Department of Radiology, Gulhane Training and Research Hospital, General Dr Tevfik Saglam St, 06010 Kecioren, Ankara, Turkey.
Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey.
AJR Am J Roentgenol. 2020 Nov;215(5):1104-1112. doi: 10.2214/AJR.20.22843. Epub 2020 Sep 9.
The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K), reverse volume transfer constant (k), plasma volume fraction (V), extravascular extracellular space volume fraction (V), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K, k, V, and V), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.
本研究旨在探究前列腺多参数 MRI（mpMRI）半定量和定量药代动力学参数以及定量表观扩散系数（ADC）值在客观鉴别前列腺癌（PCa）和前列腺炎中的诊断性能。我们回顾性分析了 2015 年 1 月至 2018 年 2 月间接受 mpMRI 检查且经活检证实为 PCa 或前列腺炎的患者。计算了病变和对侧正常前列腺组织的平均 ADC、前向容积转移常数（K）、反向容积转移常数（k）、血浆容积分数（V）、血管外细胞外空间容积分数（V）和达峰时间（TTP）值。分析了信号强度-时间曲线。还计算了药代动力学参数的病变与正常前列腺组织比值。分析了所有参数的诊断准确性和截断值，以鉴别 PCa 和前列腺炎。本研究共纳入 138 例患者（94 例 PCa，44 例前列腺炎）。统计分析显示，ADC、定量药代动力学参数（K、k、V 和 V）、其病变与正常前列腺组织比值和 TTP 值可成功鉴别 PCa 和前列腺炎。令人惊讶的是，我们发现前列腺炎病变中的 V 值明显较高。这些参数的组合具有 92.7%的总体诊断准确性。ADC、k 和 TTP 构成了鉴别诊断最成功的组合。信号强度-时间曲线分析显示，PCa 患者的增强曲线模式主要为 2 型和 3 型，而前列腺炎患者中未见 3 型曲线。mpMRI 的定量分析具有较高的灵敏度和特异性，可区分 PCa 和前列腺炎，似乎具有显著的应用潜力，可提高诊断准确性。此外，评估这些参数不会给患者带来任何额外负担。
