Evidence map›Paper›PMID 38890223›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Characterizing the accuracy of robotic bronchoscopy in localization & targeting of small pulmonary lesions.

Jessica Copeland, Mehida Rojas-Alexandre, Lilian Tsai, Franklin King, Nobuhiko Hata

Abstract readComparative Study
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2024. 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

5 authors.

Jessica CopelandDivision of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. jessmcopeland@gmail.com.ORCID http://orcid.org/0000-0002-6903-6445
Mehida Rojas-AlexandreDivision of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Lilian TsaiDivision of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Franklin KingDivision of Thoracic Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Nobuhiko HataDivision of Thoracic Surgery, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeConsidering the recent implementation of lung cancer screening guidelines, it is crucial that small pulmonary nodules are accurately diagnosed. There is a significant need for quick, precise, and minimally invasive biopsy methods, especially for patients with small lung lesions in the outer periphery. Robotic bronchoscopy (RB) has recently emerged as a novel solution. The purpose of this study was to evaluate the accuracy of RB compared to the existing standard, electromagnetic navigational bronchoscopy (EM-NB).

methodsA prospective, single-blinded, and randomized-controlled study was performed to compare the accuracy of RB to EM-NB in localizing and targeting pulmonary lesions in a porcine lung model. Four operators were tasked with navigating to four pulmonary targets in the outer periphery of a porcine lung, to which they were blinded, using both the RB and EM-NB systems. The dependent variable was accuracy. Accuracy was measured as a rate of success in lesion localization and targeting, the distance from the center of the pulmonary target, and by anatomic location. The independent variable was the navigation system, RB was compared to EM-NB using 1:1 randomization.

resultsOf 75 attempts, 72 were successful in lesion localization and 60 were successful in lesion targeting. The success rate for lesion localization was 100% with RB and 91% with EM- NB. The success rate for lesion targeting was 93% with RB and 80% for EM-NB. RB demonstrated superior accuracy in reaching the distance from the center of the lesion, at 0.62 mm compared to EM-NB at 1.28 mm (p = 0.001). Accuracy was improved using RB compared to EM- NB for lesions in the LLL (p = 0.025), LUL (p < 0.001), and RUL (p < 0.001).

conclusionOur findings support RB as a more accurate method of navigating and localizing small peripheral pulmonary targets when compared to standard EM-NB in a porcine lung model. This may be attributed to the ability of RB to reduce substantial tissue displacement seen with standard EM-NB navigation. As the development and application of RB advances, so will the ability to accurately diagnose small peripheral lung cancer nodules, providing patients with early-stage lung cancer the best possible outcomes.

Indexed as

BronchoscopyLung NeoplasmsRobotic Surgical ProceduresAnimalsDisease Models, AnimalProspective StudiesSingle-Blind MethodSwineElectromagnetic bronchoscopyInterventional bronchoscopyLung cancerNavigational bronchoscopyRobotic bronchoscopySmall pulmonary lung nodules

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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.