Artzi Moran, Blumenthal Deborah T, Bokstein Felix, Nadav Guy, Liberman Gilad, Aizenstein Orna, Ben Bashat Dafna
Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizman St, 64239, Tel Aviv, Israel.
J Neurooncol. 2015 Jan;121(2):349-57. doi: 10.1007/s11060-014-1639-3. Epub 2014 Nov 5.
This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.
本研究提出了一种基于动态对比增强（DCE）和动态磁敏感对比（DSC）磁共振成像（MRI），在胶质母细胞瘤（GB）患者个体水平上自动识别和量化不同组织成分的方法，这些组织成分包括非强化浸润性肿瘤、血管源性水肿和强化肿瘤区域。纳入了从12例GB患者获得的19套MR数据集。7例患者在开始使用贝伐单抗前及用药8周后进行了扫描。在组水平上，基于DCE和DSC的时间数据，使用k均值算法进行肿瘤区域分割，在个体水平上进一步使用支持向量机算法。根据所获得成分的时间特征、计算得到的灌注和通透性值以及磁共振波谱，将其与不同的组织类型相关联。该方法能够将肿瘤区域分割为强化可渗透成分；与血管源性水肿相关的非强化低灌注成分；以及与浸润性肿瘤相关的非强化高灌注成分。在组水平的无监督分类结果和个体水平的监督分类结果之间取得了良好的一致性，具有显著相关性（r = 0.93，p < 0.001），平均对称均方根表面距离为2.5±5.1 mm。在治疗过程中评估了这三种成分体积的纵向变化。利用DCE和DSC进行肿瘤区域分割可用于区分GB患者的血管源性水肿和浸润性肿瘤，这在治疗反应评估中具有重要的临床意义。
