Artificial Intelligence–assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Artificial Intelligence–assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation
Authors
Keywords
-
Journal
RADIOGRAPHICS
Volume 41, Issue 6, Pages 1676-1697
Publisher
Radiological Society of North America (RSNA)
Online
2021-10-02
DOI
10.1148/rg.2021210020
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives
- (2021) Octavian Sabin Tătaru et al. Diagnostics
- Independent real‐world application of a clinical‐grade automated prostate cancer detection system
- (2021) Leonard M Silva et al. JOURNAL OF PATHOLOGY
- MRI-Targeted, Systematic, and Combined Biopsy for Prostate Cancer Diagnosis
- (2020) Michael Ahdoot et al. NEW ENGLAND JOURNAL OF MEDICINE
- Prostate Cancer Incidence 5 Years After US Preventive Services Task Force Recommendations Against Screening
- (2020) Ahmedin Jemal et al. JNCI-Journal of the National Cancer Institute
- Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies
- (2020) Patricia Raciti et al. MODERN PATHOLOGY
- Delivering Clinical impacts of the MRI diagnostic pathway in prostate cancer diagnosis
- (2020) Ivo G. Schoots et al. Abdominal Radiology
- Microscope? No, Thanks: User Experience With Complete Digital Pathology for Routine Diagnosis
- (2020) Juan Antonio Retamero et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis
- (2020) Renato Cuocolo et al. EUROPEAN RADIOLOGY
- Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition
- (2020) Mohammed R. S. Sunoqrot et al. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
- Predicting Pathologic Tumor Size in Prostate Cancer Based on Multiparametric Prostate MRI and Preoperative Findings
- (2020) Aydin Pooli et al. JOURNAL OF UROLOGY
- Early Detection of Prostate Cancer in 2020 and Beyond: Facts and Recommendations for the European Union and the European Commission
- (2020) Hendrik Van Poppel et al. EUROPEAN UROLOGY
- The utility of in-bore multiparametric magnetic resonance-guided biopsy in men with negative multiparametric magnetic resonance-ultrasound software-based fusion targeted biopsy
- (2020) Andry Perrin et al. UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS
- EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer—2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent
- (2020) Nicolas Mottet et al. EUROPEAN UROLOGY
- Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2
- (2019) Baris Turkbey et al. EUROPEAN UROLOGY
- A new era: artificial intelligence and machine learning in prostate cancer
- (2019) S. Larry Goldenberg et al. Nature Reviews Urology
- Artificial intelligence at the intersection of pathology and radiology in prostate cancer
- (2019) Stephanie A. Harmon et al. Diagnostic and Interventional Radiology
- Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network
- (2019) Juan Antonio Retamero et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists
- (2019) Michela Antonelli et al. EUROPEAN RADIOLOGY
- Prostate Magnetic Resonance Imaging, with or Without Magnetic Resonance Imaging-targeted Biopsy, and Systematic Biopsy for Detecting Prostate Cancer: A Cochrane Systematic Review and Meta-analysis
- (2019) Frank-Jan H. Drost et al. EUROPEAN UROLOGY
- Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization
- (2019) Jussi Toivonen et al. PLoS One
- Decision Support Systems in Prostate Cancer Treatment: An Overview
- (2019) Y. van Wijk et al. Biomed Research International
- Interim Results from the IMPACT Study: Evidence for Prostate-specific Antigen Screening in BRCA2 Mutation Carriers
- (2019) Elizabeth C. Page et al. EUROPEAN UROLOGY
- Personalizing prostate cancer diagnosis with multivariate risk prediction tools: how should prostate MRI be incorporated?
