ArticleEuropean radiology2023
The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled it
- Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.European radiology · 2026Trial
- AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers.Journal of magnetic resonance imaging : JMRI · 2026Article
- Development and Validation of an AI-Assisted Predictive Model Integrating R2* Mapping and Clinical Indicators for Clinically Significant Prostate Cancer.Cancer medicine · 2026Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence-assisted enhancement of prostate volume metrics in the diagnosis of clinically significant prostate cancer: a retrospective analysis.Quantitative imaging in medicine and surgery · 2025Article
- Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance.Frontiers in urology · 2025Article
- Prostate-MRI reporting should be done with the aid of AI systems: Pros.European radiology · 2024Article
- Prostate MRI: what to consider when shopping for AI tools.European radiology · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy.European radiology experimental · 2024Article
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Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesTo develop an artificial intelligence (AI) model for prostate segmentation and prostate cancer (PCa) detection, and explore the added value of AI-based computer-aided diagnosis (CAD) compared to conventional PI-RADS assessment.
methodsA retrospective study was performed on multi-centers and included patients who underwent prostate biopsies and multiparametric MRI. A convolutional-neural-network-based AI model was trained and validated; the reliability of different CAD methods (concurrent read and AI-first read) were tested in an internal/external cohort. The diagnostic performance, consistency and efficiency of radiologists and AI-based CAD were compared.
resultsThe training/validation/internal test sets included 650 (400/100/150) cases from one center; the external test included 100 cases (25/25/50) from three centers. For diagnosis accuracy, AI-based CAD methods showed no significant differences and were equivalent to the radiologists in the internal test (127/150 vs. 130/150 vs. 125/150 for reader 1; 127/150 vs.132/150 vs. 131/150 for reader 2; all p > 0.05), whereas in the external test, concurrent-read methods were superior/equal to AI-first read (87/100 vs. 71/100, p < 0.001, for reader 2; 79/100 vs. 69/100, p = 0.076, for reader 1) and better than/equal to radiologists (79/100 vs. 72/100, p = 0.039, for reader 1; 87/100 vs. 86/100, p = 1.000, for reader 2). Moreover, AI-first read/concurrent read improved consistency in both internal test (κ = 1.000, 0.830) and external test (κ = 0.958, 0.713) compared to radiologists (κ = 0.747, 0.600); AI-first read method (8.54 s/7.66 s) was faster than readers (92.72 s/89.54 s) and concurrent-read method (29.15 s/28.92 s), respectively.
conclusionAI-based CAD could improve the consistency and efficiency for accurate diagnosis; the concurrent-read method could enhance the diagnostic capabilities of an inexperienced radiologist in unfamiliar situations. KEY POINTS: • For prostate cancer segmentation, the performance of multi-small Vnet displays optimal compared to small Vnet and Vnet (DSC
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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.