Histogram analysis of stretched‐exponential and monoexponential diffusion‐weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

Abstract

3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.

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

“…In the study by Liu et al . 50 DDC was found to be the only SE parameter able to differentiate low- from high-grade tumors, showing a significant negative correlation with GS. Merisaari et al .…”

“…49 3.04000/71.9SS-EPI0, 500, 1000, 2000S(b)/S 0 = exp[−(b∙DDC) α ]DDC, αNR—NRLiu et al . 50 3.04000/71.9SS-EPI0, 500, 1000, 2000S(b)/S 0 = exp[−(b∙DDC) α ]DDC, αNR—NRRosenkrantz et al . 54 3.03500/81SS-EPI0, 500, 1000, 1500, 2000S(b)/S 0 = exp(−b∙D K + b 2 ∙D K 2 ∙K/6)D K , KNR—NRTamura et al .…”

“…8 studies included SE comparison with ADC:5 on detection 15,36,46,47,49 , 2 on characterization 44,50 , and 1 on both 48 . According to Mazaheri et al .…”

“…ME DWI has been shown to be a useful tool for PCa diagnosis, not only for detection of prostatic lesions, but also for characterization of disease aggressiveness 8,9,16,17 . However, in the last years, due to the limited assumption at the basis of this approach, many Non-Gaussian diffusion models have been proposed to better depict diffusion signal 19,20,23,24 and more and more studies investigated their diagnostic role and clinical value on PCa 13,15,31,33–50,52–61 . Due to the large amount of studies and data contained therein, and the not always clear results regarding the benefits of using Non-Gaussian DWI for PCa diagnosis, we performed a systematic review followed by a meta-analysis, reviewing their parameters and trying to understand if they could provide additional information.…”

Cervical Cancer: Associations between Metabolic Parameters and Whole Lesion Histogram Analysis Derived from Simultaneous 18F-FDG-PET/MRI

“…The histogram analysis approach has been applied in other tumors, for example, in prostate cancer. For example, Liu et al characterized histogram variables of ADC as predictors for the aggressiveness of prostate cancer [ 18 ]. In a study of Shindo et al, ADC histogram analysis has been described as helpful in differentiating pancreatic adenocarcinomas from neuroendocrine tumors [ 19 ].…”