Evidence map›Paper›PMID 40114937›Full record

ArticleTranslational lung cancer research2025

Ion robotic bronchoscopy laser ablation and Da Vinci robotic segmentectomy for bilateral pulmonary nodules: a case report.

Teng Sun, Yong Ge, Zhiqiao Chen, Tianyue Ma, Shoujie Feng, Hao Zhang

Abstract readCase Reports
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Teng Sun *Thoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.
Yong Ge *Thoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.
Zhiqiao ChenThoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.
Tianyue MaThoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.
Shoujie FengThoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.
Hao ZhangThoracic Surgery Laboratory, Xuzhou Medical University, Xuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The treatment strategy of multiple pulmonary nodules presents significant challenges in thoracic surgery, particularly regarding precise diagnosis and treatment. The integration of emerging technologies for concurrent hybrid diagnostic and therapeutic approaches represents a potential breakthrough. The purpose of this study is to provide a new paradigm for the synchronous mixed treatment of multiple pulmonary nodules. Case Description: A 34-year-old female patient was initially diagnosed with multiple pulmonary nodules in August 2023 and has been undergoing regular follow-up since then. A computed tomography (CT) in September 2024 showed a mixed ground-glass nodule (mGGN) in the dorsal segment (S6) of the left lower lobe of the lung, measuring approximately 11 mm × 7 mm, indicating a high risk of malignancy; and a ground-glass opacity (GGO) measuring 5 mm × 4 mm between the dorsal segment and anterior basal segment (S6 and S8) of the right lower lobe, with both nodules showing enlargement compared to a year ago. A simultaneous bilateral lung surgery is planned. The Ion robotic system was utilized to navigate precisely to the lesion along the B6b bronchus of the right lower lobe, where the lung nodule was ablated with a laser. Subsequently, the Da Vinci robotic system used to assist in the precise resection of the S6. Pathology on the left showed adenocarcinoma; a CT one week postoperatively showed a 1 cm patchy shadow with a cavity on the right. Conclusions: This report presents the first surgical technique for ion robotic-assisted laser ablation and dual-robot collaborative surgery, offering a novel strategy for the integration of airway diagnostic and therapeutic interventions.

Indexed as

case reporthybrid surgery strategyion robotic bronchoscopylaser ablationMultiple pulmonary nodules

Identifiers

PMID40114937
PMCPMC11921196

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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.