重新审视 DCE-MRI：利用高时空分辨率 DCE-MRI 采集获得的描述性信号增强特征对前列腺组织进行分类。
Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution.
机构信息
From the Department of Radiology, University Hospital Basel, Basel, Switzerland.
NYU Langone Medical Center, New York, NY.
出版信息
Invest Radiol. 2021 Sep 1;56(9):553-562. doi: 10.1097/RLI.0000000000000772.
METHODS
A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation.
RESULTS
There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%).
CONCLUSIONS
Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.
方法
本回顾性研究（2016 年 1 月至 2019 年 7 月）纳入了 75 例患者（平均年龄 65 岁，年龄范围为 46-80 岁），这些患者具有 2.5 秒时间分辨率的 DCE-MRI 和 PIRADS 4 或 5 级病变。54 例患者经活检证实为前列腺癌（Gleason 6，15；Gleason 7，20；Gleason 8，13；Gleason 9，6），而 21 例患者 MRI/超声融合引导下的活检结果为阴性。使用定制软件对所有时间点的对比信号增强进行体素分析，包括自动动脉输入函数检测。计算了 7 个描述性参数图：归一化最大信号强度、起始时间、最大时间、最大斜率时间、最大斜率归一化最大信号和动脉输入函数（SMN1、SMN2）。使用多参数机器学习模型将参数与 ADC 进行比较，以确定分类准确性。采用 Wilcoxon 检验进行假设检验，采用 Spearman 系数进行相关性分析。
结果
在正常外周带与 PIRADS 4 或 5 级病变之间，以及活检阳性与活检阴性病变之间，所有 7 个 DCE 衍生参数均有显著差异（P < 0.05）。多参数分析显示，与 ADC 相比，ADC+DCE 作为输入时的性能更好（准确性/敏感性/特异性，97%/93%/100%），而 ADC 单独作为输入时的性能为（准确性/敏感性/特异性，94%/95%/95%），DCE 单独作为输入时的性能为（准确性/敏感性/特异性，78%/79%/77%），在区分正常外周带与 PIRADS 病变、活检阳性与活检阴性病变（准确性/敏感性/特异性，68%/33%/81%），以及区分 Gleason 6 与≥7 级前列腺癌（准确性/敏感性/特异性，69%/60%/72%）方面。
结论
使用无模型计算得出的高分辨率 DCE-MRI 得出的描述性灌注特征在正常组织和癌组织之间有显著差异，但尚未达到仅基于 ADC 分类的准确性。将 ADC 与基于 DCE 的输入特征相结合，提高了 PIRADS 病变、活检阳性与活检阴性病变的分类准确性，以及区分 Gleason 6 与 Gleason≥7 级病变的准确性。
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