Evidence map›Paper›PMID 32779056›Full record

ArticleAnnals of biomedical engineering2021

Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

Layan Nahlawi, Farhad Imani, Mena Gaed, Jose A Gomez, Madeleine Moussa, Eli Gibson, Aaron Fenster, Aaron Ward, Purang Abolmaesumi, Parvin Mousavi and 1 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Layan NahlawiSchool of Computing, Queen's University, Kingston, ON, K7L 2N8, Canada. layan.nahlawi@queensu.ca.ORCID http://orcid.org/0000-0002-3414-1376
Farhad ImaniDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Mena GaedLondon Health Sciences Centre, London, ON, Canada.
Jose A GomezLondon Health Sciences Centre, London, ON, Canada.
Madeleine MoussaLondon Health Sciences Centre, London, ON, Canada.
Eli GibsonUniversity College London, London, UK.
Aaron FensterRobarts Research Institute, London, ON, Canada.
Aaron WardDepartment of Medical Physics, Western University, London, ON, Canada.
Purang AbolmaesumiDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Parvin Mousavi *School of Computing, Queen's University, Kingston, ON, K7L 2N8, Canada.
Hagit Shatkay *School of Computing, Queen's University, Kingston, ON, K7L 2N8, Canada.

Funding

Subproject Title: Clinical Research Education, Mentoring and Career Development CoreU54GM104941 · NIGMS · UNIVERSITY OF DELAWARE · PI Gregory E Hicks · 2013 to 2026
$60.5M
Uncovering Clinical Evidence in COVID-19 Publications: An Integrated Search via Text & ImagesR01LM012527 · NLM · UNIVERSITY OF DELAWARE · PI EIGENMANN, RUDOLF, MARAI, GEORGETA-ELISABETA · 2017 to 2020
$1.9M
Utilizing Imaged-based Features in Biomedical Literature ClassificationR56LM011354 · NLM · UNIVERSITY OF DELAWARE · PI SHATKAY, HAGIT · 2014 to 2015
$560k
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada DiscoveryCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (CA) DiscoveryNational Science Foundation IIS EAGER grant #1650851NIGMS NIH HHS U54 GM104941NIH HHS R01 LM012527-01NIH HHS R56 LM011354ANLM NIH HHS R01 LM012527NLM NIH HHS R56 LM011354
6 · The paper itself

Abstract

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Indexed as

Models, TheoreticalHumansMaleMarkov ChainsProstateProstatic NeoplasmsUltrasonographyHidden Markov modelsImage guided diagnosisTime-domain analysisTissue characterizationTRUS-guided biopsies

Identifiers

PMID32779056
PMCPMC7851024

What OpenQuestion holds

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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.