ReviewDiagnostics (Basel, Switzerland)2022
Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed.
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- Predicting recurrence of prostate cancer after radical treatment using AI models based on PET/CT radiomics: a dual-center study.Frontiers in oncology · 2026Article
- Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI.Scientific reports · 2025Article
- Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.Journal of imaging · 2025Review
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Article
- A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.Journal of medical radiation sciences · 2025Article
- 3D-AttenNet model can predict clinically significant prostate cancer in PI-RADS category 3 patients: a retrospective multicenter study.Insights into imaging · 2025Article
- Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.PloS one · 2025Article
- The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS).Magma (New York, N.Y.) · 2024Review
- Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images.Journal of medical signals and sensors · 2024Article
- Reliability of Systematic and Targeted Biopsies versus Prostatectomy.Bioengineering (Basel, Switzerland) · 2023Article
- MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.Cancers · 2023Article
- Article
- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.Frontiers in oncology · 2023Review
- Article
- Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.AJR. American journal of roentgenology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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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.