Evidence map›Paper›PMID 34532565›Full record

ArticleInformatics in medicine unlocked2021

Image-guided EBUS bronchoscopy system for lung-cancer staging.

Xiaonan Zang, Ronnarit Cheirsilp, Patrick D Byrnes, Trevor K Kuhlengel, Catherine Abendroth, Thomas Allen, Rickhesvar Mahraj, Jennifer Toth, Rebecca Bascom, William E Higgins

Open access · goldAbstract read
In one paragraph

Article in Informatics in medicine unlocked, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

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  6. Optimal route planning for image-guided EBUS bronchoscopy.Computers in biology and medicine · 2019
    Article
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

10 authors at 2 institutions in 1 country.

Xiaonan ZangSchool of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Ronnarit CheirsilpSchool of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Patrick D ByrnesSchool of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Trevor K KuhlengelSchool of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Catherine AbendrothPenn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.
Thomas AllenPenn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.
Rickhesvar MahrajPenn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.
Jennifer TothPenn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.
Rebecca BascomPenn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.
William E HigginsSchool of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Penn State Milton S. Hershey Medical Center · USPennsylvania State University · US

Funding

Multimodal Image-Guided Intervention System for Lung-Cancer Diagnosis and StagingR01CA151433 · NCI · PENNSYLVANIA STATE UNIVERSITY, THE · PI HIGGINS, WILLIAM EVAN · 2010 to 2022
$3.7M
NCI NIH HHS R01 CA151433
6 · The paper itself

Abstract

The staging of the central-chest lymph nodes is a major step in the management of lung-cancer patients. For this purpose, the physician uses a device that integrates videobronchoscopy and an endobronchial ultrasound (EBUS) probe. To biopsy a lymph node, the physician first uses videobronchoscopy to navigate through the airways and then invokes EBUS to localize and biopsy the node. Unfortunately, this process proves difficult for many physicians, with the choice of biopsy site found by trial and error. We present a complete image-guided EBUS bronchoscopy system tailored to lymph-node staging. The system accepts a patient's 3D chest CT scan, an optional PET scan, and the EBUS bronchoscope's video sources as inputs. System workflow follows two phases: (1) procedure planning and (2) image-guided EBUS bronchoscopy. Procedure planning derives airway guidance routes that facilitate optimal EBUS scanning and nodal biopsy. During the live procedure, the system's graphical display suggests a series of device maneuvers to perform and provides multimodal visual cues for locating suitable biopsy sites. To this end, the system exploits data fusion to drive a multimodal virtual bronchoscope and other visualization tools that lead the physician through the process of device navigation and localization. A retrospective lung-cancer patient study and follow-on prospective patient study, performed within the standard clinical workflow, demonstrate the system's feasibility and functionality. For the prospective study, 60/60 selected lymph nodes (100%) were correctly localized using the system, and 30/33 biopsied nodes (91%) gave adequate tissue samples. Also, the mean procedure time including all user interactions was 6 min 43 s All of these measures improve upon benchmarks reported for other state-of-the-art systems and current practice. Overall, the system enabled safe, efficient EBUS-based localization and biopsy of lymph nodes.

Indexed as

BronchoscopyEndobronchial ultrasoundImage-guided surgery systemsLung cancer stagingLymph nodesMultimodal imaging

Identifiers

PMID34532565
PMCPMC8443139
OpenAlexW3178994609

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.