Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

(2021) Valentina Brancato et al.   Scientific Reports

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

(2021) Hyuna Sung et al.   CA-A CANCER JOURNAL FOR CLINICIANS

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

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

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate

(2021) David J. Winkel et al.   INVESTIGATIVE RADIOLOGY

A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features

(2020) Ji Eun Park et al.   BMC CANCER

Cancer statistics, 2020

(2020) Rebecca L. Siegel et al.   CA-A CANCER JOURNAL FOR CLINICIANS

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

(2020) Jeroen Bleker et al.   EUROPEAN RADIOLOGY

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

(2020) Geert J.L.H. van Leenders et al.   AMERICAN JOURNAL OF SURGICAL PATHOLOGY

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

Using decision curve analysis to benchmark performance of a magnetic resonance imaging–based deep learning model for prostate cancer risk assessment

(2020) Dominik Deniffel et al.   EUROPEAN RADIOLOGY

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

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

(2020) Patrick Schelb 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

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

(2020) Piotr Woźnicki et al.   Cancers

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

(2020) Jose M. Castillo T. 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

Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer

(2020) Yongsheng Zhang et al.   Frontiers in Oncology

Radiomics for Gleason Score Detection through Deep Learning

(2020) Luca Brunese et al.   SENSORS

Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer

(2020) Madhuri Monique Rudolph et al.   Scientific Reports

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

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy

(2020) Yuta Akamine et al.   MAGNETIC RESONANCE IMAGING

Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network

(2020) Yueyue Wang et al.   Physica Medica-European Journal of Medical Physics

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

(2020) Coen de Vente et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

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

Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

(2019) Mark Wu et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Assessment of prostate cancer prognostic Gleason grade group using zonal-specific features extracted from biparametric MRI using a KNN classifier

(2019) Carina Jensen et al.   Journal of Applied Clinical Medical Physics

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

Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions

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

Radiomics features measured with multiparametric MRI predict prostate cancer aggressiveness.

(2019) Stefanie J Hectors et al.   JOURNAL OF UROLOGY

Prostate cancer detection using residual networks

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

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study

(2019) Peter Q. Lee et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

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

Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI

(2019) B. Liu et al.   CLINICAL RADIOLOGY

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

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

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

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

Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis

(2019) Hassan Bagher-Ebadian et al.   Frontiers in Oncology

Feasibility of integrating computer-aided diagnosis with structured reports of prostate multiparametric MRI

(2019) Lina Zhu et al.   CLINICAL IMAGING

Multiparametric MRI‐Based Radiomics for Prostate Cancer Screening With PSA in 4–10 ng/mL to Reduce Unnecessary Biopsies

(2019) Yafei Qi et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

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

(2019) Patrick Schelb et al.   RADIOLOGY

Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board

(2019) David A. Bluemke et al.   RADIOLOGY

Prostate Cancer Detection using Deep Convolutional Neural Networks

(2019) Sunghwan Yoo et al.   Scientific Reports

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

Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study

(2018) Matthew D. Greer et al.   EUROPEAN 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

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

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

MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis

(2018) Veeru Kasivisvanathan et al.   NEW ENGLAND JOURNAL OF MEDICINE

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

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

(2018) Seong Ho Park et al.   RADIOLOGY

Current Applications and Future Impact of Machine Learning in Radiology

(2018) Garry Choy et al.   RADIOLOGY

Deep Learning Role in Early Diagnosis of Prostate Cancer

(2018) Islam Reda et al.   TECHNOLOGY IN CANCER RESEARCH & TREATMENT

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

(2018) Farzad Khalvati et al.   BMC MEDICAL IMAGING

Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators

(2018) Ben Van Calster et al.   EUROPEAN UROLOGY

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2

(2018) Tong Chen et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

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

Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer

(2018) Ahmad Chaddad et al.   Cancers

Prostate cancer heterogeneity: texture analysis score based on multiple MRI sequences for detection, stratification and selection of lesions at time of biopsy

(2018) Clement Orczyk et al.   BJU INTERNATIONAL

Prediction of prostate cancer by deep learning with multilayer artificial neural network

(2018) Takumi Takeuchi et al.   CUAJ-Canadian Urological Association Journal

Head-to-head Comparison of Transrectal Ultrasound-guided Prostate Biopsy Versus Multiparametric Prostate Resonance Imaging with Subsequent Magnetic Resonance-guided Biopsy in Biopsy-naïve Men with Elevated Prostate-specific Antigen: A Large Prospective Multicenter Clinical Study

(2018) Marloes van der Leest et al.   EUROPEAN UROLOGY

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

(2018) Ruba Alkadi et al.   JOURNAL OF DIGITAL IMAGING

Intra- and interreader reproducibility of PI-RADSv2: A multireader study

(2018) Clayton P. Smith et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study

(2018) Olivier Rouvière et al.   LANCET ONCOLOGY

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

(2018) Xinran Zhong et al.   Abdominal Radiology

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

(2018) Alejandro Rodriguez-Ruiz et al.   JNCI-Journal of the National Cancer Institute

EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

(2017) Nicolas Mottet et al.   EUROPEAN UROLOGY

A survey on deep learning in medical image analysis

(2017) Geert Litjens et al.   MEDICAL IMAGE ANALYSIS

Machine Learning for Medical Imaging

(2017) Bradley J. Erickson et al.   RADIOGRAPHICS

PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2

(2016) Jeffrey C. Weinreb et al.   EUROPEAN UROLOGY

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

(2016) Au Hoang Dinh et al.   RADIOLOGY

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

(2015) Guillaume Lemaître et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

(2015) Kai Zhao et al.   Science China-Life Sciences

Calibration of Risk Prediction Models

(2014) Ben Van Calster et al.   MEDICAL DECISION MAKING