Department of Radiation Oncology, Mid North Coast Cancer Institute, Coffs Harbour, NSW 2450, Australia; RCS Faculty of Medicine, University of New South Wales, New South Wales, Australia.
Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA; Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
Oral Oncol. 2020 Apr;103:104473. doi: 10.1016/j.oraloncology.2019.104473. Epub 2020 Feb 25.
Cancers of the skin (the majority of which are basal and squamous cell skin carcinomas, but also include the rarer Merkel cell carcinoma) are overwhelmingly the most common of all types of cancer. Most of these are treated surgically, with radiation reserved for those patients with high risk features or anatomical locations less suitable for surgery. Given the high incidence of both basal and squamous cell carcinomas, as well as the relatively poor outcome for Merkel cell carcinoma, it is useful to investigate the role of other disciplines regarding their diagnosis, staging and treatment. Mathematical modelling is one such area of investigation. The use of mathematical modelling is a relatively recent addition to the armamentarium of cancer treatment. It has long been recognised that tumour growth and treatment response is a complex, non-linear biological phenomenon with many mechanisms yet to be understood. Despite decades of research, including clinical, population and basic science approaches, we continue to be challenged by the complexity, heterogeneity and adaptability of tumours, both in individual patients in the oncology clinic and across wider patient populations. Prospective clinical trials predominantly focus on average outcome, with little understanding as to why individual patients may or may not respond. The use of mathematical models may lead to a greater understanding of tumour initiation, growth dynamics and treatment response.
皮肤癌（其中大多数是基底细胞癌和鳞状细胞癌，但也包括更罕见的 Merkel 细胞癌）是所有癌症中最常见的类型。这些癌症大多数通过手术治疗，对于具有高危特征或手术不太适合的解剖部位的患者，则保留放射治疗。鉴于基底细胞癌和鳞状细胞癌的发病率较高，以及 Merkel 细胞癌的预后相对较差，因此研究其他学科在其诊断、分期和治疗中的作用是很有帮助的。数学建模就是这样一个研究领域。数学建模的使用是癌症治疗武器库中的一个相对较新的补充。长期以来，人们一直认识到肿瘤的生长和治疗反应是一种复杂的、非线性的生物学现象，其中许多机制尚未得到理解。尽管经过几十年的研究，包括临床、人群和基础科学方法，我们仍然面临着肿瘤的复杂性、异质性和适应性的挑战，无论是在肿瘤诊所的个体患者中，还是在更广泛的患者群体中。前瞻性临床试验主要关注平均结果，而对于为什么个别患者可能会或可能不会有反应，了解甚少。使用数学模型可能会更好地了解肿瘤的发生、生长动态和治疗反应。
