Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning

SIMPLE SUMMARY: Understanding the complex network of high-level relationships within tumors and between surrounding tissue is challenging and not fully understood. Our findings demonstrate that the tumor connectomics framework (TCF) models different networks within the tumors and surrounding tissue that are detectable on imaging. The TCF extracts a set of graph network features for each lesion and provides insight into the different types of interactions of a cancer under investigation. These TCF networks are visualized with the radiological parameters and overlaid onto the structural images for better understanding of the global and regional connections within the lesion and surrounding tissue. This information could be used for improved cancer therapeutic targeting in neoplasms and response within different organ systems. ABSTRACT: The high-level relationships that form complex networks within tumors and between surrounding tissue is challenging and not fully understood. To better understand these tumoral networks, we developed a tumor connectomics framework (TCF) based on graph theory with machine learning to model the complex interactions within and around the tumor microenvironment that are detectable on imaging. The TCF characterization model was tested with independent datasets of breast, brain, and prostate lesions with corresponding validation datasets in breast and brain cancer. The TCF network connections were modeled using graph metrics of centrality, average path length (APL), and clustering from multiparametric MRI with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast lesions had a significantly higher clustering coefficient and degree centrality than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity compared to Grade 4 tumors with increased degree centrality and clustering coefficients. Gleason 7 prostate lesions had increased betweenness centrality and APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 and 0.73 to 0.87, respectively. These TCF findings were similar in the validation breast and brain datasets. In conclusion, we present a new method for tumor characterization and visualization that results in a better understanding of the global and regional connections within the lesion and surrounding tissue.