Evidence map›Paper›PMID 39958216›Full record

ArticleTranslational lung cancer research2025

The utility and feasibility of three-dimensional reconstruction in surgical planning for multiple pulmonary nodules: a prospective self-controlled study.

Zhifei Li, Runze Li, Lei Liu, Peng Li, Yue Peng, Yuichi Saito, Alberto Salvicchi, Satoshi Fumimoto, Bin Qiu, Fengwei Tan and 3 more

Abstract read
In one paragraph

Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

Who cites it

6 citing papers in PubMed.

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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

13 authors.

Zhifei Li *Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Runze Li *Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lei Liu *Department of Thoracic Surgery, Chengde Central Hospital, The Second Clinical College of Chengde Medical University, Chengde, China.
Peng Li *Department of Cardiothoracic Surgery, Zibo First Hospital, Shandong Second Medical University, Zibo, China.
Yue PengDepartment of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Yuichi SaitoDepartment of Surgery, Teikyo University School of Medicine, Tokyo, Japan.
Alberto SalvicchiThoracic Surgery Unit, Careggi University Hospital, Florence, Italy.
Satoshi FumimotoDepartment of Thoracic and Cardiovascular Surgery, Osaka Medical College, Osaka, Japan.
Bin QiuDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Fengwei TanDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Qi XueDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Shugeng GaoDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jie HeDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Three-dimensional (3D) is a useful tool for visualizing computed tomography (CT) scans, facilitating a clearer understanding of the spatial relationships between pulmonary lesions and the branching architecture of the bronchi and blood vessels. Several studies have demonstrated the benefit of preoperative 3D reconstruction in terms of intraoperative safety and short-term surgical outcomes after thoracoscopic surgery. However, evidence regarding the impact of 3D reconstruction on surgical planning of anatomical partial lobectomy (APL) is still lacking. This study aimed to evaluate the effect of 3D reconstruction in surgical planning and decision making compared with conventional clinical imaging. Methods: This prospective self-controlled study included 49 patients with multiple lung nodules (MLNs). A 3D reconstruction model was created using thin-slice CT images with Mimics 21.0 software. Two novice surgeons and two experienced surgeons respectively conducted surgical planning based on the CT images and 3D reconstruction sequentially. The surgical plans based on the CT images and 3D reconstruction were compared with those after the evaluation of intraoperative inspection. Results: Among the 49 participants enrolled to undergo sublobar resection (the patient level), 3D reconstruction significantly increased the surgical planning success rates both in novice surgeons (Surgeon A: 40.8% to 87.8%; Surgeon B: 49.0% to 89.8%) and experienced surgeons (Surgeon C: 83.7% to 100%; Surgeon D: 75.5% to 95.9%). For the entire 101 pulmonary nodules determined suitable for APL (the nodule level), all surgeons had higher surgical planning success rates with the help of 3D reconstruction and all success rates were above 90%. The gap in surgical planning success rates between novice surgeons and senior surgeons was closed. All patients were operated without intraoperative accident and without major postoperative complications. Conclusions: The surgical planning success rates of both novice surgeons and experienced surgeons were significantly increased with the help of 3D reconstruction and reached a high level. 3D reconstruction also closed the gap of surgical planning between novice surgeons and experienced surgeons at patient level.

Indexed as

anatomical partial lobectomy (APL)multiple lung nodules (MLNs)non-small cell lung cancer (NSCLC)sublobar resectionThree-dimensional reconstruction (3D reconstruction)

Identifiers

PMID39958216
PMCPMC11826287

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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.