Evidence map›Paper›PMID 35959126›Full record

ReviewFrontiers in surgery2022

How effective is indocyanine green (ICG) in localization of malignant pulmonary nodules? A systematic review and meta-analysis.

Andreas Gkikas, Savvas Lampridis, Davide Patrini, Peter B Kestenholz, Marco Scarci, Fabrizio Minervini

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

13 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Review
  3. Use of indocyanine green in thoracic surgery: a literature review.Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery · 2026
    Review
  4. Article
  5. Enhancing the Photocatalytic Efficacy of g-CMolecules (Basel, Switzerland) · 2025
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
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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 at 5 institutions in 2 countries.

Andreas GkikasDepartment of General Surgery, Hillingdon Hospital, The Hillingdon Hospitals NHS Foundation Trust, London, United Kingdom.
Savvas LampridisDepartment of Thoracic Surgery, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
Davide PatriniDepartment of Thoracic Surgery, University College London Hospitals, London, United Kingdom.
Peter B KestenholzDepartment of Thoracic Surgery, Cantonal Hospital Lucerne, Lucerne, Switzerland.
Marco ScarciDepartment of Thoracic Surgery, Imperial College Healthcare NHS Trust, London, United Kingdom.
Fabrizio MinerviniDepartment of Thoracic Surgery, Cantonal Hospital Lucerne, Lucerne, Switzerland.
University of Lucerne · CHGuy's and St Thomas' NHS Foundation Trust · GBHillingdon Hospitals NHS Foundation Trust · GBImperial College Healthcare NHS Trust · GBRoyal London Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Video-Assisted and Robotic-Assisted techniques become constantly more prominent practice in thoracic surgery for lung cancer. Furthermore, the increased frequency in detection of small lung cancers makes the intra-operative identification of these cancers even more challenging. Indocyanine Green (ICG) is one of the most commonly used dyes that assists surgeons identify small lung cancers intra-operatively. Our study aimed to evaluate the effectiveness and safety of ICG in lung cancer detection. Methods: We performed a systematic review of the literature by screening the databases of MEDLINE, EMBASE, CENTRAL and Scopus until 30th April 2022 and the first 300 articles of Google Scholar for any suitable grey literature. We included any study that investigated the effectiveness of ICG in lung cancer detection. We excluded studies that explored the use of ICG only in identification of intersegmental planes, lymph node mapping, case reports and non-English articles. We aimed to perform a meta-analysis on test accuracy studies using hierarchical summary receiver operating characteristic (HSROC) and the bivariate random-effects models. In cases where the data for a localization technique was not sufficient for that analysis, it was presented with tables with narrative purposes. Each study was assessed for Risk of Bias (RoB) and Applicability using the QUADAS-2 tool. Results: We found 30 eligible studies that included a total of 1,776 patients who underwent ICG localization of pulmonary nodules. We identified three ICG localization techniques: CT-guided, endobronchial and intravenous. From the 30 studies, 13 investigated CT-guided localization, 12 explored an endobronchial method while 8 studies administered ICG intravenously the median reported success rate was 94.3% (IQR: 91.4%-100%) and 98.3% (IQR: 94%-100%) for the first two techniques respectively. Intravenous ICG lung cancer localization showed Sensitivity of 88% (95% CI: 59%-0.97%) and Specificity of 25% (95% CI: 0.04%-0.74%). There were 15.2% (150/989) patients who experienced complications from CT guided ICG localization. No ICG-related complications were reported in endobronchial or intravenous techniques. Conclusion: Our study provides a comprehensive review of the literature on ICG localization techniques for lung cancer. Current evidence suggests that ICG is boh effective and safe. Further prospective research with standardized protocols across multiple thoracic units is required in order to accurately validate these findings.

Indexed as

ICGIndocyianine greenlung malignancylung surgerypulmonary nodules

Identifiers

PMID35959126
PMCPMC9357917
OpenAlexW4287147701

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.