Wang Christina H, Rockhill Jason K, Mrugala Maciej, Peacock Danielle L, Lai Albert, Jusenius Katy, Wardlaw Joanna M, Cloughesy Timothy, Spence Alexander M, Rockne Russ, Alvord Ellsworth C, Swanson Kristin R
Department of Pathology, University of Washington, Seattle, Washington 98195, USA.
Cancer Res. 2009 Dec 1;69(23):9133-40. doi: 10.1158/0008-5472.CAN-08-3863. Epub 2009 Nov 24.
Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness.
胶质母细胞瘤是最具侵袭性的原发性脑肿瘤，其特征在于快速增殖和脑组织的弥漫性浸润。胶质母细胞瘤患者的生存模式与许多临床病理因素有关，包括年龄和神经状态，但与每个胶质瘤的体内生长动力学的显著定量联系仍然难以捉摸。利用一种最近开发的工具，通过常规可用的磁共振成像（MRI）来量化个体患者的胶质瘤净增殖和侵袭率，我们建议将这些患者特异性的生物学侵袭动力学率与预后意义联系起来。使用我们基于生物学的胶质瘤生长和侵袭数学模型，对32例胶质母细胞瘤患者的系列治疗前MRI进行检查，从而能够量化每个患者肿瘤的这些速率。生存分析表明，即使在控制标准临床参数（如年龄和卡诺夫斯基表现状态）时，这些定义模型的量化生物学侵袭性的参数（净增殖和侵袭率）也与预后显著相关。由此产生的一个假设是，无论采用何种治疗方法，实际生存时间与（模型）预测的无任何治疗的生存持续时间之比将提供治疗总体有效性的治疗反应指数（TRI）。TRI可能提供关于个体患者治疗有效性的重要信息，而这些信息是无法通过其他方式获得的。据我们所知，这是第一份报告表明，从常规获得的治疗前成像中获得的动态见解可能在定量表征胶质母细胞瘤个体患者的生存方面有用。这种将数学建模与临床成像相结合的混合工具可能允许根据患者治疗前的生物学侵袭性对其进行分层，以用于临床研究。
