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Diffuse gliomas are highly infiltrative tumors whose early diagnosis andfollow-up usually rely on magnetic resonance imaging (MRI). However, thelimited sensitivity of this technique makes it impossible to directly assessthe extent of the glioma cell invasion, leading to sub-optimal treatmentplaning. Reaction-diffusion growth models have been proposed for decades toextrapolate glioma cell infiltration beyond margins visible on MRI and predictits spatial-temporal evolution. These models nevertheless require an initialcondition, that is the tumor cell density values at every location of the brainat diagnosis time. Several works have proposed to relate the tumor cell densityfunction to abnormality outlines visible on MRI but the underlying assumptionshave never been verified so far. In this work we propose to verify theseassumptions by stereotactic histological analysis of a non-operated brain withglioblastoma using a tailored 3D-printed slicer. Cell density maps are computedfrom histological slides using a deep learning approach. The density maps arethen registered to a postmortem MR image and related to an MR-derived geodesicdistance map to the tumor core. The relation between the edema outlines visibleon T2 FLAIR MRI and the distance to the core is also investigated. Our resultssuggest that (i) the previously suggested exponential decrease of the tumorcell density with the distance to the tumor core is not unreasonable but (ii)the edema outlines may in general not correspond to a cell density iso-contourand (iii) the commonly adopted tumor cell density value at these outlines islikely overestimated. These findings highlight the limitations of usingconventional MRI to derive glioma cell density maps and point out the need ofvalidating other methods to initialize reaction-diffusion growth models andmake them usable in clinical practice.

Initial condition assessment for reaction-diffusion glioma growth models: A translational MRI/histology (in)validation study