Evidence map›Paper›PMID 41429726›Full record

ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2026

Urethra contours on MRI: Multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking.

Yuze Song, Lily Nguyen, Anna M Dornisch, Madison T Baxter, Tristan Barrett, Anders M Dale, Robert T Dess, Mukesh Harisinghani, Sophia C Kamran, Michael A Liss and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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. 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

14 authors.

Yuze SongDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Lily NguyenDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA; University of California San Diego School of Medicine, La Jolla, CA, USA.
Anna M DornischDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Madison T BaxterDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Tristan BarrettDepartment of Radiology, University of Cambridge, Cambridge, United Kingdom.
Anders M DaleDepartment of Radiology, University of California San Diego, La Jolla, CA, USA.
Robert T DessDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Mukesh HarisinghaniDepartment of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Sophia C KamranDepartment of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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, 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.
Tyler M SeibertDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Urology, University of California San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. 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
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 TR000100NIBIB NIH HHS K08 EB026503
6 · The paper itself

Abstract

introductionThe urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify it on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model. MATERIALS AND

methodsA multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus urethra contours on MRI data for 71 patients from 6 centers, establishing a reference standard. Four of these patients were previously used in an international study (PURE-MRI) where 62 physicians contoured the prostate and urethra. Using an independent training dataset (n = 151 patients, 1 center), we developed a deep-learning AI model for urethra segmentation. We evaluated the AI tool in the consensus reference dataset and compared it to human performance using Dice, percent urethra coverage, and maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard.

resultsThe AI model outperformed most physicians, achieving median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81 % (vs. 36 %), and Max 2D HD of 1.8 mm (vs. 1.6 mm) in the four PURE-MRI cases. In the full reference dataset, AI performance remained consistent, with Dice of 0.40, Coverage of 89 %, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions.

conclusionWe established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.

Indexed as

Artificial IntelligenceBenchmarkingMagnetic Resonance ImagingProstatic NeoplasmsRadiotherapy Planning, Computer-AssistedUrethraConsensusDeep LearningHumansMaleReference Standards

Identifiers

PMID41429726
PMCPMC13284904

What OpenQuestion holds

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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.