Purpose

Perfusion-weighted (PWI) magnetic resonance imaging (MRI) and O‑(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) positron emission tomography (PET) are both useful for discrimination of progressive disease (PD) from radiation necrosis (RN) in patients with gliomas. Previous literature showed that the combined use of FET-PET and MRI-PWI is advantageous; hhowever the increased diagnostic performances were only modest compared to the use of a single modality. Hence, the goal of this study was to further explore the benefit of combining MRI-PWI and [18F]FET-PET for differentiation of PD from RN. Secondarily, we evaluated the usefulness of cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TTP) as previous studies mainly examined cerebral blood volume (CBV).

Methods

In this single center study, we retrospectively identified patients with WHO grades II–IV gliomas with suspected tumor recurrence, presenting with ambiguous findings on structural MRI. For differentiation of PD from RN we used both MRI-PWI and [18F]FET-PET. Dynamic susceptibility contrast MRI-PWI provided normalized parameters derived from perfusion maps (r(relative)CBV, rCBF, rMTT, rTTP). Static [18F]FET-PET parameters including mean and maximum tumor to brain ratios (TBR_mean, TBR_max) were calculated. Based on histopathology and radioclinical follow-up we diagnosed PD in 27 and RN in 10 cases. Using the receiver operating characteristic (ROC) analysis, area under the curve (AUC) values were calculated for single and multiparametric models. The performances of single and multiparametric approaches were assessed with analysis of variance and cross-validation.

Results

After application of inclusion and exclusion criteria, we included 37 patients in this study. Regarding the in-sample based approach, in single parameter analysis rTBR_mean (AUC = 0.91, p < 0.001), rTBR_max (AUC = 0.89, p < 0.001), rTTP (AUC = 0.87, p < 0.001) and rCBV_mean (AUC = 0.84, p < 0.001) were efficacious for discrimination of PD from RN. The rCBF_mean and rMTT did not reach statistical significance. A classification model consisting of TBR_mean, rCBV_mean and rTTP achieved an AUC of 0.98 (p < 0.001), outperforming the use of rTBR_mean alone, which was the single parametric approach with the highest AUC. Analysis of variance confirmed the superiority of the multiparametric approach over the single parameter one (p = 0.002). While cross-validation attributed the highest AUC value to the model consisting of TBR_mean and rCBV_mean, it also suggested that the addition of rTTP resulted in the highest accuracy. Overall, multiparametric models performed better than single parameter ones.

Conclusion

A multiparametric MRI-PWI and [18F]FET-PET model consisting of TBR_mean, rCBV_mean and PWI rTTP significantly outperformed the use of rTBR_mean alone, which was the best single parameter approach. Secondarily, we firstly report the potential usefulness of PWI rTTP for discrimination of PD from RN in patients with glioma; however, for validation of our findings the prospective studies with larger patient samples are necessary.

中文翻译：

---

DSC-MR 灌注和 [18F]FET-PET 相结合的多参数分析优于单参数方法来区分进行性胶质瘤与放射性坏死

目的

灌注加权 (PWI) 磁共振成像 (MRI) 和 O-(2-[18F]氟乙基-)-l-酪氨酸 ([18F]FET) 正电子发射断层扫描 (PET) 均有助于鉴别进展性疾病 (PD) ）来自神经胶质瘤患者的放射性坏死（RN）。既往文献表明，FET-PET和MRI-PWI联合使用具有优势；然而，与使用单一模式相比，诊断性能的提高只是有限的。因此，本研究的目的是进一步探讨结合 MRI-PWI 和 [18F]FET-PET 来区分 PD 和 RN 的益处。其次，我们评估了脑血流量（CBF）、平均通过时间（MTT）和达峰时间（TTP）的有用性，因为之前的研究主要检查脑血容量（CBV）。

方法

在这项单中心研究中，我们回顾性地鉴定了疑似肿瘤复发的 WHO II-IV 级神经胶质瘤患者，其结构 MRI 的结果不明确。为了区分 PD 和 RN，我们使用 MRI-PWI 和 [18F]FET-PET。动态磁敏感对比 MRI-PWI 提供源自灌注图的标准化参数（r（相对）CBV、rCBF、rMTT、rTTP）。计算静态[18F]FET-PET参数，包括平均和最大肿瘤与脑比率（TBR_平均值、TBR_{最大值）。}根据组织病理学和放射临床随访，我们诊断出 27 例 PD 和 10 例 RN。使用受试者工作特征 (ROC) 分析，计算单参数和多参数模型的曲线下面积 (AUC) 值。通过方差分析和交叉验证来评估单参数和多参数方法的性能。

结果

应用纳入和排除标准后，我们​​将 37 名患者纳入本研究。关于基于样本的方法，在单参数分析中，rTBR_平均值（AUC = 0.91，p  < 0.001）、rTBR _max（AUC = 0.89，p  < 0.001）、rTTP（AUC = 0.87，p  < 0.001）和rCBV_平均值（ AUC = 0.84，p  < 0.001）可有效区分 PD 和 RN。rCBF_平均值和 rMTT 未达到统计学显着性。_{由 TBR平均值}、rCBV_平均值和 rTTP组成的分类模型的AUC 为 0.98 ( p  < 0.001)，优于单独使用 rTBR_平均值，这是 AUC 最高的单参数方法。方差分析证实了多参数方法相对于单参数方法的优越性 ( p  = 0.002)。虽然交叉验证将最高的 AUC 值归因于由 TBR_平均值和 rCBV_平均值组成的模型，但它也表明添加 rTTP 会导致最高的准确性。总体而言，多参数模型的表现优于单参数模型。

结论

由 TBR_平均值、rCBV_平均值和 PWI rTTP 组成的多参数 MRI-PWI 和 [18F]FET-PET 模型显着优于单独使用 rTBR 平均值_，这是最佳的单参数方法。其次，我们首先报告了 PWI rTTP 在区分神经胶质瘤患者的 PD 和 RN 方面的潜在用途；然而，为了验证我们的研究结果，有必要对更大的患者样本进行前瞻性研究。