ReviewCancers2020
Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Assessing inter-observer variability in prostate and GTV segmentation on mpMRI: A comparison between radiation oncologists and AI-based method.Journal of applied clinical medical physics · 2026Article
- Post-Treatment Imaging in Focal Therapy: Understanding TARGET and PI-FAB Scoring Systems.Diagnostics (Basel, Switzerland) · 2025Review
- Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Review
- Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.The British journal of radiology · 2024Article
- Prostate Cancer: Genetics, Epigenetics and the Need for Immunological Biomarkers.International journal of molecular sciences · 2023Review
- Review
- Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol.Systematic reviews · 2023Article
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
- Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers.La Radiologia medica · 2022Article
- Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches.Molecules (Basel, Switzerland) · 2022Review
- Developments in proton MR spectroscopic imaging of prostate cancer.Magma (New York, N.Y.) · 2022Review
- The role of MRI in prostate cancer: current and future directions.Magma (New York, N.Y.) · 2022Review
- Deep learning-based artificial intelligence applications in prostate MRI: brief summary.The British journal of radiology · 2022Review
- Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer.Frontiers in oncology · 2022Article
- Multiparametric prostate MRI quality assessment using a semi-automated PI-QUAL software program.European radiology experimental · 2021Article
- Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization.International journal of molecular sciences · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.
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.