- (2019) Ivo G. Schoots et al. WORLD JOURNAL OF UROLOGY
- Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology
- (2019) Teodoro Martín Noguerol et al. Journal of the American College of Radiology
- False-Negative Histopathologic Diagnosis of Prostatic Adenocarcinoma
- (2019) Chen Yang et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Comparison of MRI- and TRUS-Informed Prostate Biopsy for Prostate Cancer Diagnosis in Biopsy-Naive Men: A Systematic Review and Meta-Analysis
- (2019) Hanan Goldberg et al. JOURNAL OF UROLOGY
- Genomics of lethal prostate cancer at diagnosis and castration resistance
- (2019) Joaquin Mateo et al. JOURNAL OF CLINICAL INVESTIGATION
- Detecting Prostate Cancer with Deep Learning for MRI: A Small Step Forward
- (2019) Anwar R. Padhani et al. RADIOLOGY
- Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment
- (2019) Patrick Schelb et al. RADIOLOGY
- Prostate Imaging-Reporting and Data System Steering Committee: PI-RADS v2 Status Update and Future Directions
- (2018) Anwar R. Padhani et al. EUROPEAN UROLOGY
- Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer
- (2018) Sean D. McGarry et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Prostate-Specific Antigen–Based Screening for Prostate Cancer
- (2018) Joshua J. Fenton et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Diagnosis of Prostate Cancer with Noninvasive Estimation of Prostate Tissue Composition by Using Hybrid Multidimensional MR Imaging: A Feasibility Study
- (2018) Aritrick Chatterjee et al. RADIOLOGY
- Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction
- (2018) Seong Ho Park et al. RADIOLOGY
- Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation
- (2018) Sonia Gaur et al. Oncotarget
- Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods
- (2018) J. Ferlay et al. INTERNATIONAL JOURNAL OF CANCER
- Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer—Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies
- (2017) Jan Philipp Radtke et al. EUROPEAN UROLOGY
- The Value of PSA Density in Combination with PI-RADS™ for the Accuracy of Prostate Cancer Prediction
- (2017) Florian A. Distler et al. JOURNAL OF UROLOGY
- Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer
- (2017) Tobias Nordström et al. PROSTATE CANCER AND PROSTATIC DISEASES
- European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators significantly outperform the Prostate Cancer Prevention Trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study
- (2016) Robert W. Foley et al. BJU INTERNATIONAL
- KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach
- (2016) William JF Green et al. BRITISH JOURNAL OF CANCER
- Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI
- (2016) Rakesh Shiradkar et al. Radiation Oncology
- The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma
- (2015) Jonathan I. Epstein et al. AMERICAN JOURNAL OF SURGICAL PATHOLOGY
- Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer
- (2015) George Lee et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Comparison of MR/Ultrasound Fusion–Guided Biopsy With Ultrasound-Guided Biopsy for the Diagnosis of Prostate Cancer
- (2015) M. Minhaj Siddiqui et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Intraoperative Registered Transrectal Ultrasound Guidance for Robot-Assisted Laparoscopic Radical Prostatectomy
- (2015) Omid Mohareri et al. JOURNAL OF UROLOGY
- Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
- (2015) Duc Fehr et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Changes in Epithelium, Stroma, and Lumen Space Correlate More Strongly with Gleason Pattern and Are Stronger Predictors of Prostate ADC Changes than Cellularity Metrics
- (2015) Aritrick Chatterjee et al. RADIOLOGY
- Screening for Prostate Cancer With the Prostate-Specific Antigen Test
- (2014) Julia H. Hayes et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- The Mutational Landscape of Prostate Cancer
- (2013) Christopher E. Barbieri et al. EUROPEAN UROLOGY
- Multiparametric MRI maps for detection and grading of dominant prostate tumors
- (2012) Mehdi Moradi et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Prostate Cancer: Computer-aided Diagnosis with Multiparametric 3-T MR Imaging—Effect on Observer Performance
- (2012) Thomas Hambrock et al. RADIOLOGY
- Comparing the Gleason Prostate Biopsy and Gleason Prostatectomy Grading System: The Lahey Clinic Medical Center Experience and an International Meta-Analysis
- (2008) Michael S. Cohen et al. EUROPEAN UROLOGY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started