Deike Katerina, Wiestler Benedikt, Graf Markus, Reimer Caroline, Floca Ralf O, Bäumer Philipp, Kickingereder Philipp, Heiland Sabine, Schlemmer Heinz-Peter, Wick Wolfgang, Bendszus Martin, Radbruch Alexander
Neurological University Clinic, Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
Department of Neurooncology, University of Heidelberg Medical Center, Heidelberg, Germany.
J Neurooncol. 2016 Feb;126(3):463-72. doi: 10.1007/s11060-015-1982-z. Epub 2015 Oct 30.
We analyzed whether the combined visualization of decreased apparent diffusion coefficient (ADC) values and increased cerebral blood volume (CBV) in perfusion imaging can identify prognosis-related growth patterns in patients with newly diagnosed glioblastoma. Sixty-five consecutive patients were examined with diffusion and dynamic susceptibility-weighted contrast-enhanced perfusion weighted MRI. ADC and CBV maps were co-registered on the T1-w image and a region of interest (ROI) was manually delineated encompassing the enhancing lesion. Within this ROI pixels with ADC values <the 30th percentile (ADCmin), pixels with CBV values >the 70th percentile (CBVmax) and the intersection of pixels with ADCmin and CBVmax were automatically calculated and visualized. Initially, all tumors with a mean intersection greater than the upper quartile of the normally distributed mean intersection of all patients were subsumed to the first growth pattern termed big intersection (BI). Subsequently, the remaining tumors' growth patterns were categorized depending on the qualitative representation of ADCmin, CBVmax and their intersection. Log-rank test exposed a significantly longer overall survival of BI (n = 16) compared to non-BI group (n = 49) (p = 0.0057). Thirty-one, four and 14 patients of the non-BI group were classified as predominant ADC-, CBV- and mixed growth group, respectively. In a multivariate Cox regression model, the BI-, CBV- and mixed groups had significantly lower adjusted hazard ratios (p-value, α(Bonferroni) < 0.006) when compared to the reference group ADC: 0.29 (0.0027), 0.11 (0.038) and 0.33 (0.0059). Our study provides evidence that the combination of diffusion and perfusion imaging allows visualization of different glioblastoma growth patterns that are associated with prognosis. A possible biological hypothesis for this finding could be the interpretation of the ADCmin fraction as the invasion-front of tumor cells while the CBVmax fraction might represent the vascular rich tumor border that is "trailing behind" the invasion-front in the ADC group.
我们分析了灌注成像中表观扩散系数（ADC）值降低和脑血容量（CBV）增加的联合可视化是否能识别新诊断胶质母细胞瘤患者的预后相关生长模式。对65例连续患者进行了扩散和动态磁敏感加权对比增强灌注加权MRI检查。将ADC和CBV图与T1加权图像进行配准，并手动勾勒出包含强化病变的感兴趣区域（ROI）。在该ROI内，自动计算并可视化ADC值<第30百分位数（ADCmin）的像素、CBV值>第70百分位数（CBVmax）的像素以及具有ADCmin和CBVmax的像素的交集。最初，所有平均交集大于所有患者正态分布平均交集上四分位数的肿瘤被归入第一种生长模式，称为大交集（BI）。随后，根据ADCmin、CBVmax及其交集的定性表现对其余肿瘤的生长模式进行分类。对数秩检验显示，与非BI组（n = 49）相比，BI组（n = 16）的总生存期显著更长（p = 0.0057）。非BI组的31例、4例和14例患者分别被分类为主导ADC、CBV和混合生长组。在多变量Cox回归模型中，与参考组ADC相比，BI组、CBV组和混合组的调整后风险比显著更低（p值，α（Bonferroni）<0.006）：0.29（0.0027）、0.11（0.038）和0.33（0.0059）。我们的研究提供了证据，表明扩散和灌注成像的联合可以可视化与预后相关的不同胶质母细胞瘤生长模式。这一发现的一个可能生物学假说是，将ADCmin部分解释为肿瘤细胞的侵袭前沿，而CBVmax部分可能代表在ADC组中位于侵袭前沿“后方”的富含血管的肿瘤边界。
