Trial reportScientific reports2022
Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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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
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled it
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI.Medical image analysis · 2026Article
- Adaptive Knowledge Distillation for Anatomical Segmentation in Pelvic CT Imaging of Prostate Cancer.Annals of biomedical engineering · 2026Article
- From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence, biomarkers, and imaging.Current urology · 2026Review
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy.Journal of imaging informatics in medicine · 2026Article
- Comparison of Artificial Intelligence and Radiologists in MRI-Based Prostate Cancer Diagnosis: A Meta-Analysis of Accuracy and Effectiveness.Biomedicines · 2025Article
- A Multi-Stage Hybrid Learning Model with Advanced Feature Fusion for Enhanced Prostate Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025Review
- A modified deep learning approach for seminal vesicle region localization in prostate MRI.Scientific reports · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- Beyond PSA: The Future of Prostate Cancer Diagnosis Using Artificial Intelligence, Novel Biomarkers, and Advanced Imagery.Life (Basel, Switzerland) · 2025Review
- Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images.NPJ digital medicine · 2025Article
- Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.European radiology · 2025Article
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.American journal of clinical and experimental urology · 2025Review
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 2025Article
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG[Formula: see text]2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection . We hope that this will represent a landmark for future research to use, compare and improve upon.
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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.