Evidence map›Paper›PMID 29589258›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy.

Shekoofeh Azizi, Nathan Van Woudenberg, Samira Sojoudi, Ming Li, Sheng Xu, Emran M Abu Anas, Pingkun Yan, Amir Tahmasebi, Jin Tae Kwak, Baris Turkbey and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
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

15 authors.

Shekoofeh AziziThe University of British Columbia, Vancouver, BC, Canada. shazizi@ece.ubc.ca.ORCID http://orcid.org/0000-0002-7447-6031
Nathan Van WoudenbergThe University of British Columbia, Vancouver, BC, Canada.
Samira SojoudiThe University of British Columbia, Vancouver, BC, Canada.
Ming LiNational Institutes of Health, Bethesda, MD, USA.
Sheng XuNational Institutes of Health, Bethesda, MD, USA.
Emran M Abu AnasJohns Hopkins University, Baltimore, MD, USA.
Pingkun YanRensselaer Polytechnic Institute, Troy, NY, USA.
Amir TahmasebiPhilips Research North America, Cambridge, MA, USA.
Jin Tae KwakSejong University, Gwangjin-gu, Seoul, Korea.
Baris TurkbeyNational Institutes of Health, Bethesda, MD, USA.
Peter ChoykeNational Institutes of Health, Bethesda, MD, USA.
Peter PintoNational Institutes of Health, Bethesda, MD, USA.
Bradford WoodNational Institutes of Health, Bethesda, MD, USA.
Parvin MousaviQueen's University, Kingston, ON, Canada.
Purang AbolmaesumiThe University of British Columbia, Vancouver, BC, Canada.

Funding

Prostate Cancer ImagingZIABC010655 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CHOYKE, PETER L · 2009 to 2025
$49.2M
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Intramural NIH HHS ZIA BC010655
6 · The paper itself

Abstract

purposeWe have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation.

methodsIn this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client-server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution.

resultsThe system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85.

conclusionOur results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Indexed as

AlgorithmsBiopsy, Large-Core NeedleComputer SystemsHumansImage-Guided BiopsyMaleProstatic NeoplasmsSensitivity and SpecificityUltrasonography, Interventional3D slicerProstate cancerReal-time biopsy guidanceTemporal enhanced ultrasound

Identifiers

PMID29589258
PMCPMC6436916

What OpenQuestion holds

Textmetadata
LicenceTDM
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.