Background

Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 requires multiparametric MRI of the prostate, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging sequences; however, the contribution of DCE imaging remains unclear.

Purpose

To assess whether DCE imaging in addition to apparent diffusion coefficient (ADC) and normalized T2 values improves PI-RADS version 2.0 for prediction of clinically significant prostate cancer (csPCa).

Materials and Methods

In this retrospective study, clinically reported PI-RADS lesions in consecutive men who underwent 3-T multiparametric MRI (T2-weighted, DWI, and DCE MRI) from May 2015 to September 2016 were analyzed quantitatively and compared with systematic and targeted MRI–transrectal US fusion biopsy. The normalized T2 signal (nT2), ADC measurement, mean early-phase DCE signal (mDCE), and heuristic DCE parameters were calculated. Logistic regression analysis indicated the most predictive DCE parameters for csPCa (Gleason grade group ≥2). Receiver operating characteristic parameter models were compared using the Obuchowski test. Recursive partitioning analysis determined ADC and mDCE value ranges for combined use with PI-RADS.

Results

Overall, 260 men (median age, 64 years [IQR, 58–69 years]) with 432 lesions (csPCa [n = 152] and no csPCa [n = 280]) were included. The mDCE parameter was predictive of csPCa when accounting for the ADC and nT2 parameter in the peripheral zone (odds ratio [OR], 1.76; 95% CI: 1.30, 2.44; P = .001) but not the transition zone (OR, 1.17; 95% CI: 0.81, 1.69; P = .41). Recursive partitioning analysis selected an ADC cutoff of 0.897 × 10⁻³ mm²/sec (P = .04) as a classifier for peripheral zone lesions with a PI-RADS score assessed on the ADC map (hereafter, ADC PI-RADS) of 3. The mDCE parameter did not differentiate ADC PI-RADS 3 lesions (P = .11), but classified lesions with ADC PI-RADS scores greater than 3 with low ADC values (less than 0.903 × 10⁻³ mm²/sec, P < .001) into groups with csPCa rates of 70% and 97% (P = .008). A lesion size cutoff of 1.5 cm and qualitative DCE parameters were not defined as classifiers according to recursive partitioning (P > .05).

Conclusion

Quantitative or qualitative dynamic contrast-enhanced MRI was not relevant for Prostate Imaging Reporting and Data System (PI-RADS) 3 lesion risk stratification, while quantitative apparent diffusion coefficient (ADC) values were helpful in upgrading PI-RADS 3 and PI-RADS 4 lesions. Quantitative ADC measurement may be more important for risk stratification than current methods in future versions of PI-RADS.

© RSNA, 2022

Online supplemental material is available for this article

See also the editorial by Goh in this issue.

中文翻译：

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动态对比增强和弥散 MRI 对 PI-RADS 检测临床显着前列腺癌的贡献

背景

前列腺成像报告和数据系统 (PI-RADS) 2.0 版需要前列腺的多参数 MRI，包括弥散加权成像 (DWI) 和动态对比增强 (DCE) 成像序列；然而，DCE 成像的贡献仍不清楚。

目的

评估除了表观扩散系数 (ADC) 和标准化 T2 值之外的 DCE 成像是否改进了 PI-RADS 2.0 版以预测具有临床意义的前列腺癌 (csPCa)。

材料和方法

在这项回顾性研究中，对 2015 年 5 月至 2016 年 9 月接受 3-T 多参数 MRI（T2 加权、DWI 和 DCE MRI）的连续男性的临床报告 PI-RADS 病变进行定量分析，并与系统和靶向 MRI-经直肠进行比较美国融合活检。计算归一化 T2 信号 (nT2)、ADC 测量值、平均早期 DCE 信号 (mDCE) 和启发式 DCE 参数。逻辑回归分析表明对 csPCa（格里森等级组≥2）最具预测性的 DCE 参数。使用 Obuchowski 测试比较接受者操作特征参数模型。递归分区分析确定了与 PI-RADS 联合使用的 ADC 和 mDCE 值范围。

结果

总体而言，包括260 名男性（中位年龄，64 岁 [IQR，58-69 岁]）和 432 个病变（csPCa [ n = 152] 和无 csPCa [ n = 280]）。当考虑周边区的 ADC 和 nT2 参数时，mDCE 参数可预测 csPCa（比值比 [OR]，1.76；95% CI：1.30，2.44；P = .001）但不是过渡区（OR ， 1.17 ；95% CI：0.81，1.69；P = .41）。^{递归分区分析选择 0.897 × 10 −3} mm ² /sec ( P = .04) 的ADC 截止值作为外围区域病变的分类器，在 ADC 图（以下称为 ADC PI-RADS）上评估的 PI-RADS 评分为3. mDCE 参数没有区分 ADC PI-RADS 3 病灶（P= .11)，但将 ADC PI-RADS 评分大于 3 且 ADC 值较低（小于 0.903 × 10 ^-3 mm ² /sec，P < .001）的病变分类为 csPCa 率为 70% 和 97% 的组（P = .008）。根据递归划分，1.5 cm 的病变大小截止值和定性 DCE 参数未定义为分类器 ( P > .05)。

结论

定量或定性动态对比增强 MRI 与前列腺影像报告和数据系统 (PI-RADS) 3 病变风险分层无关，而定量表观扩散系数 (ADC) 值有助于升级 PI-RADS 3 和 PI-RADS 4病变。在未来版本的 PI-RADS 中，定量 ADC 测量对于风险分层可能比当前方法更重要。

©北美放射学会，2022

本文提供在线补充材料

另见本期 Goh 的社论。