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Tumors are extremely heterogeneous and comprise of a number of intratumormicroenvironments or sub-regions. These tumor microenvironments may interactwith eac based on complex high-level relationships, which could provideimportant insight into the organizational structure of the tumor network. Tothat end, we developed a tumor connectomics framework (TCF) to understand andmodel the complex functional and morphological interactions within the tumor.Then, we demonstrate the TCF's potential in predicting treatment response inbreast cancer patients being treated with neoadjuvant chemotherapy. The TCF wasimplemented on a breast cancer patient cohort of thirty-four patients withdynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) undergoingneodjuvant chemotherapy treatment. The intra-tumor network connections (tumorconnectome) before and after treatment were modeled using advanced graphtheoretic centrality, path length and clustering metrics from the DCE-MRI. Thepercentage change of the graph metrics between two time-points (Baseline and1st cycle) was computed to predict the patient's final response to treatment.The TCF visualized the inter-voxel network connections across multipletime-points and was able to evaluate specific changes in the tumor connectomewith treatment. Degree centrality was identified as the most significantpredictor of treatment response with an AUC of 0.83 for classifying respondersfrom non-responders. In conclusion, the TCF graph metrics produced excellentbiomarkers for prediction of breast cancer treatment response with improvedvisualization and interpretability of changes both locally and globally in thetumor.

Tumor Connectomics: Mapping the intra-tumoral complex interaction network