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Recent advances in cancer imaging

Banff International Research Station for Mathematical Innovation and Discovery

In many cancer imaging settings, radiologists identify the presence of solid tumors through informal assessment of the extent to which candidate regions of interest absorb and maintain contrast over a series of a few repeated scans. Often multiple interdependent ROIs are evaluated in isolation, using data from only a few scans. Independent estimation appears limiting for analysis of sparse functional data derived from dynamic imaging techniques that use physiological models to derive multiple interdependent biomarkers acquired from multiple regions of interests (ROI) within the same organ. We consider statistical methods for joint estimation of sparse spatiotemporally correlated imaging-biomarkers using semi-parametric models. Joint prediction is used to identify liver metastases using perfusion characteristics from multiple ROIs acquired using dynamic computed tomography.

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: University of Texas MD Anderson Cancer Center

2016-08-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58685

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/