Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI

Abstract

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.

Retrospective analysis of hemispheric structural network change as a function of location and size of glioma

“…Similar patterns of increased integration within the ipsilesional hemisphere have been reported in cerebral glioma cases, with one study finding increased functional integration of the hippocampus ( Esposito et al , 2012 ). However, from a structural connectivity standpoint, though consistent with previous findings of increased ipsilesional count, these results are inconsistent with the anatomical effects of glioma presence, which include tract displacement ( Schonberg et al , 2006 ; Angeli et al , 2018 ). With respect to the structural integrity of integration and modularity measures, we observed an ipsilesional decline.…”

“…One possible explanation attributes this to the cortical neuroplasticity described earlier, primarily the idea of recruitment of contralesional brain regions to aid its ipsilesional counterparts, a phenomenon noted in functional recovery of stroke patients ( Small et al , 2002 ). Another could be mass effect concentrating surrounding fibres, providing the fibre tracking algorithm to read an increase in connectivity between two lesion proximal nodes ( Schonberg et al , 2006 ; Angeli et al , 2018 ). Finally, the presence of oedema could be affecting fibre tracking, feigning the presence of increased connectivity while also showing an decrease in FA and increase mean diffusivity ( Field and Alexander, 2004 ; Wu et al , 2004 ; Bulakbaşı, 2009 ).…”

Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma

“…In this study, we did not directly measure tract displacement, since we did not use a normal control for comparison. However, previous studies have demonstrated that the degree of mechanical displacement of brain tissue caused by mass effect is reflected by a change in diffusion coefficients [65]. To control for the relative difference in sampled voxels between the two hemispheres and to assess whether tumor mass correlated with any of the measured diffusion coefficients, we compared the lesion volume, as a fraction of total intracranial volume, with the percent difference in diffusion coefficient between the hemispheres (Fig 3).…”

On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data

“…DTI data have been used successfully in the context of glioma evaluation in several regards. For example, they have been used to adapt target volume definitions for GBM treatment planning [24][25][26], for modeling of brain tumor growth [27][28][29][30], to detect early malignant transformation of low-grade glioma [31], to differentiate between GBM and brain metastases [32], to use DTI-derived fiber tracking in surgery planning [33], and to detect the infiltration of the corpus callosum [34]. Our inability to properly connect most primary and secondary tumors does not diminish the value of DTI for these applications.…”