ArticleBMC urology2025
Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.
Article in BMC urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 3 papers.
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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.
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.
Clinical Validation of Transrectal Multiparametric Ultrasound (PCaVision) for Detecting Clinically Significant Prostate Cancer: A European Head-to-Head Comparison With MRI in Primary Diagnosis and Active Surveillance Populations
Optimization of PCaVision: A Prospective Study for CAD-based Detection of Clinically Significant Prostate Cancer Using mpUS and Contrast Agents
Who cites it
3 citing papers in PubMed.
- Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer.European radiology · 2026Article
- Reply to the Letter to the Editor: Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.European radiology · 2026Article
- Three-Dimensional Shear-Wave Viscoelastographic Estimation by System Identification for Prostate Cancer Localization.Ultrasound in medicine & biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeArtificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an essential step in creating such a dataset. This study presents a comprehensive framework for the registration of 3D transrectal ultrasound (TRUS) to radical prostatectomy specimen (RPS) pathology.
methodsThe study enrolled patients who underwent 3D TRUS and were scheduled for radical prostatectomy. A four-step process for registering RPS to TRUS was used: image segmentation, 3D reconstruction of RPS pathology, registration and ground truth calculation. Accuracy was assessed using a target-registration error (TRE) based on landmarks visible on both TRUS and pathology.
results20 Sets of 3D TRUS and RPS pathology were included for analyses. The mean TRE was 3.5 mm, (range: 0.4 to 5.4 mm), with TRE values in the apex-base, left-right and posterior-anterior directions of 2.5 mm, 1.1 mm, and 1.4 mm, respectively.
conclusionThe framework proposed in this study accomplishes precise registration between prostate pathology and imaging. The methodologies employed hold the potential for broader application across diverse imaging modalities and other target organs. However, limitations such a small sample size and the need for manual segmentation should be considered when interpreting te results. Future efforts should focus on automating key steps to enhance reproducibility and scalability.
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Registered trials
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.