Evidence map›Paper›PMID 41505387›Full record

ArticleRespiration; international review of thoracic diseases2026

Learning Shape-Sensing Robotic-Assisted Bronchoscopy after Mastering Advanced Image-Guided Navigation Bronchoscopy.

Aniek R C Bruinen, Roel L J Verhoeven, Gerjon Hannink, Erik H F M van der Heijden

Abstract read
In one paragraph

Article in Respiration; international review of thoracic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. RoboticERJ open research · 2026
    Article
  2. 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

4 authors.

Aniek R C BruinenDepartment of Pulmonary Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Roel L J VerhoevenDepartment of Pulmonary Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Gerjon HanninkDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Erik H F M van der HeijdenDepartment of Pulmonary Medicine, Radboud University Medical Center, Nijmegen, The Netherlands, erik.vanderheijden@radboudumc.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<p>Introduction: Shape-sensing robotic-assisted bronchoscopy (ssRAB) is a novel technique for the diagnosis of peripheral pulmonary lesions (PPLs). There are no prior studies who have assessed the learning curve using learning curve cumulative summation (LC-CUSUM) and cumulative summation (CUSUM) analyses with diagnostic yield (DY) as the main endpoint.

methodsWe performed a single-center analysis of the learning curve of ssRAB combined with cone beam computed tomography (CBCT) procedures for the diagnosis of PPL performed by two bronchoscopists experienced with 3D imaging-guided bronchoscopy techniques using (1) an LC-CUSUM followed by CUSUM analysis, (2) a CUSUM analysis, with strict DY as endpoint. We will compare these methods.

resultsA total of 131 patients with a median lesion size of 12 mm (9-18 mm) were navigated by two bronchoscopists. In the first method, the LC-CUSUM analysis indicated that both bronchoscopists were statistically declared proficient after 43 and 42 procedures, with subsequent CUSUM analysis confirming sustained performance thereafter. In the second method, CUSUM analysis revealed that both bronchoscopists were deemed in control during all procedures.

conclusionThe CBCT-enhanced ssRAB performance of bronchoscopists experienced with CBCT-guided navigation bronchoscopy was in control from the start. LC-CUSUM and CUSUM are useful tools for assessing the learning curve and procedural performance. While CUSUM indicated that performance of the experienced bronchoscopists were in control from the start, LC-CUSUM inherently assumes initial non-proficiency leading to a larger number of procedures required to establish proficiency with statistical certainty. In the diagnosis of PPL, there will always be variability in performance, which may be attributable to patient-specific characteristics. </p>.

Indexed as

BronchoscopyLearning CurveLung NeoplasmsRobotic Surgical ProceduresAgedCone-Beam Computed TomographyFemaleHumansImaging, Three-DimensionalMaleMiddle AgedDiagnostic yieldLearning curveLearning curve cumulative summationLung cancerNavigation bronchoscopyPeripheral pulmonary lesionsShape-sensing robotic-assisted bronchoscopy

Identifiers

PMID41505387
PMCPMC12885498

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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.