Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI.
Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI.
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DOI：
10.1016/j.nicl.2018.08.032
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Stylianopoulos T
中科院分区：
文献类型：
--
作者：
Angeli S;Emblem KE;Due-Tonnessen P;Stylianopoulos T
以前模拟脑肿瘤进展的研究通常调查癌细胞密度的时间变化或肿瘤块的整体组织水平生长。在这里，我们开发了一个计算模型来连接这两种方法。该模型结合了组织水平上的肿瘤生物力学响应，并通过对癌细胞增殖、向周围组织的浸润和向远处的侵袭进行建模来解释细胞事件。此外，在模拟中采用了来自解剖磁共振成像、扩散张量成像和灌注成像的高分辨率人体数据的采集，以实现逼真的患者特定模型。该模型预测的肿瘤内机械应力范围为20至34 kPa，导致邻近健康组织位移高达4.5 mm。此外，该模型预测了脑内沿着白色纤维束的可能的癌细胞侵袭模式。最后，通过改变肿瘤血管密度及其浸润外环厚度，我们的模型显示了这些参数用于指导癌细胞远端浸润的时间（83-90天）以及浸润部位的数量（0-2个部位）和位置（颞叶和/或顶叶）的潜力。
Previous studies to simulate brain tumor progression, often investigate either temporal changes in cancer cell density or the overall tissue-level growth of the tumor mass. Here, we developed a computational model to bridge these two approaches. The model incorporates the tumor biomechanical response at the tissue level and accounts for cellular events by modeling cancer cell proliferation, infiltration to surrounding tissues, and invasion to distant locations. Moreover, acquisition of high resolution human data from anatomical magnetic resonance imaging, diffusion tensor imaging and perfusion imaging was employed within the simulations towards a realistic and patient specific model. The model predicted the intratumoral mechanical stresses to range from 20 to 34 kPa, which caused an up to 4.5 mm displacement to the adjacent healthy tissue. Furthermore, the model predicted plausible cancer cell invasion patterns within the brain along the white matter fiber tracts. Finally, by varying the tumor vascular density and its invasive outer ring thickness, our model showed the potential of these parameters for guiding the timing (83–90 days) of cancer cell distant invasion as well as the number (0–2 sites) and location (temportal and/or parietal lobe) of the invasion sites.
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影响因子：
3.7
作者：
Cheng G;Tse J;Jain RK;Munn LL
通讯作者：
Munn LL
影响因子：
4.6
作者：
Alfonso, J. C. L.;Koehn-Luque, A.;Hatzikirou, H.
通讯作者：
Hatzikirou, H.
影响因子：
4.6
作者：
Chen, Xinjian;Summers, Ronald;Yao, Jianhua
通讯作者：
Yao, Jianhua
影响因子：
46.9
作者：
Helmlinger, G;Netti, PA;Jain, RK
通讯作者：
Jain, RK
DOI：
10.1146/annurev-bioeng-071813-105259
发表时间：
2014-07-11
影响因子：
9.7
作者：
Jain RK;Martin JD;Stylianopoulos T
通讯作者：
Stylianopoulos T