Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma.
Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma.
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DOI：
10.3174/ajnr.a4451
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发表时间：
2015-12
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影响因子：
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通讯作者：
Erickson BJ
中科院分区：
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作者：
Hu LS;Kelm Z;Korfiatis P;Dueck AC;Elrod C;Ellingson BM;Kaufmann TJ;Eschbacher JM;Karis JP;Smith K;Nakaji P;Brinkman D;Pafundi D;Baxter LC;Erickson BJ
为了确定建模实施的差异是否会影响3T场强下T2*加权动态磁化率加权对比增强(DSC)-MRI测量的渗漏效应(血脑屏障破坏)和相对脑血容量(RCBV)计算的校正。这项符合HIPAA标准的研究包括52名接受DSC-MRI检查的胶质瘤患者。36名患者同时接受了非预负荷剂量(PLD)和PLD校正后的DSC采集，16名患者仅接受PLD校正后的采集。对于每笔收购，我们使用两个独立的、广泛发布的、FDA批准的商业软件包：IB Neuro(IBN)和NordicICE(NICE)生成两套rCBV指标。我们计算了肿瘤体积内的4个rCBV指标：平均rCBV、模式rCBV、rCBV>；1.75的体素百分比(%>；1.75)和rCBV>；1.0的体素百分比(肿瘤负荷分数或FTB)。我们确定了皮尔逊(R)和斯皮尔曼(ρ)之间非经PLD和经PLD校正的指标之间的相关性。在一组复发的胶质母细胞瘤患者(n=25)中，我们确定了受试者操作员特征(ROC)曲线下面积(AUC)的FTB准确性，以预测肿瘤复发与治疗后疗效(PTRE)的组织诊断。我们还确定了12名患者(n=29)的rCBV和来自立体定向活检的微血管面积(MVA)的相关性。使用IBN，FTB(r=0.96，ρ=0.94)、%>；1.75(r=0.93，ρ=0.91)、平均值(r=0.87，ρ=0.86)和模式(r=0.78，ρ=0.76)的rCBV指标在未校正和校正后的条件下高度相关。这些相关性与NICE的相关性大幅下降。FTB诊断肿瘤与PTRE的准确率IBN高于NICE(AUC=0.85vs0.67)(p<；0.01)。相关系数最高的是PLD和IBN(r=0.64，ρ=0.58，p=0.001)。磁共振灌注成像软件建模的不同实现方式会影响渗漏校正、rCBV计算的准确性以及与组织学基准的相关性。
To determine whether differences in modeling implementation will impact the correction of leakage effects (from blood brain barrier disruption) and relative cerebral blood volume (rCBV) calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced (DSC)-MRI at 3T field strength. This HIPAA-compliant study included 52 glioma patients undergoing DSC-MRI. Thirty-six patients underwent both non Preload Dose (PLD) and PLD-corrected DSC acquisitions, with sixteen patients undergoing PLD-corrected acquisitions only. For each acquisition, we generated two sets of rCBV metrics using two separate, widely published, FDA-approved commercial software packages: IB Neuro (IBN) and NordicICE (NICE). We calculated 4 rCBV metrics within tumor volumes: mean rCBV, mode rCBV, percentage of voxels with rCBV > 1.75 (%>1.75), and percentage of voxels with rCBV > 1.0 (Fractional Tumor Burden or FTB). We determined Pearson (r) and Spearman (ρ) correlations between non-PLD- and PLD-corrected metrics. In a subset of recurrent glioblastoma patients (n=25), we determined Receiver Operator Characteristic (ROC) Areas-Under-Curve (AUC) for FTB accuracy to predict the tissue diagnosis of tumor recurrence versus post-treatment effect (PTRE). We also determined correlations between rCBV and microvessel area (MVA) from stereotactic biopsies (n=29) in twelve patients. Using IBN, rCBV metrics correlated highly between non-PLD- and PLD-corrected conditions for FTB (r=0.96, ρ=0.94), %>1.75 (r=0.93, ρ=0.91), mean (r=0.87, ρ=0.86) and mode (r=0.78, ρ=0.76). These correlations dropped substantially with NICE. Using FTB, IBN was more accurate than NICE in diagnosing tumor vs PTRE (AUC=0.85 vs 0.67) (p<0.01). The highest rCBV-MVA correlations required PLD and IBN (r=0.64, ρ=0.58, p=0.001). Different implementations of perfusion MRI software modeling can impact the accuracy of leakage correction, rCBV calculation, and correlations with histologic benchmarks.