Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.
Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.
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DOI：
10.1088/0031-9155/55/12/001
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发表时间：
2010-06-21
影响因子：
3.5
通讯作者：
Swanson KR
中科院分区：
文献类型：
--
作者：
Rockne R;Rockhill JK;Mrugala M;Spence AM;Kalet I;Hendrickson K;Lai A;Cloughesy T;Alvord EC Jr;Swanson KR
多形性胶质母细胞瘤（GBM）是原发性脑肿瘤中最恶性的一种。尽管进行了积极的治疗，但它们的扩散和侵袭范围很广，预期寿命很短。对治疗的反应通常是根据治疗相似的患者组的生存率来衡量的，但这种统计方法忽略了可能对治疗有反应或可能因治疗而受伤的亚组。这样的统计数据并不能让那些经历过这些治疗的病人感到安心。此外，目前在单个患者中基于成像的治疗反应指标忽略了肿瘤生长动力学的患者特异性差异，即使在相同的组织学诊断中，肿瘤生长动力学在患者之间也存在很大差异，不幸的是，这些指标在预测患者预后方面仅显示出极小的成功。我们考虑9名新诊断的GBM患者接受诊断活检后，标准护理外束放射治疗（XRT）。我们提出并应用了一个患者特异性的、基于生物学的胶质瘤生长数学模型，该模型量化了个体患者体内对XRT的反应。该数学模型使用恶性肿瘤细胞的净增殖和迁移率来表征肿瘤的生长和侵袭，并使用线性二次模型来描述对放射治疗的反应。仅使用常规的治疗前核磁共振成像来告知患者特异性的生物数学模型模拟，我们发现这些患者的辐射反应，通过临床和模型生成的测量来量化，可以在治疗前高精度地预测。具体来说，我们发现净增殖率与辐射响应参数相关（r = 0.89, p = 0.0007），从而产生一种预测关系，可以用留一交叉验证技术进行测试。这种关系预测治疗后肿瘤大小到观察者之间肿瘤体积的不确定性。本研究结果表明，数学模型可以创建一个与特定患者具有相同生长动力学的虚拟计算机肿瘤，不仅可以预测个体患者体内的治疗反应，还可以为评估每个患者对任何给定治疗的反应提供基础。
Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of survival of groups of patients treated similarly but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard of care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically-based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor’s growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross validation technique. This relationship predicts the tumor size post therapy to within inter-observer tumor volume uncertainty. The results of this study suggest that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.
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DOI：
10.1016/0360-3016(95)00162-r
发表时间：
1995-09-30
影响因子：
7
作者：
LEE, SP;LEU, MY;WITHERS, HR
通讯作者：
WITHERS, HR
影响因子：
3.5
作者：
Garcia, L. M.;Leblanc, J.;Raaphorst, G. P.
通讯作者：
Raaphorst, G. P.
影响因子：
1.9
作者：
Rockne, R.;Alvord, E. C., Jr.;Rockhill, J. K.;Swanson, K. R.
通讯作者：
Swanson, K. R.
影响因子：
45.3
作者：
MACDONALD, DR;CASCINO, TL;CAIRNCROSS, JG
通讯作者：
CAIRNCROSS, JG
影响因子：
3.7
作者：
SHAO, J
通讯作者：
SHAO, J