Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
J Mol Cell Biol. 2012 Jun;4(3):127-32. doi: 10.1093/jmcb/mjs020. Epub 2012 May 4.
If a mathematical model is to be used in the diagnosis, treatment, or prognosis of a disease, it must describe the inherent quantitative dynamics of the state. An ideal candidate disease is prostate cancer owing to the fact that it is characterized by an excellent biomarker, prostate-specific antigen (PSA), and also by a predictable response to treatment in the form of androgen suppression therapy. Despite a high initial response rate, the cancer will often relapse to a state of androgen independence which no longer responds to manipulations of the hormonal environment. In this paper, we present relevant background information and a quantitative mathematical model that potentially can be used in the optimal management of patients to cope with biochemical relapse as indicated by a rising PSA.
如果一个数学模型要用于疾病的诊断、治疗或预后，它必须描述状态的内在定量动态。前列腺癌是一个理想的候选疾病，因为它具有一个极好的生物标志物，即前列腺特异性抗原（PSA），并且也具有以雄激素抑制治疗形式出现的可预测的治疗反应。尽管初始反应率很高，但癌症通常会复发到雄激素不依赖的状态，不再对激素环境的操作产生反应。在本文中，我们介绍了相关的背景信息和一个定量数学模型，该模型可能用于患者的最佳管理，以应对 PSA 升高所表明的生化复发。
