ArticleEuropean radiology2022
Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed, 117 citations in OpenAlex.
- The Role of Artificial Intelligence in Optimizing Diagnosis in Prostate Cancer-A Narrative Review.Journal of clinical medicine · 2026Review
- Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data.Medical physics · 2026Article
- Development and internal validation of a biparametric MRI-based nomogram for predicting extraprostatic extension in patients undergoing robot-assisted radical prostatectomy.Journal of robotic surgery · 2026Article
- Landmark Regression with Attention U-Net for Assessing Breast Positioning Quality in MLO Mammography View.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning classification of significant prostate cancer on MRI: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI.Medical image analysis · 2026Article
- Prostate cancer detection using modified transformer with optimal feature selection from MRI images.Scientific reports · 2026Article
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.European radiology · 2025Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review.Cancers · 2025Review
- Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate MRI.Journal of magnetic resonance imaging : JMRI · 2025Article
- Artificial Intelligence-Assisted Biparametric MRI for Detecting Prostate Cancer-A Comparative Multireader Multicase Accuracy Study.Journal of clinical medicine · 2025Article
- AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study.European radiology · 2025Article
- Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.European radiology · 2025Article
- 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
4 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess Prostate Imaging Reporting and Data System (PI-RADS)-trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men with a suspicion of PCa.
methodsMulti-institution data included 2734 consecutive biopsy-naïve men with elevated PSA levels (≥ 3 ng/mL) that underwent multi-parametric MRI (mpMRI). mpMRI exams were prospectively reported using PI-RADS v2 by expert radiologists. A DL framework was designed and trained on center 1 data (n = 1952) to predict PI-RADS ≥ 4 (n = 1092) lesions from bi-parametric MRI (bpMRI). Experiments included varying the number of cases and the use of automatic zonal segmentation as a DL prior. Independent center 2 cases (n = 296) that included pathology outcome (systematic and MRI targeted biopsy) were used to compute performance for radiologists and DL. The performance of detecting PI-RADS 4-5 and Gleason > 6 lesions was assessed on 782 unseen cases (486 center 1, 296 center 2) using free-response ROC (FROC) and ROC analysis.
resultsThe DL sensitivity for detecting PI-RADS ≥ 4 lesions was 87% (193/223, 95% CI: 82-91) at an average of 1 false positive (FP) per patient, and an AUC of 0.88 (95% CI: 0.84-0.91). The DL sensitivity for the detection of Gleason > 6 lesions was 85% (79/93, 95% CI: 77-83) @ 1 FP compared to 91% (85/93, 95% CI: 84-96) @ 0.3 FP for a consensus panel of expert radiologists. Data size and prior zonal knowledge significantly affected performance (4%, [Formula: see text]).
conclusionPI-RADS-trained DL can accurately detect and localize Gleason > 6 lesions. DL could reach expert performance using substantially more than 2000 training cases, and DL zonal segmentation. KEY POINTS: • AI for prostate MRI analysis depends strongly on data size and prior zonal knowledge. • AI needs substantially more than 2000 training cases to achieve expert performance.
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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.