Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

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

Utility of computed diffusion-weighted MRI for predicting aggressiveness of prostate cancer

(2017) Yuma Waseda et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Quantified analysis of histological components and architectural patterns of gleason grades in apparent diffusion coefficient restricted areas upon diffusion weighted MRI for peripheral or transition zone cancer locations

(2017) Olivier Helfrich et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Evaluating the performance of PI-RADS v2 in the non-academic setting

(2017) Eric J. Jordan et al.   Abdominal Radiology

High prostate cancer gene 3 (PCA3) scores are associated with elevated Prostate Imaging Reporting and Data System (PI-RADS) grade and biopsy Gleason score, at magnetic resonance imaging/ultrasonography fusion software-based targeted prostate biopsy after

(2016) Stefano De Luca et al.   BJU INTERNATIONAL

Mean diffusivity discriminates between prostate cancer with grade group 1&2 and grade groups equal to or greater than 3

(2016) M. Nezzo et al.   EUROPEAN JOURNAL OF RADIOLOGY

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

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

Active Surveillance for the Management of Localized Prostate Cancer (Cancer Care Ontario Guideline): American Society of Clinical Oncology Clinical Practice Guideline Endorsement

(2016) Ronald C. Chen et al.   JOURNAL OF CLINICAL ONCOLOGY

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

The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer

(2016) J.E. Thompson et al.   JOURNAL OF UROLOGY

Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging

(2016) Geert J. S. Litjens et al.   RADIOLOGY

Prostate MRI based on PI-RADS version 2: how we review and report

(2016) Philipp Steiger et al.   CANCER IMAGING

The role of MRI in active surveillance for men with localized prostate cancer

(2015) Pedro Recabal et al.   CURRENT OPINION IN 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

Machine Learning methods for Quantitative Radiomic Biomarkers

(2015) Chintan Parmar et al.   Scientific Reports

MR-sequences for prostate cancer diagnostics: validation based on the PI-RADS scoring system and targeted MR-guided in-bore biopsy

(2014) Lars Schimmöller et al.   EUROPEAN RADIOLOGY

Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors

(2014) Shoshana B. Ginsburg et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets

(2013) Robert Toth et al.   COMPUTER VISION AND IMAGE UNDERSTANDING

Inter-reader agreement of the ESUR score for prostate MRI using in-bore MRI-guided biopsies as the reference standard

(2013) L. Schimmöller et al.   EUROPEAN RADIOLOGY

Evaluation of Diffusion-Weighted MR Imaging at Inclusion in an Active Surveillance Protocol for Low-Risk Prostate Cancer

(2013) Diederik M. Somford et al.   INVESTIGATIVE RADIOLOGY

PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland

(2013) Jan Egger   PLoS One

ESUR prostate MR guidelines 2012

(2012) Jelle O. Barentsz et al.   EUROPEAN RADIOLOGY

Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation

(2012) Robert Toth et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2-weighted MR imagery

(2012) Satish E. Viswanath et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation

(2010) Robert Toth et al.   MEDICAL IMAGE ANALYSIS