Bogdańska M U, Bodnar M, Belmonte-Beitia J, Murek M, Schucht P, Beck J, Pérez-García V M
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, ul. Banacha 2, 02-097 Warsaw, Poland; Departamento de Matemáticas, Universidad de Castilla-La Mancha, ETSI Industriales, Avda. Camilo José Cela 3, 13071 Ciudad Real, Spain.
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, ul. Banacha 2, 02-097 Warsaw, Poland.
Math Biosci. 2017 Jun;288:1-13. doi: 10.1016/j.mbs.2017.02.003. Epub 2017 Feb 16.
Low grade gliomas (LGGs) are infiltrative and incurable primary brain tumours with typically slow evolution. These tumours usually occur in young and otherwise healthy patients, bringing controversies in treatment planning since aggressive treatment may lead to undesirable side effects. Thus, for management decisions it would be valuable to obtain early estimates of LGG growth potential. Here we propose a simple mathematical model of LGG growth and its response to chemotherapy which allows the growth of LGGs to be described in real patients. The model predicts, and our clinical data confirms, that the speed of response to chemotherapy is related to tumour aggressiveness. Moreover, we provide a formula for the time to radiological progression, which can be possibly used as a measure of tumour aggressiveness. Finally, we suggest that the response to a few chemotherapy cycles upon diagnosis might be used to predict tumour growth and to guide therapeutical actions on the basis of the findings.
低级别胶质瘤(LGGs)是浸润性且无法治愈的原发性脑肿瘤,其发展通常较为缓慢。这些肿瘤通常发生在年轻且其他方面健康的患者身上,这在治疗方案制定方面引发了争议,因为积极治疗可能会导致不良副作用。因此,对于管理决策而言,早期评估LGG的生长潜力将非常有价值。在此,我们提出了一个LGG生长及其对化疗反应的简单数学模型,该模型能够描述真实患者中LGG的生长情况。该模型预测,并且我们的临床数据证实,对化疗的反应速度与肿瘤侵袭性相关。此外,我们提供了一个放射学进展时间的公式,该公式有可能被用作肿瘤侵袭性的衡量指标。最后,我们建议,诊断时对几个化疗周期的反应可用于预测肿瘤生长,并根据这些结果指导治疗行动。