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。Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
Elena Bertelli et al.
FRONTIERS IN ONCOLOGY (2022)
Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions
Pengfei Jin et al.
FRONTIERS IN ONCOLOGY (2022)
Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status
Xiaoyang Liu et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2022)
Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions
Valentina Brancato et al.
SCIENTIFIC REPORTS (2021)
A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade
Jose M. Castillo T. et al.
DIAGNOSTICS (2021)
Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions
Stefanie J. Hectors et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)
Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer
Jeroen Bleker et al.
INSIGHTS INTO IMAGING (2021)
Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis
Christopher S. Lim et al.
ABDOMINAL RADIOLOGY (2021)
PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer
Dario Giambelluca et al.
CURRENT PROBLEMS IN DIAGNOSTIC RADIOLOGY (2021)
The Use of Multiparametric Magnetic Resonance Imaging for Follow-up of Patients Included in Active Surveillance Protocol. Can PSA Density Discriminate Patients at Different Risk of Reclassification?
Marco Roscigno et al.
CLINICAL GENITOURINARY CANCER (2020)
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg et al.
RADIOLOGY (2020)
A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions
Ying Hou et al.
ABDOMINAL RADIOLOGY (2020)
Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2
Baris Turkbey et al.
EUROPEAN UROLOGY (2019)
PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway
Anwar R. Padhani et al.
RADIOLOGY (2019)
MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis
V Kasivisvanathan et al.
NEW ENGLAND JOURNAL OF MEDICINE (2018)
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
David Bonekamp et al.
RADIOLOGY (2018)
Elastic Versus Rigid Image Registration in Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy: A Systematic Review and Meta-analysis
Wulphert Venderink et al.
EUROPEAN UROLOGY FOCUS (2018)
MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions?
Ivo G. Schoots
TRANSLATIONAL ANDROLOGY AND UROLOGY (2018)
Multicentre evaluation of targeted and systematic biopsies using magnetic resonance and ultrasound image-fusion guided transperineal prostate biopsy in patients with a previous negative biopsy
Nienke L. Hansen et al.
BJU INTERNATIONAL (2017)
Accuracy and Agreement of PIRADSv2 for Prostate Cancer mpMRI: A Multireader Study
Matthew D. Greer et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (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)
Magnetic Resonance and Ultrasound Image Fusion Supported Transperineal Prostate Biopsy Using the Ginsburg Protocol: Technique, Learning Points, and Biopsy Results
Nienke Hansen et al.
EUROPEAN UROLOGY (2016)
Global Cancer Statistics, 2012
Lindsey A. Torre et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2015)
Do apparent diffusion coefficient (ADC) values obtained using high b-values with a 3-T MRI correlate better than a transrectal ultrasound (TRUS)-guided biopsy with true Gleason scores obtained from radical prostatectomy specimens for patients with prostate cancer?
Kazuhiro Kitajima et al.
EUROPEAN JOURNAL OF RADIOLOGY (2013)
Correlation of Gleason Scores with Diffusion-Weighted Imaging Findings of Prostate Cancer
Rajakumar Nagarajan et al.
ADVANCES IN UROLOGY (2012)
Multiparametric magnetic resonance imaging for the detection and localization of prostate cancer: combination of T2-weighted, dynamic contrast-enhanced and diffusion-weighted imaging
Nicolas Barry Delongchamps et al.
BJU INTERNATIONAL (2011)
Relationship between Apparent Diffusion Coefficients at 3.0-T MR Imaging and Gleason Grade in Peripheral Zone Prostate Cancer
Thomas Hambrock et al.
RADIOLOGY (2011)
Apparent diffusion coefficient values in peripheral and transition zones of the prostate: Comparison between normal and malignant prostatic tissues and correlation with histologic grade
Tsutomu Tamada et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2008)
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Paul A. Yushkevich et al.
NEUROIMAGE (2006)
The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma
JI Epstein et al.
AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2005)