Abstract #1112

Correlation assessment between quantitative multimodality imaging metrics using a community detection algorithm

Ramesh Paudyal¹, Milan Grkovski¹, Jung Hun Oh¹, Heiko Schoder², David Aramburd Nunez¹, Vaios Hatzoglou², Joseph O Deasy¹, John L Hum¹, Nancy Lee³, and Amita Shukla-Dave^1,2

¹Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

This study aims to assess the correlation between the pre-treatment quantitative imaging metrics obtained from multimodality imaging (MMI) techniques such as 18[F]-FMISO PET/CT, 18[F]-FDG PET/CT, DW- and DCE- MRI describing tumor metabolism, hypoxia, diffusion, perfusion, and cell metabolic activity, using a community detection algorithm. The method partitioned the network into four groups with strong and weak connections. The community connection results show complementary, rather than competitive, information about tumor metabolism, hypoxia, diffusion, and perfusion.

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