Evidence map›Paper›PMID 34679484›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification.

Yongkai Liu, Haoxin Zheng, Zhengrong Liang, Qi Miao, Wayne G Brisbane, Leonard S Marks, Steven S Raman, Robert E Reiter, Guang Yang, Kyunghyun Sung

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 31 citations in OpenAlex.

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  15. Tumor Diagnosis and Treatment: Imaging Assessment.Tomography (Ann Arbor, Mich.) · 2022
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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

10 authors at 3 institutions in 2 countries.

Yongkai LiuDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0003-4478-4006
Haoxin ZhengDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Zhengrong LiangDepartments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Qi MiaoDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Wayne G BrisbaneDepartment of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Leonard S MarksDepartment of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Steven S RamanDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Robert E ReiterDepartment of Urology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
Guang YangNational Heart and Lung Institute, Imperial College London, South Kensington, London SW7 2AZ, UK.ORCID 0000-0001-7344-7733
Kyunghyun SungDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0003-4175-5322
University of California, Los Angeles · USImperial College London · GBStony Brook University · US

Funding

Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer ClassificationR01CA248506 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUNG, KYUNG HYUN, WU, HOLDEN H · 2020 to 2024
$3.0M
British Heart Foundation PG/16/78/32402British Heart Foundation TG/18/5/34111Medical Research Council MR/V023799/1NCI NIH HHS R01 CA248506NIH HHS R01-CA248506
6 · The paper itself

Abstract

The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (

Indexed as

convolutional neural networkdeep learningPI-RADSprostate cancer classificationtexture analysis

Identifiers

PMID34679484
PMCPMC8535024
OpenAlexW3203583757

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.