ArticleRadiology2024
Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.
Article in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled it
- AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Recent advances in discriminating prostate cancer from benign prostatic diseases in the pre-biopsy population.Medical oncology (Northwood, London, England) · 2026Review
- Navigating Uncertainty in MRI Diagnosis: A Human-AI Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
- mpMRI-based clinic-radiomics-deep learning model integrating lesion and PPAT for predicting csPCa in PI-RADS category 3 lesions: a multicenter study.International urology and nephrology · 2026Article
- A comprehensive framework for multi-class prostate cancer classification using biparametric MRI: a multi-center retrospective study.Insights into imaging · 2026Article
- Autonomous AI in prostate cancer: the road ahead towards clinical implementation.Abdominal radiology (New York) · 2026Review
- Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects.Abdominal radiology (New York) · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- AI for screening in healthcare: promise and challenges.Abdominal radiology (New York) · 2026Review
- Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.Abdominal radiology (New York) · 2026Article
- High-sensitivity Raman spectroscopy for prostate cancer detection and tissue extraction guidance.Biomedical optics express · 2026Article
- Detecting clinically significant prostate cancer with a distributed parameter model based on quantitative dynamic contrast-enhanced magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning classification of significant prostate cancer on MRI: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.Abdominal radiology (New York) · 2026Review
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Article
- Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.Insights into imaging · 2026Article
- Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.Abdominal radiology (New York) · 2026Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (
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