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

V Brancato,C Cavaliere,M Salvatore,S Monti

doi:10.1038/s41598-019-53350-8

Abstract

The 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.

Highlights

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature
Results on f, when significant, revealed a lower value in PCa than in NT31–34,36,43, with the exception of study by Ueda et al.[35] who, found f significantly higher in peripheral zone (PZ) cancer than in normal PZ tissue, they found no significance when performing the same analysis in transitional zone (TZ)
While Linear Discriminant Analysis performed by Pesapane et al.[43] and Valerio et al.[42] showed that the additional use of Intravoxel Incoherent Motion Model (IVIM) increased performances of conventional T2/DWI for PCa detection, ROC analysis performed by Kuru et al proved that none of IVIM parameters yielded a clear added value, such as in Feng et al.[36]

Summary

Introduction

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. The most commonly used prostate cancer screening paradigm consists of the serum prostate-specific antigen (PSA) test, digital rectal examination, transrectal ultrasound (TRUS), and prostatic biopsies Since these methods are often inaccurate and invasive, there is a growing need for non-invasive tools to improve diagnosis of prostate cancer in terms of both detection and characterization. Depending on how much water molecules movement is limited by tissue structure, the DWI signal intensity changes, and this has been proven useful in PCa to distinguish benign from malignant lesions and to characterize aggressiveness in terms of distinction between high- and low-grade tumors and correlation of tumor with Gleason Score (GS)[8,9,10,11,12,13,14]. Correlation with GS is important but it is of clinical importance to separate low Gleason grade PCa lesions from intermediate and high Gleason grade lesions[15]

Objectives

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Results

Conclusion