相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer
Harri Merisaari et al.
MAGNETIC RESONANCE IN MEDICINE (2020)
Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics
Lixin Gong et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2020)
PD52-08 CHANGES IN BLOOD LEVELS OF PROSTATE KALLIKREIN-RELATED AND MICROSEMINOPROTEIN-BETA MARKER LEVELS FOLLOWING 5ARI THERAPY
Emily Vertosick et al.
JOURNAL OF UROLOGY (2020)
Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis
Renato Cuocolo et al.
EUROPEAN RADIOLOGY (2020)
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
Simon Bernatz et al.
EUROPEAN RADIOLOGY (2020)
Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters
Piotr Woznicki et al.
CANCERS (2020)
Avoiding Unnecessary Magnetic Resonance Imaging (MRI) and Biopsies: Negative and Positive Predictive Value of MRI According to Prostate-specific Antigen Density, 4Kscore and Risk Calculators
Ugo Giovanni Falagario et al.
EUROPEAN UROLOGY ONCOLOGY (2020)
Multiparametric MRI and radiomics in prostate cancer: a review
Yu Sun et al.
AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE (2019)
Likert vs. PI-RADS v2: A comparison of two radiological scoring systems for detection of clinically significant prostate cancer
C.C. Khoo et al.
EUROPEAN UROLOGY SUPPLEMENTS (2019)
Association of Treatment With 5-Reductase Inhibitors With Time to Diagnosis and Mortality in Prostate Cancer
Reith R. Sarkar et al.
JAMA INTERNAL MEDICINE (2019)
Clinically significant prostate cancer detection on MRI: A radiomic shape features study
Renato Cuocolo et al.
EUROPEAN JOURNAL OF RADIOLOGY (2019)
Validation of IMPROD biparametric MRI in men with clinically suspected prostate cancer: A prospective multi-institutional trial
Ivan Jambor et al.
PLOS MEDICINE (2019)
Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet
Ruiming Cao et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)
Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment
Patrick Schelb et al.
RADIOLOGY (2019)
Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI
Xinran Zhong et al.
ABDOMINAL RADIOLOGY (2019)
Association of Treatment With 5α-Reductase Inhibitors and Prostate Cancer Mortality Among Older Adults
Abhishek Kumar et al.
JAMA NETWORK OPEN (2019)
Optimizing patient's selection for prostate biopsy: A single institution experience with multi-parametric MRI and the 4Kscore test for the detection of aggressive prostate cancer
Sanoj Punnen et al.
PLOS ONE (2018)
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
David Bonekamp et al.
RADIOLOGY (2018)
Simple biparametric MRI combined with PSA density rules out Gleason–10 prostate cancer and avoids unnecessary biopsies in biopsy-naïve men
L. Boesen et al.
EUROPEAN UROLOGY SUPPLEMENTS (2018)
Assessment of the Diagnostic Accuracy of Biparametric Magnetic Resonance Imaging for Prostate Cancer in Biopsy-Naive Men The Biparametric MRI for Detection of Prostate Cancer (BIDOC) Study
Lars Boesen et al.
JAMA NETWORK OPEN (2018)
Novel Biparametric MRI and Targeted Biopsy Improves Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer (IMPROD Trial)
Ivan Jambor et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2017)
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen et al.
CANCER RESEARCH (2017)
The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma Definition of Grading Patterns and Proposal for a New Grading System
Jonathan I. Epstein et al.
AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2016)
Prostate cancer detection with magnetic resonance-ultrasound fusion biopsy: The role of systematic and targeted biopsies
Christopher P. Filson et al.
CANCER (2016)
A Four-kallikrein Panel Predicts High-grade Cancer on Biopsy: Independent Validation in a Community Cohort
Katharina Braun et al.
EUROPEAN UROLOGY (2016)
PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2
Jeffrey C. Weinreb et al.
EUROPEAN UROLOGY (2016)
STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration
Jeremie F. Cohen et al.
BMJ OPEN (2016)
Predicting High-Grade Cancer at Ten-Core Prostate Biopsy Using Four Kallikrein Markers Measured in Blood in the ProtecT Study
Richard J. Bryant et al.
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2015)
Predicting High-Grade Cancer at Ten-Core Prostate Biopsy Using Four Kallikrein Markers Measured in Blood in the ProtecT Study
Richard J. Bryant et al.
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2015)
Prebiopsy Multiparametric 3T Prostate MRI in Patients With Elevated PSA, Normal Digital Rectal Examination, and No Previous Biopsy
Ivan Jambor et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2015)
Standards of Reporting for MRI-targeted Biopsy Studies (START) of the Prostate: Recommendations from an International Working Group
Caroline M. Moore et al.
EUROPEAN UROLOGY (2013)
A panel of kallikrein markers can predict outcome of prostate biopsy following clinical work-up: an independent validation study from the European Randomized Study of Prostate Cancer screening, France
Amine Benchikh et al.
BMC CANCER (2010)