Hirata Yoshito, Azuma Shun-ichi, Aihara Kazuyuki
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan.
Methods. 2014 Jun 1;67(3):278-81. doi: 10.1016/j.ymeth.2014.03.018. Epub 2014 Mar 27.
Mathematical modeling of prostate cancer under intermittent androgen suppression revealed that we may be able to delay relapse by optimally scheduling the hormone therapy for each patient. However, our previous study showed the difficulty of the scheduling by minimizing the maximal tumor growth rate because the transient dynamics is also important and can help to delay the relapse for a finite time. Here, we propose to use model predictive control for scheduling intermittent androgen suppression. We find that model predictive control tends to delay the relapse of prostate specific antigen more than the method with minimizing the maximal tumor growth rate. Therefore, model predictive control is a promising approach for practically applying the mathematical model to optimally schedule intermittent androgen suppression.
间歇性雄激素抑制下前列腺癌的数学模型显示，我们或许能够通过为每位患者优化激素治疗的时间安排来延迟复发。然而，我们之前的研究表明，通过最小化最大肿瘤生长速率来进行时间安排存在困难，因为瞬态动力学也很重要，并且能够在有限时间内帮助延迟复发。在此，我们提议使用模型预测控制来安排间歇性雄激素抑制。我们发现，与最小化最大肿瘤生长速率的方法相比，模型预测控制更倾向于延迟前列腺特异性抗原的复发。因此，模型预测控制是一种将数学模型实际应用于优化间歇性雄激素抑制时间安排的有前景的方法。
