Evidence map›Paper›PMID 40506493›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2025

[Advancements in Research on Preoperative Localization of Pulmonary Nodules].

Jialong Chen, Lei Zhou, Lingling Qin, Chunlai Liu

Abstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jialong ChenDepartment of Cardiothoracic Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
Lei ZhouDepartment of Cardiothoracic Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
Lingling QinDepartment of Cardiothoracic Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
Chunlai LiuDepartment of Cardiothoracic Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the widespread application of chest computed tomography (CT) screening has led to a significant increase in the detection rate of pulmonary nodules. As a critical diagnostic tool for early-stage lung cancer, video-assisted thoracic surgery (VATS) has emerged as the preferred therapeutic approach for pulmonary nodules. Clinical evidence demonstrates that precise preoperative localization significantly enhances surgical success rates (reducing conversion to thoracotomy), minimizes complications, and shortens operation time. This comprehensive review systematically evaluates six cutting-edge localization techniques: percutaneous puncture-assisted localization, electromagnetic navigation bronchoscopy (ENB) localization, 3D-printed auxiliary localization, basin-analysis-based localization, robotic navigation system localization, and mixed reality (MR)-guided localization. By critically analyzing their operational principles, efficacy, safety profiles, and clinical applicability, this paper aims to provide evidence-based recommendations for optimizing clinical decision-making in pulmonary nodule management.
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Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleHumansThoracic Surgery, Video-AssistedTomography, X-Ray ComputedComputed tomographyPulmonary noduleVideo-assisted thoracic surgery

Identifiers

PMID40506493
PMCPMC12171616

What OpenQuestion holds

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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.