  Central nervous system (CNS) tumors come with the vastly heterogeneous
histologic, molecular and radiographic landscape, rendering their precise
characterization challenging. The rapidly growing fields of biophysical
modeling and radiomics have shown promise in better characterizing the
molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis
of CNS tumors, including clinically-acquired multi-parametric magnetic
resonance imaging (mpMRI) and the inverse problem of calibrating biophysical
models to mpMRI data, assists in identifying macroscopic quantifiable tumor
patterns of invasion and proliferation, potentially leading to improved (i)
detection/segmentation of tumor sub-regions, and (ii) computer-aided
diagnostic/prognostic/predictive modeling. This paper presents a summary of (i)
biophysical growth modeling and simulation, (ii) inverse problems for model
calibration, (iii) their integration with imaging workflows, and (iv) their
application on clinically-relevant studies. We anticipate that such
quantitative integrative analysis may even be beneficial in a future revision
of the World Health Organization (WHO) classification for CNS tumors,
ultimately improving patient survival prospects.
