Christine
M. Leon1, 2, Peder EZ Larson1, Adam B. Kerr3,
Robert Bok1, John M. Pauly3, John Kurhanewicz1,
Daniel B. Vigneron1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States; 2UC Berkeley | UCSF Graduate Group in Bioengineering, University of California, Berkeley and University of California, San Francisco , San Francisco, CA, United States; 3Department of Electrical Engineering, Stanford University, Stanford, CA, United States
We propose a new and robust method for quantification of dynamic data. Using Metabolic Activity Decomposition, we investigated real-time conversion parameters as biomarkers of cancer. ex vivo enzyme experiments validated the technique, allowing for direct observation of real-time conversion, which can only be due to the LDH enzyme. Conventional modeling yields four unknowns but only two equations, an underdetermined system of equations. Using Metabolic Activity Decomposition, twice the amount of information can be obtained from a same acquisition providing a well-conditioned system. Moreover, fitting in vivo data with MAD-STEAM yielded KPyr→Lac values that robustly distinguished tumor versus normal (p-value=0.009)