In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Sci Rep. 2019 Jan 31;9(1):1081. doi: 10.1038/s41598-018-37155-9.
Apart from offering insight into the biomechanisms involved in cancer, many recent mathematical modeling efforts aspire to the ultimate goal of clinical translation, wherein models are designed to be used in the future as clinical decision support systems in the patient-individualized context. Most significant challenges are the integration of multiscale biodata and the patient-specific model parameterization. A central aim of this study was the design of a clinically-relevant parameterization methodology for a patient-specific computational model of cervical cancer response to radiotherapy treatment with concomitant cisplatin, built around a tumour features-based search of the parameter space. Additionally, a methodological framework for the predictive use of the model was designed, including a scoring method to quantitatively reflect the similarity and bilateral predictive ability of any two tumours in terms of their regression profile. The methodology was applied to the datasets of eight patients. Tumour scenarios in accordance with the available longitudinal data have been determined. Predictive investigations identified three patient cases, anyone of which can be used to predict the volumetric evolution throughout therapy of the tumours of the other two with very good results. Our observations show that the presented approach is promising in quantifiably differentiating tumours with distinct regression profiles.
除了深入了解癌症相关的生物力学机制，许多近期的数学建模工作都渴望达到最终的临床转化目标，即将模型设计为未来在个体化患者背景下作为临床决策支持系统使用。最大的挑战是整合多尺度生物数据和患者特异性模型参数化。本研究的一个核心目标是设计一种基于肿瘤特征的参数搜索方法，为宫颈癌患者的个体化计算模型进行放射治疗和顺铂联合治疗的反应提供临床相关的参数化方法。此外，还设计了模型的预测使用方法学框架，包括一种评分方法，用于根据回归曲线来定量反映任意两个肿瘤之间的相似性和双边预测能力。该方法学已应用于 8 名患者的数据集。根据现有的纵向数据确定了肿瘤情况。预测性研究确定了三个患者案例，其中任何一个案例都可以用于预测另外两个患者肿瘤在整个治疗过程中的体积演变，结果非常好。我们的观察结果表明，所提出的方法在定量区分具有不同回归曲线的肿瘤方面具有很大的潜力。
