Evidence map›Paper›PMID 41695400›Full record

ArticleEuropean urology open science2026

Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging.

Yuze Song, Mariluz Rojo Domingo, Christopher C Conlin, Deondre D Do, Madison T Baxter, Anna Dornisch, George Xu, Aditya Bagrodia, Tristan Barrett, Mukesh Harisinghani and 21 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology open science, 2026. 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
–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. Urethra contours on MRI: Multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
    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

31 authors.

Yuze SongDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Mariluz Rojo DomingoDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Christopher C ConlinDepartment of Radiology, University of California-San Diego, La Jolla, CA, USA.
Deondre D DoDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Madison T BaxterDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Anna DornischDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
George XuDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Aditya BagrodiaDepartment of Urology, University of California-San Diego, La Jolla, CA, USA.
Tristan BarrettDepartment of Radiology, Cambridge University, Cambridge, UK.
Mukesh HarisinghaniDepartment of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Gary HollenbergDepartment of Clinical Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA.
Sophia KamranDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
Christopher J KaneDepartment of Urology, University of California-San Diego, La Jolla, CA, USA.
Dimitri A KesslerDepartment of Radiology, Cambridge University, Cambridge, UK.
Joshua KupermanDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.
Kanglung LeeDepartment of Radiology, Cambridge University, Cambridge, UK.
Michael A LissDepartment of Urology, University of California-San Diego, La Jolla, CA, USA.
Daniel J A MargolisDepartment of Radiology, Cornell University, Ithaca, NY, USA.
Paul M MurphyDepartment of Radiology, University of California-San Diego, La Jolla, CA, USA.
Nabih NakrourDepartment of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Truong NgyuenDepartment of Electrical and Computer Engineering, University of California-San Diego, La Jolla, CA, USA.
Thomas L OsinskiDepartment of Urology, University of Rochester Medical Center, Rochester, NY, USA.
Rebecca Rakow-PennerDepartment of Radiology, University of California-San Diego, La Jolla, CA, USA.
Shoumik RoychowdhuryDepartment of Electrical Engineering and Computer Sciences, University of California-Berkeley, Berkeley, CA, USA.
Ahmed S ShabikDepartment of Pathology, University of California-San Diego, La Jolla, CA, USA.
Shaun TrecartenDepartment of Urology, University of Texas Health Sciences Center, San Antonio, TX, USA.
Natasha WehrliDepartment of Radiology, Cornell University, Ithaca, NY, USA.
Eric P WeinbergDepartment of Clinical Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA.
Sean A WoolenDepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA, USA.
Anders M DaleDepartment of Radiology, University of California-San Diego, La Jolla, CA, USA.
Tyler M SeibertDepartment of Radiation Medicine, University of California-San Diego, La Jolla, CA, USA.

Funding

Improving assessment of prostate cancer bone metastases using advanced diffusion imagingK08EB026503 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SEIBERT, TYLER MICHAEL · 2019 to 2022
$805k
NIBIB NIH HHS K08 EB026503
6 · The paper itself

Abstract

Background and objective: Our aim was to evaluate whether combining the maximum restriction score derived from restriction spectrum imaging (RSIrs Methods: A total of 1892 patients from seven institutions who underwent imaging between January 2016 and March 2024 were included on the basis of magnetic resonance imaging (MRI) findings and biopsy-confirmed prostate cancer diagnosis. Two DL architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrs Key findings and limitations: Neither RSIrs Conclusions and clinical implications: Both RSIrs Patient summary: We looked at whether adding advanced scan data (ASD) and artificial intelligence (AI) models to radiologist assessments of MRI (magnetic resonance imaging) scans was better in detecting aggressive prostate cancer (PCa). We found that adding AI models or ASD to standard scan scores improved cancer detection in comparison to standard scores alone. The results suggest that combining radiologist expertise with AI and ASD may help in earlier identification of more patients with csPCa.

Indexed as

Deep learningMagnetic resonance imagingProstate cancer

Identifiers

PMID41695400
PMCPMC12905774

What OpenQuestion holds

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Registered trials

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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.