Evidence map›Paper›PMID 32385597›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

Ultrasound 3D reconstruction of malignant masses in robotic-assisted partial nephrectomy using the PAF rail system: a comparison study.

Chongyun Wang, Charalampos Komninos, Stephanie Andersen, Claudia D'Ettorre, George Dwyer, Efthymios Maneas, Philip Edwards, Adrien Desjardins, Agostino Stilli, Danail Stoyanov

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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.

Chongyun WangWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
Charalampos KomninosDepartment of Electrical and Computer Engineering, University of Patras, 26504, Rio, Patras, Greece.
Stephanie AndersenDepartment of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
Claudia D'EttorreWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
George DwyerWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
Efthymios ManeasWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
Philip EdwardsWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
Adrien DesjardinsWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.
Agostino StilliWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK. a.stilli@ucl.ac.uk.
Danail StoyanovWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, 43-45 Foley St., Fitzrovia, London, W1W 7EJ, UK.

Funding

Engineering and Physical Sciences Research Council EP/P012841/1Engineering and Physical Sciences Research Council EP/P027938/1Engineering and Physical Sciences Research Council EP/R004080/1Royal Academy of Engineering CiET1819_2_36UCL Innovation and Enterprise EP/R511638/1Wellcome/EPSRC 203145Z/16/ZWellcome Trust
6 · The paper itself

Abstract

purposeIn robotic-assisted partial nephrectomy (RAPN), the use of intraoperative ultrasound (IOUS) helps to localise and outline the tumours as well as the blood vessels within the kidney. The aim of this work is to evaluate the use of the pneumatically attachable flexible (PAF) rail system for US 3D reconstruction of malignant masses in RAPN. The PAF rail system is a novel device developed and previously presented by the authors to enable track-guided US scanning.

methodsWe present a comparison study between US 3D reconstruction of masses based on: the da Vinci Surgical System kinematics, single- and stereo-camera tracking of visual markers embedded on the probe. An US-realistic kidney phantom embedding a mass is used for testing. A new design for the US probe attachment to enhance the performance of the kinematic approach is presented. A feature extraction algorithm is proposed to detect the margins of the targeted mass in US images.

resultsTo evaluate the performance of the investigated approaches the resulting 3D reconstructions have been compared to a CT scan of the phantom. The data collected indicates that single camera reconstruction outperformed the other approaches, reconstructing with a sub-millimetre accuracy the targeted mass.

conclusionsThis work demonstrates that the PAF rail system provides a reliable platform to enable accurate US 3D reconstruction of masses in RAPN procedures. The proposed system has also the potential to be employed in other surgical procedures such as hepatectomy or laparoscopic liver resection.

Indexed as

HumansImaging, Three-DimensionalLaparoscopyNephrectomyRobotic Surgical ProceduresTomography, X-Ray ComputedTreatment OutcomeUltrasonography, Interventional3D ultrasoundLaparoscopySoft roboticsSurgical robotics

Identifiers

PMID32385597
PMCPMC7316668

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.