluoting Health Technology Co., Ltd, Hangzhou, Zhejiang, China.
Xiuning LeDepartment of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.ORCID http://orcid.org/0000-0002-8554-1185
Tony MokKey laboratory of Translational Oncology, Department of Clinical Oncology, Chinese University of Hong Kong, Hong Kong, China. tony@clo.cuhk.edu.hk.ORCID http://orcid.org/0000-0002-8251-0551
Wei ChenZhejiang University School of Medicine, Liangzhu Laboratory, Hangzhou, Zhejiang, China. jackweichen@zju.edu.cn.ORCID http://orcid.org/0000-0001-5366-7253
Junqiang FanDepartment of Thoracic Surgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. zrxwk@zju.edu.cn.ORCID http://orcid.org/0000-0002-6502-2556
Weiwei YinKey Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China. wwyin@zju.edu.cn.
Wen LiKey Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. liwen@zju.edu.cn.ORCID http://orcid.org/0000-0002-8981-8380

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82422001;82422001
6 · The paper itself

Abstract

Early detection of lung cancer is crucial for improving patient outcomes. However, accurately diagnosing invasive pulmonary nodules and predicting tumor invasiveness remain major clinical challenges. Given the established role of immune dysfunction in cancer development, we hypothesize that peripheral immune profiling could provide a strategy for managing pulmonary nodules. In this multi-center, prospective study, we combine peripheral immune profiling via mass cytometry with machine learning algorithms to develop an integrated pulmonary nodule management platform. This platform accurately distinguishes invasive from non-invasive pulmonary nodules (AUC = 0.952), outperforming established clinical and radiomics-based models. Furthermore, it effectively predicts tumor invasiveness, differentiating minimally invasive from invasive adenocarcinoma (AUC = 0.949), thereby offering valuable guidance for surgical decision-making. In conclusion, the platform demonstrates substantial clinical utility and holds significant promise as a precision tool for future management of pulmonary nodules.

Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesSingle-Cell AnalysisSolitary Pulmonary NoduleAgedFemaleHumansMachine LearningMaleMiddle AgedNeoplasm InvasivenessProspective Studies

Identifiers

PMID41360981
PMCPMC12686440

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.