Evidence map›Paper›PMID 39040587›Full record

ArticleERJ open research2024

The incremental contribution of mobile cone-beam computed tomography to the tool-lesion relationship during shape-sensing robotic-assisted bronchoscopy.

Bryan C Husta, Anu Menon, Reza Bergemann, I-Hsin Lin, Jaclyn Schmitz, Rastko Rakočević, Tejaswi R Nadig, Prasad S Adusumilli, Jason A Beattie, Robert P Lee and 5 more

Abstract read
In one paragraph

Article in ERJ open research, 2024. 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
–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

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.

  1. Trial
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  7. VirtualJournal of thoracic disease · 2025
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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

15 authors.

Bryan C HustaSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID https://orcid.org/0000-0001-9462-7477
Anu MenonSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Reza BergemannSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
I-Hsin LinDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Jaclyn SchmitzDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Rastko RakočevićSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Tejaswi R NadigSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Prasad S AdusumilliThoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID https://orcid.org/0000-0002-1699-2046
Jason A BeattieSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Robert P LeeSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Bernard J ParkThoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID https://orcid.org/0000-0003-3814-5144
Gaetano RoccoThoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Matthew J BottThoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID https://orcid.org/0000-0003-0523-4028
Mohit ChawlaSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Or Kalchiem-DekelSection of Interventional Pulmonology, Pulmonary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID https://orcid.org/0000-0001-9856-627X

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Introduction: This study aims to answer the question of whether adding mobile cone-beam computed tomography (mCBCT) imaging to shape-sensing robotic-assisted bronchoscopy (ssRAB) translates into a quantifiable improvement in the tool-lesion relationship. Methods: Data from 102 peripheral lung lesions with ≥2 sequential mCBCT orbital spins and from 436 lesions with 0-1 spins were prospectively captured and retrospectively analysed. The primary outcome was the tool-lesion relationship status across the first and the last mCBCT spins. Secondary outcomes included 1) the change in distance between the tip of the sampling tool and the centre of the lesion between the first and the last spins and 2) the per-lesion diagnostic yield. Results: Compared to lesions requiring 0-1 spins, lesions requiring ≥2 spins were smaller and had unfavourable bronchus sign and intra-operative sonographic view. On the first spin, 54 lesions (53%) were designated as non-tool-in-lesion (non-TIL) while 48 lesions (47%) were designated as TIL. Of the 54 initially non-TIL cases, 49 (90%) were converted to TIL status by the last spin. Overall, on the last spin, 96 out of 102 lesions (94%) were defined as TIL and six out of 102 lesions (6%) were defined as non-TIL (p<0.0001). The mean distance between the tool and the centre of the lesion decreased from 10.4 to 6.6 mm between the first and last spins (p<0.0001). The overall diagnostic yield was 77%. Conclusion: Targeting traditionally challenging lung lesions, intra-operative volumetric imaging allowed for the conversion of 90% of non-TIL status to TIL. Guidance with mCBCT resulted in a significant decrease in the distance between the tip of the needle to lesion centre.

Identifiers

PMID39040587
PMCPMC11261373

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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.