Quantitative Tumor Segmentation for Evaluation of Extent of Glioblastoma Resection to Facilitate Multisite Clinical Trials
Quantitative Tumor Segmentation for Evaluation of Extent of Glioblastoma Resection to Facilitate Multisite Clinical Trials
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DOI：
10.1593/tlo.13835
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发表时间：
2014-02-01
影响因子：
5
通讯作者：
Holder, Chad A.
中科院分区：
文献类型：
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作者：
Cordova, James S.;Schreibmann, Eduard;Holder, Chad A.
胶质母细胞瘤是最常见和最具侵袭性的原发性成人脑肿瘤，其标准治疗是最安全的切除术，其次是放疗和化疗。由于最大限度地切除可能对这些患者有益，因此目前正在评估通过术中5-氨基乙酰丙酸荧光引导手术（FGS）等方法改善肿瘤切除范围（EOR）。然而，由于缺乏可靠的肿瘤分割方法，特别是术后磁共振成像（MRI）扫描，在这些研究中难以重复判断EOR。因此，需要一种可靠的、易于分发的分割方法来进行有效的比较，特别是在多个站点之间。我们报告了一种分割方法，结合了多功能区域的兴趣斑点生成与自动聚类方法。我们将其应用于接受FGS的胶质母细胞瘤病例和匹配的对照组，以说明该方法的可靠性和准确性。使用一致性相关系数评估分割之间的一致性和评价者间变异性，并使用Dice相似性指数和平均欧氏距离确定空间准确性。具有三个类别的模糊C-均值聚类是最佳的执行方法，生成的体积与手动轮廓高度一致，术前和术后的评价者之间高度一致。所提出的分割方法允许在多中心试验中量化EOR所需的无偏、可重复的方式中对对比增强的T-1加权图像进行肿瘤体积测量。
Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T-1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.