Evidence map›Paper›PMID 31856177›Full record

ArticlePloS one2019

4D perfusion CT of prostate cancer for image-guided radiotherapy planning: A proof of concept study.

Lucian Beer, Stephan H Polanec, Pascal A T Baltzer, Georg Schatzl, Dietmar Georg, Christian Schestak, Anja Dutschke, Harald Herrmann, Peter Mazal, Alexander K Brendel and 4 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
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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

14 authors at 4 institutions in 3 countries.

Lucian BeerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0003-4388-7580
Stephan H PolanecDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Pascal A T BaltzerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0002-3755-5398
Georg SchatzlDepartment of Urology, Medical University of Vienna, Vienna, Austria.
Dietmar GeorgDepartment of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
Christian SchestakDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Anja DutschkeDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Harald HerrmannDepartment of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
Peter MazalClinical Institute of Pathology, Medical University of Vienna, Vienna, Austria.
Alexander K BrendelClinical Institute of Pathology, Medical University of Vienna, Vienna, Austria.
Shahrokh F ShariatDepartment of Urology, Medical University of Vienna, Vienna, Austria.
Helmut RinglDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Thomas H HelbichDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Paul ApfaltrerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Medical University of Vienna · ATCancer Research UK Cambridge Center · GBChristian Doppler Laboratory for Thermoelectricity · ATHeidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAdvanced forms of prostate cancer (PCa) radiotherapy with either external beam therapy or brachytherapy delivery techniques aim for a focal boost and thus require accurate lesion localization and lesion segmentation for subsequent treatment planning. This study prospectively evaluated dynamic contrast-enhanced computed tomography (DCE-CT) for the detection of prostate cancer lesions in the peripheral zone (PZ) using qualitative and quantitative image analysis compared to multiparametric magnet resonance imaging (mpMRI) of the prostate.

methodsWith local ethics committee approval, 14 patients (mean age, 67 years; range, 57-78 years; PSA, mean 8.1 ng/ml; range, 3.5-26.0) underwent DCE-CT, as well as mpMRI of the prostate, including standard T2, diffusion-weighted imaging (DWI), and DCE-MRI sequences followed by transrectal in-bore MRI-guided prostate biopsy. Maximum intensity projections (MIP) and DCE-CT perfusion parameters (CTP) were compared between healthy and malignant tissue. Two radiologists independently rated image quality and the tumor lesion delineation quality of PCa using a five-point ordinal scale. MIP and CTP were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/area under the curve (AUC) analysis.

resultsThe PCa detection rate ranged between 57 to 79% for the two readers for DCE-CT and was 92% for DCE-MRI. DCE-CT perfusion parameters in PCa tissue in the PZ were significantly different compared to regular prostate tissue and benign lesions. Image quality and lesion visibility were comparable between DCE-CT and DCE-MRI (VGC: AUC 0.612 and 0.651, p>0.05).

conclusionOur preliminary results suggest that it is feasible to use DCE-CT for identification and visualization, and subsequent segmentation for focal radiotherapy approaches to PCa.

Indexed as

AgedBiopsy, Large-Core NeedleContrast MediaDiffusion Magnetic Resonance ImagingFeasibility StudiesFour-Dimensional Computed TomographyHumansImage-Guided BiopsyMaleMiddle AgedNeoplasm GradingPerfusion ImagingProof of Concept StudyProspective StudiesProstateProstatic NeoplasmsContrast Media

Identifiers

PMID31856177
PMCPMC6922381
OpenAlexW2995047179

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.