Luminal Water Imaging: Comparison With Diffusion‐Weighted Imaging (DWI) and PI‐RADS for Characterization of Prostate Cancer Aggressiveness

2020 ◽  
Vol 52 (1) ◽  
pp. 271-279 ◽  
Author(s):  
Stefanie J. Hectors ◽  
Daniela Said ◽  
Jeffrey Gnerre ◽  
Ashutosh Tewari ◽  
Bachir Taouli
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Cancer Aggressiveness

scholarly journals Comparison ofT2*mapping with diffusion-weighted imaging in the characterization of low-gradevsintermediate-grade and high-grade prostate cancer

2016 ◽  
Vol 89 (1063) ◽  
pp. 20151076 ◽  
Author(s):  
Lian-Ming Wu ◽  
Zi-Zhou Zhao ◽  
Xiao-Xi Chen ◽  
Qing Lu ◽  
Shi-Teng Suo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
High Grade ◽  
Diffusion Weighted

scholarly journals Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

2021 ◽  
Author(s):  
Sean D. McGarry ◽  
Michael Brehler ◽  
John D. Bukowy ◽  
Allison K. Lowman ◽  
Samuel A. Bobholz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Cancer Aggressiveness

Relaxation along fictitious field, diffusion-weighted imaging, and T2mapping of prostate cancer: Prediction of cancer aggressiveness

2015 ◽  
Vol 75 (5) ◽  
pp. 2130-2140 ◽  
Author(s):  
Ivan Jambor ◽  
Marko Pesola ◽  
Harri Merisaari ◽  
Pekka Taimen ◽  
Peter J. Boström ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Cancer Prediction ◽  
Diffusion Weighted ◽  
Field Diffusion ◽  
Cancer Aggressiveness

Prostate Cancer Aggressiveness: In Vivo Assessment of MR Spectroscopy and Diffusion-weighted Imaging at 3 T

Radiology ◽  
2012 ◽  
Vol 265 (2) ◽  
pp. 457-467 ◽  
Author(s):  
Thiele Kobus ◽  
Pieter C. Vos ◽  
Thomas Hambrock ◽  
Maarten De Rooij ◽  
Christina A. Hulsbergen—Van de Kaa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mr Spectroscopy ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Cancer Aggressiveness ◽  
And Diffusion ◽  
In Vivo Assessment

scholarly journals Characterization of prostate cancer with MR spectroscopic imaging and diffusion-weighted imaging at 3 Tesla

2019 ◽  
Vol 55 ◽  
pp. 93-102 ◽  
Author(s):  
Yousef Mazaheri ◽  
Amita Shukla-Dave ◽  
Debra A. Goldman ◽  
Chaya S. Moskowitz ◽  
Toshikazu Takeda ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Spectroscopic Imaging ◽  
Diffusion Weighted ◽  
And Diffusion ◽  
Mr Spectroscopic Imaging

scholarly journals Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
V. Brancato ◽  
C. Cavaliere ◽  
M. Salvatore ◽  
S. Monti
Keyword(s):  
Prostate Cancer ◽  
Systematic Review ◽  
Diffusion Weighted Imaging ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Gaussian Model ◽  
Model Parameters ◽  
Diffusion Weighted ◽  
Non Gaussian

AbstractThe importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

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