ReviewEuropean radiology2024
Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?
Review in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- The role of magnetic resonance imaging in the active surveillance of prostate cancer: state of the art.Abdominal radiology (New York) · 2026Review
- Artificial intelligence-enabled MRI surveillance of prostate cancer: integrating PRECISE v2, PI-QUAL, and longitudinal biomarkers-a narrative review.Abdominal radiology (New York) · 2026Review
- Multicenter Validation of a Risk Classification Cluster for Unfavorable Pathology in Prostatectomy Specimens of Patients with an Active Surveillance Expanded Inclusion Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.Cancers · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025Review
- Personalized risk-adapted models in prostate cancer during active surveillance using MRI-a narrative review.European radiology · 2025Review
- The 5-year results of the Stratified Cancer Active Surveillance programme for men with prostate cancer.BJU international · 2025Article
- A Personalized, Risk-Based Approach to Active Surveillance for Prostate Cancer with Takeaways from Broader Oncology Practices: A Mixed Methods Review.Journal of personalized medicine · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.
methodsResearch studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.
resultsPreliminary results show the role of AI in a range of diagnostic tasks in AS populations, including the localization, follow-up, and prognostication of prostate cancer. Current evidence is insufficient to support a shift to AI-based AS, with studies being limited by small dataset sizes, heterogeneous inclusion and outcome definitions, or lacking appropriate benchmarks.
conclusionThe AI-based integration of prostate MRI is a direction that promises substantial benefits for AS in the future, but evidence is currently insufficient to support implementation. Studies with standardized inclusion criteria and standardized progression definitions are needed to support this. The increasing inclusion of patients in AS protocols and the incorporation of MRI as a scheduled examination in AS protocols may help to alleviate these challenges in future studies. CLINICAL RELEVANCE STATEMENT: This manuscript provides an overview of available evidence for the integration of prostate MRI and AI in active surveillance, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation. KEY POINTS: Active surveillance is currently based on diagnostic tests such as PSA, biopsy, and imaging. Prostate MRI and AI demonstrate promising diagnostic accuracy across a variety of tasks, including the localization, follow-up and risk estimation in active surveillance cohorts. A transition to AI-based active surveillance is not currently realistic; larger studies using standardized inclusion criteria and outcomes are necessary to improve and validate existing evidence.
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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.