Kirkby Norman F, Jefferies Sarah J, Jena Raj, Burnet Neil G
Fluids & Systems Research Centre, School of Engineering (D2), University of Surrey, Guildford, Surrey GU2 7XH, UK.
J Theor Biol. 2007 Mar 7;245(1):112-24. doi: 10.1016/j.jtbi.2006.09.007. Epub 2006 Sep 16.
More years of life per patient are lost as the result of primary brain tumours than any other form of cancer. The most aggressive of these is known as glioblastoma (GBM). The median survival time of patients with GBM is under 10 months and the outlook has hardly improved over the past 20 years. Generally, these tumours are remarkably resistant to radiotherapy and yet about 2-3% of all GBMs appear to be cured. The objectives of this study were to formulate a mathematical and phenomenological model of tumour growth in a population of patients with GBM to predict survival, and to use the model to extract biological information from clinical data. The model describes the growth of the tumour and the resulting damage to the normal brain using simple concepts borrowed from chemical reaction engineering. Death is assumed to result when the amount of surviving normal brain falls to a critical level. Radiotherapy is assumed to destroy tumour but not healthy brain. Simple rules are included to represent approximately the clinician's decisions about what type of treatment to offer each patient. A population of patients is constructed by assuming that key parameters can be sampled from statistical distributions. Following Monte Carlo simulation, the model can be fitted to data from clinical trials. The model reproduces clinical data extremely accurately. This suggests that the long-term survivors are not a separate sub-population but are the 'lucky tail' of a unimodal distribution. The estimated values of radiation sensitivity (represented as SF2, the survival fraction after 2Gy) suggest the presence of severe hypoxia, which renders cells less sensitive to radiation. The model can predict the probable age distribution of tumours at presentation. The model shows the complicated effects of waiting times for treatment on the survival outcomes, and is used to predict the effects of escalation of radiotherapy dose. The model may aid the design of clinical trials using radiotherapy for patients with GBM, especially in helping to estimate the size of trial required. It is also designed in a generic form, and might be applicable to other tumour types.
与任何其他癌症形式相比,原发性脑肿瘤导致每位患者失去的生命年数更多。其中最具侵袭性的是胶质母细胞瘤(GBM)。GBM患者的中位生存时间不到10个月,并且在过去20年中预后几乎没有改善。一般来说,这些肿瘤对放疗具有显著抗性,但所有GBM中约有2 - 3%似乎得到了治愈。本研究的目的是构建一个数学和现象学模型,用于预测GBM患者群体中的肿瘤生长情况及生存情况,并利用该模型从临床数据中提取生物学信息。该模型使用从化学反应工程中借鉴的简单概念来描述肿瘤的生长以及对正常大脑造成的损害。假设当存活的正常大脑量降至临界水平时会导致死亡。假设放疗会破坏肿瘤但不会损伤健康大脑。包含了简单规则以大致体现临床医生针对每位患者选择何种治疗方式的决策。通过假设关键参数可从统计分布中抽样来构建患者群体。经过蒙特卡罗模拟后,该模型可以拟合临床试验数据。该模型极其准确地再现了临床数据。这表明长期存活者并非一个单独的亚群体,而是单峰分布的“幸运尾端”。辐射敏感性的估计值(表示为SF2,即2Gy后的存活分数)表明存在严重缺氧,这使得细胞对辐射不太敏感。该模型可以预测肿瘤初发时可能的年龄分布。该模型展示了治疗等待时间对生存结果的复杂影响,并用于预测放疗剂量增加的效果。该模型可能有助于设计针对GBM患者的放疗临床试验,特别是在帮助估计所需试验规模方面。它也是以通用形式设计的,可能适用于其他肿瘤类型。