  Tumors are extremely heterogeneous and comprise of a number of intratumor
microenvironments or sub-regions. These tumor microenvironments may interact
with eac based on complex high-level relationships, which could provide
important insight into the organizational structure of the tumor network. To
that end, we developed a tumor connectomics framework (TCF) to understand and
model the complex functional and morphological interactions within the tumor.
Then, we demonstrate the TCF's potential in predicting treatment response in
breast cancer patients being treated with neoadjuvant chemotherapy. The TCF was
implemented on a breast cancer patient cohort of thirty-four patients with
dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) undergoing
neodjuvant chemotherapy treatment. The intra-tumor network connections (tumor
connectome) before and after treatment were modeled using advanced graph
theoretic centrality, path length and clustering metrics from the DCE-MRI. The
percentage change of the graph metrics between two time-points (Baseline and
1st cycle) was computed to predict the patient's final response to treatment.
The TCF visualized the inter-voxel network connections across multiple
time-points and was able to evaluate specific changes in the tumor connectome
with treatment. Degree centrality was identified as the most significant
predictor of treatment response with an AUC of 0.83 for classifying responders
from non-responders. In conclusion, the TCF graph metrics produced excellent
biomarkers for prediction of breast cancer treatment response with improved
visualization and interpretability of changes both locally and globally in the
tumor.
