Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

[1]  S. Crouzet,et al.  Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? - A systematic review. , 2022, Diagnostic and interventional imaging.

[2]  H. Huisman,et al.  A concurrent, deep learning–based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists , 2022, European Radiology.

[3]  R. Souchon,et al.  Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI. , 2022, Diagnostic and interventional imaging.

[4]  H. Schlemmer,et al.  Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection , 2022, Investigative radiology.

[5]  L. Krane,et al.  Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review , 2022, Current Urology Reports.

[6]  C. Tan,et al.  Comparison of diagnostic performance and inter-reader agreement between PI-RADS v2.1 and PI-RADS v2: systematic review and meta-analysis. , 2021, The British journal of radiology.

[7]  H. Huisman,et al.  ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging , 2021, European Radiology.

[8]  Jeong Kon Kim,et al.  Performance of Prostate Imaging Reporting and Data System Version 2.1 for Diagnosis of Prostate Cancer: A Systematic Review and Meta‐Analysis , 2021, Journal of magnetic resonance imaging : JMRI.

[9]  N. Rupp,et al.  Comparison of the PI-RADS 2.1 scoring system to PI-RADS 2.0: Impact on diagnostic accuracy and inter-reader agreement , 2020, PloS one.

[10]  P. Asbach,et al.  Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer , 2020, Scientific Reports.

[11]  Zhengyu Jin,et al.  Comparison of PI-RADS version 2.1 and PI-RADS version 2 regarding interreader variability and diagnostic accuracy for transition zone prostate cancer , 2020, Abdominal Radiology.

[12]  William R. Bradley,et al.  PI-RADS versions 2 and 2.1: Interobserver Agreement and Diagnostic Performance in Peripheral and Transition Zone Lesions Among Six Radiologists. , 2020, AJR. American journal of roentgenology.

[13]  P. Choyke,et al.  Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection. , 2020, AJR. American journal of roentgenology.

[14]  Derya Yakar,et al.  Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI. , 2020, AJR. American journal of roentgenology.

[15]  Junkang Shen,et al.  Diagnostic Accuracy and Inter-observer Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers. , 2020, AJR. American journal of roentgenology.

[16]  Jeong Kon Kim,et al.  Risk Stratification of Prostate Cancer According to PI-RADS Version 2 Categories: Meta-Analysis for Prospective Studies. , 2020, The Journal of urology.

[17]  G. Villeirs,et al.  How does PI-RADS v2.1 impact patient classification? A head-to-head comparison between PI-RADS v2.0 and v2.1 , 2020, Acta radiologica.

[18]  A. Padhani,et al.  ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists’ training , 2020, European Radiology.

[19]  Baris Turkbey,et al.  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, Radiology.

[20]  P. Asbach,et al.  Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer , 2020, European Radiology.

[21]  M. McInnes,et al.  Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes , 2020, European Radiology.

[22]  A. Rosenkrantz,et al.  Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review. , 2020, European urology oncology.

[23]  F. Montorsi,et al.  Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1 , 2020, European Radiology.

[24]  Jeong Kon Kim,et al.  Direct Comparison of PI‐RADS Version 2 and 2.1 in Transition Zone Lesions for Detection of Prostate Cancer: Preliminary Experience , 2020, Journal of magnetic resonance imaging : JMRI.

[25]  L. Schimmöller,et al.  Perspective: a critical assessment of PI-RADS 2.1 , 2020, Abdominal Radiology.

[26]  T. Mussi,et al.  Interobserver agreement of PI‐RADS v. 2 lexicon among radiologists with different levels of experience , 2020, Journal of magnetic resonance imaging : JMRI.

[27]  Xiaoying Wang,et al.  Feasibility of integrating computer-aided diagnosis with structured reports of prostate multiparametric MRI. , 2019, Clinical imaging.

[28]  T. Sone,et al.  Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer. , 2019, European journal of radiology.

[29]  D. Margolis,et al.  Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. , 2019, European urology.

[30]  G. Carvalhal,et al.  Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer , 2019, World Journal of Urology.

[31]  O. Rouvière,et al.  The primacy of multiparametric MRI in men with suspected prostate cancer , 2019, European Radiology.

[32]  Baris Turkbey,et al.  Intra‐ and interreader reproducibility of PI‐RADSv2: A multireader study , 2019, Journal of magnetic resonance imaging : JMRI.

[33]  Ewout W Steyerberg,et al.  Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. , 2019, The Cochrane database of systematic reviews.

[34]  Nathan Lay,et al.  Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI. , 2019, AJR. American journal of roentgenology.

[35]  Prasad R. Shankar,et al.  A Systematic Review of the Existing Prostate Imaging Reporting and Data System Version 2 (PI-RADSv2) Literature and Subset Meta-Analysis of PI-RADSv2 Categories Stratified by Gleason Scores. , 2019, AJR. American journal of roentgenology.

[36]  N. Obuchowski,et al.  Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials , 2018, Journal of magnetic resonance imaging : JMRI.

[37]  M. Esposito,et al.  Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position - A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[38]  J. Fütterer,et al.  Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation , 2018, Oncotarget.

[39]  S. Crouzet,et al.  Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate. , 2018, Diagnostic and interventional imaging.

[40]  H. G. van der Poel,et al.  EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. , 2017, European urology.

[41]  Carole Lartizien,et al.  Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging--influence of a computer-aided diagnosis system. , 2014, Radiology.

[42]  S. Crouzet,et al.  Value of prostate multiparametric magnetic resonance imaging for predicting biopsy results in first or repeat biopsy. , 2014, Clinical radiology.

[43]  P. Qiu The Statistical Evaluation of Medical Tests for Classification and Prediction , 2005 .

[44]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[45]  Margaret Sullivan Pepe,et al.  Distribution-free ROC analysis using binary regression techniques. , 2002, Biostatistics.