Evidence map›Paper›PMID 31362107›Full record

ArticleComputers in biology and medicine2019

Optimal route planning for image-guided EBUS bronchoscopy.

Xiaonan Zang, Jason D Gibbs, Ronnarit Cheirsilp, Patrick D Byrnes, Jennifer Toth, Rebecca Bascom, William E Higgins

Open access · greenAbstract read
In one paragraph

Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Two-phase radial endobronchial ultrasound bronchoscopy registration.Journal of medical imaging (Bellingham, Wash.) · 2025
    Article
  3. Article
  4. Endobronchial Ultrasound Image Simulation for Image-Guided Bronchoscopy.IEEE transactions on bio-medical engineering · 2023
    Article
  5. Efficient procedure planning for comprehensive lymph node staging bronchoscopy.Journal of medical imaging (Bellingham, Wash.) · 2022
    Article
  6. Article
  7. Article
  8. 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

7 authors at 2 institutions in 1 country.

Xiaonan ZangSchool of Electrical Engineering and Computer Science, USA; EDDA Technologies, Princeton, NJ, 08540, USA.
Jason D GibbsSchool of Electrical Engineering and Computer Science, USA; X-Nav Technologies, Lansdale, PA, 19446, USA.
Ronnarit CheirsilpSchool of Electrical Engineering and Computer Science, USA; Broncus Medical, San Jose, CA, USA.
Patrick D ByrnesSchool of Electrical Engineering and Computer Science, USA.
Jennifer TothDepartment of Medicine, Division of Pulmonary, Allergy, and Critical Care Penn State University, University Park and Hershey, PA, USA.
Rebecca BascomDepartment of Medicine, Division of Pulmonary, Allergy, and Critical Care Penn State University, University Park and Hershey, PA, USA.
William E HigginsSchool of Electrical Engineering and Computer Science, USA. Electronic address: weh2@psu.edu.
Penn State Milton S. Hershey Medical Center · USBroncus (United States) · 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 lung-cancer management procedure. To perform a staging procedure, the physician first uses a patient's 3D X-ray computed-tomography (CT) chest scan to interactively plan airway routes leading to selected target lymph nodes. Next, using an integrated EBUS bronchoscope (EBUS = endobronchial ultrasound), the physician uses videobronchoscopy to navigate through the airways toward a target node's general vicinity and then invokes EBUS to localize the node for biopsy. Unfortunately, during the procedure, the physician has difficulty in translating the preplanned airway routes into safe, effective biopsy sites. We propose an automatic route-planning method for EBUS bronchoscopy that gives optimal localization of safe, effective nodal biopsy sites. To run the method, a 3D chest model is first computed from a patient's chest CT scan. Next, an optimization method derives feasible airway routes that enables maximal tissue sampling of target lymph nodes while safely avoiding major blood vessels. In a lung-cancer patient study entailing 31 nodes (long axis range: [9.0 mm, 44.5 mm]), 25/31 nodes yielded safe airway routes having an optimal tissue sample size = 8.4 mm (range: [1.0 mm, 18.6 mm]) and sample adequacy = 0.42 (range: [0.05, 0.93]). Quantitative results indicate that the method potentially enables successful biopsies in essentially 100% of selected lymph nodes versus the 70-94% success rate of other approaches. The method also potentially facilitates adequate tissue biopsies for nearly 100% of selected nodes, as opposed to the 55-77% tissue adequacy rates of standard methods. The remaining nodes did not yield a safe route within the preset safety-margin constraints, with 3 nodes never yielding a route even under the most lenient safety-margin conditions. Thus, the method not only helps determine effective airway routes and expected sample quality for nodal biopsy, but it also helps point out situations where biopsy may not be advisable. We also demonstrate the methodology in an image-guided EBUS bronchoscopy system, used successfully in live lung-cancer patient studies. During a live procedure, the method provides dynamic real-time sample size visualization in an enhanced virtual bronchoscopy viewer. In this way, the physician vividly sees the most promising biopsy sites along the airway walls as the bronchoscope moves through the airways.

Indexed as

BronchoscopyDecision Making, Computer-AssistedLung NeoplasmsSurgery, Computer-AssistedTomography, X-Ray ComputedFemaleHumansMaleBronchoscopyChest CT imagingEndobronchial ultrasoundImage-guided surgery systemsLung cancerProcedure planning

Identifiers

PMID31362107
PMCPMC6820695
OpenAlexW2964473892

What OpenQuestion holds

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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.