ArticleThe Canadian journal of urology2025
AI-based detection of MRI-invisible prostate cancer with nnU-Net.
Article in The Canadian journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Analysis of risk factors for MRI-invisible prostate cancer-the significance of AGGF1 immunohistochemical detection and PSAD.The Canadian journal of urology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop an artificial intelligence (AI)-based image recognition system using the nnU-Net adaptive neural network to assist clinicians in detecting magnetic resonance imaging (MRI)-invisible prostate cancer. The motivation stems from the diagnostic challenges, especially when MRI findings are inconclusive (Prostate Imaging Reporting and Data System [PI-RADS] score ≤ 3).
methodsWe retrospectively included 150 patients who underwent systematic prostate biopsy at Beijing Friendship Hospital between January 2013 and January 2023. All were pathologically confirmed to have clinically significant prostate cancer, despite negative findings on preoperative MRI. A total of 1475 MRI images, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) sequences, were collected. The nnU-Net was employed as the initial segmentation framework to delineate tumor regions in MRI images, based on histopathologically confirmed prostate cancer sites. A convolutional neural network-based deep learning model was subsequently designed and trained. Its performance was evaluated using five-fold cross-validation.
resultsAmong 150 patients with clinically significant prostate cancer diagnosed, all with PI-RADS ≤ 3 on MRI, the median age was 67 years (IQR: 62-72), and 105 patients (70.0%) had a Gleason score ≥ 7. A total of 1475 multiparametric MRI images were analyzed. Using five-fold cross-validation, the AI-based image recognition system achieved a mean Dice similarity coefficient of 55.0% (range: 51.6-56.5%), with a mean sensitivity of 50.5% and a mean specificity of 96.9%. The corresponding mean false-positive and false-negative rates were 3.1% and 49.5%, respectively.
conclusionWe successfully developed an AI-based image recognition system utilizing the nnU-Net adaptive neural network, demonstrating promising diagnostic performance in detecting MRI-invisible prostate cancer. This system has the potential to enhance early detection and management of prostate cancer.
Indexed as
Identifiers
41220354What OpenQuestion holds
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.