IRCCS SDN, Napoli, Italy.
Sci Rep. 2019 Nov 14;9(1):16837. doi: 10.1038/s41598-019-53350-8.
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 I 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.
扩散加权成像（DWI）在前列腺癌（PCa）诊断中的重要性在文献中已有广泛论述。在过去十年中，由于单指数模型的局限性，一些研究探讨了非高斯 DWI 模型及其在 PCa 诊断中的应用。由于研究结果往往不一致且相互矛盾，我们对 2012 年以来研究非高斯 DWI 模型在前列腺癌检测和特征描述中的最常用模型的研究进行了系统回顾。对每个非高斯模型检测前列腺癌病变和区分低级别和中高级别病变的能力进行了荟萃分析。计算了加权均数差和 95%置信区间，并使用 I 统计量估计了异质性。对 29 项研究进行了系统评价，结果表明非高斯模型参数的实际有用性和附加值并不明确，存在不一致性。12 项研究纳入荟萃分析，结果显示，几个非高斯参数对前列腺癌的检测具有统计学意义，而对前列腺癌的特征描述则具有较小的统计学意义。我们的研究结果表明，非高斯模型参数可能在前列腺癌的检测和特征描述中发挥作用，但需要进一步研究以确定用于前列腺癌诊断的标准化 DWI 采集方案。
