1 Department of Radiology, West China Hospital of Sichuan University, GuoXue Xiang, No. 37 Chengdu, Sichuan Province, 610041, China.
AJR Am J Roentgenol. 2018 Aug;211(2):358-368. doi: 10.2214/AJR.17.18934. Epub 2018 May 29.
We aimed to compare the diagnostic performance of monoexponential DWI and diffusion kurtosis imaging (DKI) for the detection of prostate cancer (PCa).
A systematic literature search was conducted for studies evaluating the diagnostic value of monoexponential DWI and DKI for PCa in the same patient cohorts with sufficient data to construct 2 × 2 contingency tables. Qualities of the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Data were extracted to calculate pooled sensitivities and specificities. We constructed summary ROC curves and calculated AUCs to determine the performances of DKI parameters (diffusion coefficient and kurtosis characterizing the deviation from the monoexponential decay) and apparent diffusion coefficient (ADC) values in diagnosing PCa.
Five studies (463 patients) were included, with eight, nine, and 10 subsets of data available for analysis of ADC, diffusion, and kurtosis, respectively. Pooled sensitivities were 89% (95% CI, 80-94%) for ADC, 91% (95% CI, 84-95%) for diffusion, and 87% (95% CI, 83-91%) for kurtosis. Pooled specificities were 86% (95% CI, 80-90%) for ADC, 78% (95% CI, 71-84%) for diffusion, and 85% (95% CI, 81-89%) for kurtosis. According to the summary ROC analyses, the AUC was 0.93 (95% CI, 0.90-0.95) for ADC, 0.89 (95% CI, 0.86-0.92) for diffusion, and 0.93 (95% CI, 0.90-0.95) for kurtosis. There was no notable publication bias, but significant heterogeneity was observed.
Monoexponential DWI and DKI showed comparable diagnostic accuracies for PCa. DKI is a potentially helpful method for the diagnosis of PCa. Therefore, on the basis of current evidence, we do not recommend including DKI in routine clinical assessment of PCa for the moment.
本研究旨在比较单指数 DWI 和扩散峰度成像（DKI）在检测前列腺癌（PCa）方面的诊断性能。
系统检索了评价单指数 DWI 和 DKI 对同一患者队列中 PCa 诊断价值的研究，这些研究需要有足够的数据来构建 2×2 四格表。采用 Quality Assessment of Diagnostic Accuracy Studies-2 工具评估纳入研究的质量。提取数据以计算汇总敏感度和特异度。我们构建了汇总受试者工作特征曲线，并计算了 AUC，以确定 DKI 参数（扩散系数和峰度，用于描述对单指数衰减的偏离）和表观扩散系数（ADC）值在诊断 PCa 中的表现。
纳入了 5 项研究（463 例患者），其中分别有 8、9 和 10 个子集的数据可用于 ADC、扩散和峰度分析。ADC 的汇总敏感度为 89%（95%CI，80-94%），扩散的汇总敏感度为 91%（95%CI，84-95%），峰度的汇总敏感度为 87%（95%CI，83-91%）。ADC 的汇总特异度为 86%（95%CI，80-90%），扩散的汇总特异度为 78%（95%CI，71-84%），峰度的汇总特异度为 85%（95%CI，81-89%）。根据汇总受试者工作特征分析，ADC 的 AUC 为 0.93（95%CI，0.90-0.95），扩散的 AUC 为 0.89（95%CI，0.86-0.92），峰度的 AUC 为 0.93（95%CI，0.90-0.95）。不存在显著的发表偏倚，但观察到明显的异质性。
单指数 DWI 和 DKI 对 PCa 的诊断准确性相当。DKI 是一种有助于诊断 PCa 的潜在方法。因此，基于当前证据，我们目前不建议将 DKI 纳入 PCa 的常规临床评估。
