The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms

Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra

(2022) Mason J. Belue et al.   ACADEMIC RADIOLOGY

Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3‐T Multiparametric Magnetic Resonance Imaging

(2021) Ruiming Cao et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

(2021) Oliver Diaz et al.   Physica Medica-European Journal of Medical Physics

Maschinelles Lernen und multiparametrische MRT in der Früherkennung des Prostatakarzinoms

(2021) D. Bonekamp et al.   UROLOGE

Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

(2021) Piotr Sobecki et al.   PeerJ

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

(2021) Jinke Xie et al.   Frontiers in Oncology

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

(2021) Octavian Sabin Tătaru et al.   Diagnostics

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

(2021) Tobias Penzkofer et al.   EUROPEAN RADIOLOGY

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

(2021) Kicky G. van Leeuwen et al.   EUROPEAN RADIOLOGY

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion

(2021) Pegah Khosravi et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Artificial Intelligence in Magnetic Resonance Imaging–based Prostate Cancer Diagnosis: Where Do We Stand in 2021?

(2021) Rodrigo Suarez-Ibarrola et al.   European Urology Focus

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

(2021) Ying Hou et al.   EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)

(2021) Liping Si et al.   EUROPEAN RADIOLOGY

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies

(2021) Tom Syer et al.   Cancers

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

(2021) Valentina Giannini et al.   Diagnostics

A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

(2021) Sherif Mehralivand et al.   ACADEMIC RADIOLOGY

Quality of Prostate MRI: Is the PI-RADS Standard Sufficient?

(2020) Jonathan Sackett et al.   ACADEMIC RADIOLOGY

Preparing Medical Imaging Data for Machine Learning

(2020) Martin J. Willemink et al.   RADIOLOGY

Variability and Standardization of Quantitative Imaging

(2020) Akifumi Hagiwara et al.   INVESTIGATIVE RADIOLOGY

Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel

(2020) Antonio C. Westphalen et al.   RADIOLOGY

Multicenter Multireader Evaluation of an Artificial Intelligence–Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI

(2020) Sherif Mehralivand et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

(2020) Rogier R. Wildeboer et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

(2020) Muhammad Arif et al.   EUROPEAN RADIOLOGY

Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation

(2020) Yansheng Kan et al.   EUROPEAN RADIOLOGY

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

(2020) Simon Bernatz et al.   EUROPEAN RADIOLOGY

Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

(2020) Neda Gholizadeh et al.   Journal of Applied Clinical Medical Physics

Deep‐Learning‐Based Artificial Intelligence for PI‐RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study

(2020) Thomas Sanford et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

(2020) Piotr Woźnicki et al.   Cancers

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study

(2020) Ahmad Algohary et al.   Cancers

Impact of radiomics on prostate cancer detection: a systematic review of clinical applications

(2020) Dordaneh Sugano et al.   CURRENT OPINION IN UROLOGY

Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy

(2020) Stephan Ellmann et al.   Cancers

Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System

(2020) Patrick Schelb et al.   ROFO-FORTSCHRITTE AUF DEM GEBIET DER RONTGENSTRAHLEN UND DER BILDGEBENDEN VERFAHREN

Prostate cancer classification with multiparametric MRI transfer learning model

(2019) Yixuan Yuan et al.   MEDICAL PHYSICS

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

(2019) Satish E. Viswanath et al.   BMC MEDICAL IMAGING

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images

(2019) Bino Varghese et al.   Scientific Reports

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: cross-validation of a machine learning method

(2019) Xiangde Min et al.   EUROPEAN JOURNAL OF RADIOLOGY

Prostate cancer detection using residual networks

(2019) Helen Xu et al.   International Journal of Computer Assisted Radiology and Surgery

Artificial intelligence at the intersection of pathology and radiology in prostate cancer

(2019) Stephanie A. Harmon et al.   Diagnostic and Interventional Radiology

Clinically significant prostate cancer detection on MRI: A radiomic shape features study

(2019) Renato Cuocolo et al.   EUROPEAN JOURNAL OF RADIOLOGY

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

(2019) Michela Antonelli et al.   EUROPEAN RADIOLOGY

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

(2019) Jussi Toivonen et al.   PLoS One

Computer-aided diagnosis system for characterizing ISUP grade ≥ 2 prostate cancers at multiparametric MRI: A cross-vendor evaluation

(2019) S. Transin et al.   Diagnostic and Interventional Imaging

Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network

(2019) Nader Aldoj et al.   EUROPEAN RADIOLOGY

Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet

(2019) Ruiming Cao et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Machine Learning Principles for Radiology Investigators

(2019) Stephen M. Borstelmann   ACADEMIC RADIOLOGY

Künstliche Intelligenz und Radiomics in der MRT-basierten Prostatadiagnostik

(2019) Charlie Alexander Hamm et al.   RADIOLOGE

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment

(2019) Patrick Schelb et al.   RADIOLOGY

Prostate Cancer Detection using Deep Convolutional Neural Networks

(2019) Sunghwan Yoo et al.   Scientific Reports

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning

(2018) Yu Sun et al.   ACTA ONCOLOGICA

Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm

(2018) Junichiro Ishioka et al.   BJU INTERNATIONAL

Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study

(2018) Jiance Li et al.   EUROPEAN JOURNAL OF RADIOLOGY

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network

(2018) Zhiwei Wang et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI

(2018) Yang Song et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy

(2018) Au Hoang Dinh et al.   RADIOLOGY

MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

(2018) Farzad Khalvati et al.   BMC MEDICAL IMAGING

Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

(2018) Bejoy Abraham et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis

(2018) Satheesh Krishna et al.   EUROPEAN RADIOLOGY

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values

(2018) David Bonekamp 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

Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy

(2018) Riccardo Campa et al.   Abdominal Radiology

Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI

(2018) Xinran Zhong et al.   Abdominal Radiology

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

(2017) Jing Wang et al.   EUROPEAN RADIOLOGY

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI

(2017) Xin Yang et al.   MEDICAL IMAGE ANALYSIS

MRI-based prostate cancer detection with high-level representation and hierarchical classification

(2017) Yulian Zhu et al.   MEDICAL PHYSICS

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

(2017) Anika Thon et al.   PLoS One

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study

(2016) Shoshana B. Ginsburg et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Area under the Free-Response ROC Curve (FROC) and a Related Summary Index

(2008) Andriy I. Bandos et al.   BIOMETRICS