Evidence map›Paper›PMID 37819450›Full record

ArticlePhysical and engineering sciences in medicine2023

Prostate volume analysis in image registration for prostate cancer care: a verification study.

Jessica M Bugeja, Georges Mehawed, Matthew J Roberts, Nicholas Rukin, Jason Dowling, Rebecca Murray

Open access · hybridAbstract read
In one paragraph

Article in Physical and engineering sciences in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Jessica M BugejaAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Herston, Australia. Jess.Bugeja@csiro.au.ORCID http://orcid.org/0000-0003-3631-969X
Georges MehawedHerston Biofabrication Institute, Urology Program, Herston, Australia.
Matthew J RobertsHerston Biofabrication Institute, Urology Program, Herston, Australia.
Nicholas RukinHerston Biofabrication Institute, Urology Program, Herston, Australia.
Jason DowlingAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Herston, Australia.
Rebecca MurrayHerston Biofabrication Institute, Urology Program, Herston, Australia.
The University of Queensland · AUCommonwealth Scientific and Industrial Research Organisation · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

Indexed as

ProstateProstatic NeoplasmsHumansMalePelvisPositron Emission Tomography Computed TomographyTomography, X-Ray ComputedCancer careComputed tomographyImage registrationMagnetic resonance imagingProstateUrology

Identifiers

PMID37819450
PMCPMC10703743
OpenAlexW4387531984

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.