Evidence map›Paper›PMID 40154847›Full record

ArticleInternational journal of radiation oncology, biology, physics2025

Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking.

Yuze Song, Anna M Dornisch, Robert T Dess, Daniel J A Margolis, Eric P Weinberg, Tristan Barrett, Mariel Cornell, Richard E Fan, Mukesh Harisinghani, Sophia C Kamran and 10 more

Abstract readConsensus Statement
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2025. 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. Article
  2. PURE-MRI: An international study assessing physician accuracy in delineating the prostate and urethra on prostate MRI.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
    Article
  3. 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. Review
  5. Review
  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

20 authors.

Yuze SongDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California.
Anna M DornischDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
Robert T DessDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Daniel J A MargolisDepartment of Radiology, Cornell University, Ithaca, New York.
Eric P WeinbergDepartment of Clinical Imaging Sciences, University of Rochester Medical Center, Rochester, New York.
Tristan BarrettDepartment of Radiology, University of Cambridge, Cambridge, United Kingdom.
Mariel CornellRadformation, New York, New York.
Richard E FanDepartment of Urology, Stanford School of Medicine, Palo Alto, California.
Mukesh HarisinghaniDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
Sophia C KamranDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
Jeong Hoon LeeDepartment of Radiology, Stanford School of Medicine, Palo Alto, California.
Cynthia Xinran LiInstitute for Computational and Mathematical Engineering, Stanford University, Palo Alto, California.
Michael A LissDepartment of Urology, University of Texas Health Sciences Center San Antonio, San Antonio, Texas.
Mirabela RusuDepartment of Urology, Stanford School of Medicine, Palo Alto, California; Department of Radiology, Stanford School of Medicine, Palo Alto, California; Department of Biomedical Data Science, Stanford University, Palo Alto, California.
Jason SantosQuibim, New York, New York.
Geoffrey A SonnDepartment of Urology, Stanford School of Medicine, Palo Alto, California; Department of Radiology, Stanford School of Medicine, Palo Alto, California.
Igor VidicCortechs.ai, San Diego, California.
Sean A WoolenDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Anders M DaleDepartment of Radiology, University of California San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California; Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California.
Tyler M SeibertDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California; Department of Bioengineering, University of California San Diego, La Jolla, California; Department of Urology, University of California San Diego, La Jolla, California. Electronic address: tseibert@health.ucsd.edu.

Funding

San Diego Clinical and Translational Research InstituteUL1TR000100 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S · 2012 to 2015
$19.7M
Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRIR37CA260346 · NCI · STANFORD UNIVERSITY · PI Mirabela Rusu · 2022 to 2026
$2.8M
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
NCATS NIH HHS UL1 TR000100NCI NIH HHS R37 CA260346NIBIB NIH HHS K08 EB026503
6 · The paper itself

Abstract

purposeEvaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2) evaluate various AI tools against this standard. METHODS AND MATERIALS: We obtained prostate magnetic resonance imaging cases from six institutions from the Qualitative Prostate Imaging Consortium. A panel of 4 experts (2 genitourinary radiologists and 2 prostate radiation oncologists) meticulously developed consensus prostate segmentations on axial T

resultsThe panel achieved consensus prostate segmentation on each slice of all 68 patient cases included in the reference data set. We present 2 patient examples to serve as contouring guides. Depending on the AI tool, median Dice scores (across patients) ranged from 0.80 to 0.94 for whole prostate segmentation. For a typical (median) patient, AI tools had a mean error over the prostate surface ranging from 1.3 to 2.4 mm. They maximally deviated 3.0 to 9.4 mm outside the prostate and 3.0 to 8.5 mm inside the prostate for a typical patient. Error in prostate volume measurement for a typical patient ranged from 4.3% to 31.4%.

conclusionsWe established an expert consensus benchmark for prostate segmentation. The best-performing AI tools have typical accuracy greater than that reported for radiation oncologists using computed tomography scans (the most common clinical approach for radiation therapy planning). Physician review remains essential to detect occasional major errors.

Indexed as

Artificial IntelligenceBenchmarkingMagnetic Resonance ImagingProstateProstatic NeoplasmsAlgorithmsHumansMaleReference Standards

Identifiers

PMID40154847
PMCPMC12243632

What OpenQuestion holds

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LicenceCC BY
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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.