ReviewAmerican journal of clinical and experimental urology2025
Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.
Review in American journal of clinical and experimental urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to evaluate the overall impact of incorporating deep learning (DL) with magnetic resonance imaging (MRI) for improving diagnostic performance in the detection and stratification of prostate cancer (PC).
methodsA systematic search was conducted in the PubMed database to identify relevant studies. The QUADAS-2 tool was employed to assess the scientific quality, risk of bias, and applicability of primary diagnostic accuracy studies. Additionally, adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines was evaluated to determine the extent of heterogeneity among the included studies. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.
resultsA total of 29 articles involving 17,954 participants were included in the study. The median agreement to the 42 CLAIM checklist items across studies was 61.90% (IQR: 57.14-66.67, range: 40.48-80.95). Most studies utilized T2-weighted imaging (T2WI) and/or apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) as input for evaluating the performance of DL-based architectures. Notably, the detection and stratification of PC in the transition zone was the least explored area.
conclusionsDL demonstrates significant advancements in the rapid, sensitive, specific, and robust detection and stratification of PC. Promising applications include enhancing the quality of DWI, developing advanced DL models, and designing innovative nomograms or diagnostic tools to improve clinical decision-making.
Indexed as
Identifiers
What 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.