  The concept of tumor field effect implies that cancer is a systemic disease
with its impact way beyond the visible tumor confines. For instance, in
Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial
pressure due to tumor burden often leads to brain herniation and poor outcomes.
Our work is based on the rationale that highly aggressive tumors tend to grow
uncontrollably, leading to pronounced biomechanical tissue deformations in the
normal parenchyma, which when combined with local morphological differences in
the tumor confines on MRI scans, will comprehensively capture tumor field
effect. Specifically, we present an integrated MRI-based descriptor,
radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor
comprises measurements of the subtle perturbations in tissue deformations
throughout the surrounding normal parenchyma due to mass effect. This involves
non-rigidly aligning the patients MRI scans to a healthy atlas via
diffeomorphic registration. The resulting inverse mapping is used to obtain the
deformation field magnitudes in the normal parenchyma. These measurements are
then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic
Gradient Orientations (COLLAGE), which captures the morphological heterogeneity
within the tumor confines, on MRI scans. R-DepTH, on N = 207 GBM cases
(training set (St) = 128, testing set (Sv) = 79), demonstrated improved
prognosis of overall survival by categorizing patients into low- (prolonged
survival) and high-risk (poor survival) groups (on St, p-value = 0.0000035, and
on Sv, p-value = 0.0024). R-DepTH descriptor may serve as a comprehensive
MRI-based prognostic marker of disease aggressiveness and survival in solid
tumors.
