Ever　growing　＂omics＂　data　and　continuously　accumulated　biological　knowledge　provide　an　unprecedented　opportunity　to　identify　molecular　biomarkers　and　their　interactions　that　are　responsible　for　cancer　phenotypes　that　can　be　accurately　defined　by　clinical　measurements　such　as　in　vivo　imaging.　Since　signaling　or　regulatory　networks　are　dynamic　and　context-specific,　systematic　efforts　to　characterize　such　structural　alterations　must　effectively　distinguish　significant　network　rewiring　from　random　background　fluctuations.　Here　we　introduced　a　novel　integration　of　network　biology　and　imaging　to　study　cancer　phenotypes　and　responses　to　treatments　at　the　molecular　systems　level.　Specifically,　Differential　Dependence　Network　(DDN)　analysis　was　used　to　detect　statistically　significant　topological　rewiring　in　molecular　networks　between　two　phenotypic　conditions,　and　in　vivo　Magnetic　Resonance　Imaging　(MRI)　was　used　to　more　accurately　define　phenotypic　sample　groups　for　such　differential　analysis.　We　applied　DDN　to　analyze　two　distinct　phenotypic　groups　of　breast　cancer　and　study　how　genomic　instability　affects　the　molecular　network　topologies　in　high-grade　ovarian　cancer.　Further,　FDA-approved　arsenic　trioxide　(ATO)　and　the　ND2-SmoA1　mouse　model　of　Medulloblastoma　(MB)　were　used　to　extend　our　analyses　of　combined　MRI　and　Reverse　Phase　Protein　Microarray　(RPMA)　data　to　assess　tumor　responses　to　ATO　and　to　uncover　the　complexity　of　therapeutic　molecular　biology.