Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with radiochemotherapy
Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with radiochemotherapy
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DOI：
10.1093/neuonc/nos314
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发表时间：
2013-03-01
期刊：
影响因子：
15.9
通讯作者：
Pope, Whitney B.
中科院分区：
文献类型：
--
作者：
Ellingson, Benjamin M.;Cloughesy, Timothy F.;Pope, Whitney B.
背景。功能扩散图 (fDM) 是一种癌症成像技术，利用表观扩散系数 (ADC) 的体素变化来评估治疗反应。尽管初步结果很有希望，但图像配准的不确定性仍然是广泛临床应用的最大障碍。当前的研究引入了 fDM 量化的概率方法，以克服其中的一些限制。方法。共有 143 名新诊断的胶质母细胞瘤患者正在接受标准放化疗，参加了这项回顾性研究。传统和概率 fDM 使用治疗前后获取的 ADC 图进行计算。通过对治疗前和治疗后 ADC 图应用随机、有限平移和旋转扰动来计算概率 fDM，然后重复计算反映治疗后变化的 fDM，从而得到显示 fDM 分类的体素概率的概率 fDM。然后将概率 fDM 与传统 fDM 预测无进展生存期 (PFS) 和总生存期 (OS) 的能力进行比较。结果。将概率 fDM 应用于新诊断的接受放化疗治疗的胶质母细胞瘤患者，结果表明，肿瘤体积较大且治疗后相对于基线扫描评估的 ADC 降低的患者中，PFS 和 OS 缩短。或者，在治疗后根据基线扫描进行评估且 ADC 增加的肿瘤体积较大的患者更有可能进展较晚且寿命更长。通过使用接受者-操作者特征分析，概率 fDM 在预测 12 个月 PFS 和 24 个月 OS 方面比传统 fDM 表现更好。 Kaplan-Meier 数据的单变量对数秩分析还表明，与传统 fDM 相比，概率 fDM 可以根据 PFS 和 OS 更好地区分患者。结论。结果表明，与传统的 fDM 分析相比，概率性 fDM 是新诊断胶质母细胞瘤中 12 个月 PFS 和 24 个月 OS 更具预测性的生物标志物。
Background. Functional diffusion mapping (fDM) is a cancer imaging technique that uses voxel-wise changes in apparent diffusion coefficients (ADC) to evaluate response to treatment. Despite promising initial results, uncertainty in image registration remains the largest barrier to widespread clinical application. The current study introduces a probabilistic approach to fDM quantification to overcome some of these limitations.Methods. A total of 143 patients with newly diagnosed glioblastoma who were undergoing standard radiochemotherapy were enrolled in this retrospective study. Traditional and probabilistic fDMs were calculated using ADC maps acquired before and after therapy. Probabilistic fDMs were calculated by applying random, finite translational, and rotational perturbations to both pre-and posttherapy ADC maps, then repeating calculation of fDMs reflecting changes after treatment, resulting in probabilistic fDMs showing the voxel-wise probability of fDM classification. Probabilistic fDMs were then compared with traditional fDMs in their ability to predict progression-free survival (PFS) and overall survival (OS).Results. Probabilistic fDMs applied to patients with newly diagnosed glioblastoma treated with radiochemotherapy demonstrated shortened PFS and OS among patients with a large volume of tumor with decreasing ADC evaluated at the posttreatment time with respect to the baseline scans. Alternatively, patients with a large volume of tumor with increasing ADC evaluated at the posttreatment time with respect to baseline scans were more likely to progress later and live longer. Probabilistic fDMs performed better than traditional fDMs at predicting 12-month PFS and 24-month OS with use of receiver-operator characteristic analysis. Univariate log-rank analysis on Kaplan-Meier data also revealed that probabilistic fDMs could better separate patients on the basis of PFS and OS, compared with traditional fDMs.Conclusions. Results suggest that probabilistic fDMs are a more predictive biomarker in terms of 12-month PFS and 24-month OS in newly diagnosed glioblastoma, compared with traditional fDM analysis.