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Decoding global gene expression programs in cancer by non-invasive imaging


Eran Segal, Claude B. Sirlin, Clara Ooi, Adam S Adler, Jeremy Gollub, Xin Chen, Bryan K. Chan, George R. Matcuk, Christopher T. Barry, Howard Y. Chang, Michael D. Kuo


Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) efficiently encode the global gene expression programs of primary human liver cancer. Combinations of twenty eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by non-invasive imaging, thereby enabling non-invasive, serial, and frequent molecular profiling for personalized medicine.