Evidence map›Paper›PMID 41652379›Full record

ArticleBMC medical imaging2026

ICG combined with medical adhesive in preoperative localization of complex pulmonary nodules: a retrospective study.

Wenhao Wang, Yulong Tan, Dong Xu, Haoxin Liu, Yifeng Qian, Huijun Zhang, Meng Shi, Xiaofeng Chen

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Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Wenhao Wang *Department of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China.
Yulong Tan *Department of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China.
Dong Xu *Department of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China.
Haoxin LiuDepartment of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China.
Yifeng QianDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Huijun ZhangDepartment of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China. zhanghuijunhp@163.com.
Meng ShiDepartment of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China. mengshi@fudan.edu.cn.
Xiaofeng ChenDepartment of Thoracic Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, China. xiaofengchen@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecise pulmonary nodule localization, especially for deep-seated or pleural-adherent lesions, remains challenging. Conventional methods face accuracy limitations and high complication rates. This study validates a CT-guided indocyanine green (ICG)-medical adhesive composite, leveraging fluorescence stability and adhesive retention to optimize video-assisted thoracoscopic surgery (VATS) precision in complex anatomical scenarios.

methodsThis retrospective single-center study evaluated a novel CT-guided preoperative localization technique combining ICG with medical adhesive in 262 patients (288 nodules). Preoperative localization was performed under CT guidance using a composite mixture of ICG and medical adhesive. The mixture was injected within 15 mm of the target nodule, and VATS was conducted with intraoperative fluorescence imaging guidance.

resultsTechnical success was achieved in all cases (100%), with a mean procedural duration of 12.3 ± 4.2 min. Complications included mild pneumothorax (7.3%), self-limited hemorrhage (5.3%), and mild cough (8.0%), resolving spontaneously. The method successfully localized 57 nodules ≥ 2 cm from the pleura and 28 cases with pleural adhesions (including 7 total pleural adhesions). All nodules were resected without thoracotomy conversion. The fluorescence signal remained detectable for a maximum duration of 8 days post-localization, enabling flexible surgical scheduling.

conclusionsICG-medical adhesive localization offers a safe, effective, and adaptable strategy for preoperative pulmonary nodule localization, particularly in anatomically challenging cases. Its low complication rates, sustained fluorescence, and reliability in complex scenarios support broader clinical adoption to enhance VATS precision.

Indexed as

Coloring AgentsIndocyanine GreenLung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective StudiesThoracic Surgery, Video-AssistedTomography, X-Ray ComputedColoring AgentsIndocyanine GreenCombinationIndocyanine greenMedical adhesivePulmonary nodulesVideo-assisted thoracoscopic surgery

Identifiers

PMID41652379
PMCPMC12973608

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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.