Caraguel Flavien, Lesart Anne-Cécile, Estève François, van der Sanden Boudewijn, Stéphanou Angélique
Clinatec, INSERM UA01, 38054 Grenoble, France.
Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France.
Comput Math Methods Med. 2016;2016:7851789. doi: 10.1155/2016/7851789. Epub 2016 Dec 19.
The design of a patient-specific virtual tumour is an important step towards Personalized Medicine. However this requires to capture the description of many key events of tumour development, including angiogenesis, matrix remodelling, hypoxia, and cell state heterogeneity that will all influence the tumour growth kinetics and degree of tumour invasiveness. To that end, an integrated hybrid and multiscale approach has been developed based on data acquired on a preclinical mouse model as a proof of concept. Fluorescence imaging is exploited to build case-specific virtual tumours. Numerical simulations show that the virtual tumour matches the characteristics and spatiotemporal evolution of its real counterpart. We achieved this by combining image analysis and physiological modelling to accurately described the evolution of different tumour cases over a month. The development of such models is essential since a dedicated virtual tumour would be the perfect tool to identify the optimum therapeutic strategies that would make Personalized Medicine truly reachable and achievable.
设计针对特定患者的虚拟肿瘤是迈向个性化医疗的重要一步。然而，这需要捕捉肿瘤发展的许多关键事件的描述，包括血管生成、基质重塑、缺氧和细胞状态异质性，这些都会影响肿瘤生长动力学和肿瘤侵袭程度。为此，基于在临床前小鼠模型上获取的数据，开发了一种综合的混合多尺度方法作为概念验证。利用荧光成像构建针对具体病例的虚拟肿瘤。数值模拟表明，虚拟肿瘤与其真实对应物的特征和时空演变相匹配。我们通过结合图像分析和生理建模来准确描述不同肿瘤病例在一个月内的演变，从而实现了这一点。开发这样的模型至关重要，因为专用的虚拟肿瘤将是确定最佳治疗策略的理想工具，这将使个性化医疗真正得以实现。
