Rationale and objectives
Materials and Methods
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- Cancer statistics, 2020.CA Cancer J Clin. 2020; 70: 7-30
- Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study.Lancet (London, England). 2017; 389: 815-822
- MRI-targeted or standard biopsy for prostate-cancer diagnosis.The New England journal of medicine. 2018; 378: 1767-1777
- MRI-Targeted, systematic, and combined biopsy for prostate cancer diagnosis.The New England journal of medicine. 2020; 382: 917-928
- Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.JAMA. 2015; 313: 390-397
- PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2.Eur Urol. 2016; 69: 16-40
- Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2.European Urology. 2019; 0232: 1-12
- Interreader Agreement with Prostate Imaging Reporting and Data System Version 2 for Prostate Cancer Detection: A Systematic Review and Meta-Analysis.The Journal of urology. 2020; 204: 661-670
- International evaluation of an AI system for breast cancer screening.Nature. 2020; 577: 89-94
- Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA. 2016; 316: 2402-2410
- Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.Radiology. 2019; 293: 607-617
- Computer-Aided Detection of Prostate Cancer in MRI.IEEE Transactions on Medical Imaging. 2014; 33: 1083-1092
- The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.Journal of Digital Imaging. 2013; 26: 1045-1057
- Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study.Journal of magnetic resonance imaging: JMRI. 2020; 52: 1499-1507
- Fully automated prostate segmentation on MRI: comparison with manual segmentation methods and specimen volumes.AJR Am J Roentgenol. 2013; 201: W720-W729
Çiçek Ö, Abdulkadir A, Lienkamp SS, et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation2016; Cham: Springer International Publishing.
- All over the map: An interobserver agreement study of tumor location based on the PI-RADSv2 sector map.Journal of Magnetic Resonance Imaging. 2018; 48: 482-490
- Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists.Radiology. 2016; 280: 793-804
- Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.European radiology. 2020; 30: 6582-6592
This project is based on a Cooperative Research and Development Agreement (CRADA) between the National Institutes of Health and the NVIDIA Corporation.