Toward improved quantification of disease progression and treatment monitoring using dynamic or static steady-state tumor-model parameters
Clinical Oncological imaging is performed with various modalities, CT, MRI and F-18-FDG-PET. Recently, investigators have used diffusion-advective-reaction tumor-growth models for registration to brain-atlas for MRI brain-tumor datasets. We wish to extract model parameters from clinical time series scans of tumors (e.g. CT or MRI of brain or lung tumors) to see if some of the parameters, tumor growth rate and/or diffusion-coefficient, could potentially serve as predictive markers for monitoring disease and treatment response. We can then correlate with disease history and/or PET SUV to assess the viability of the model parameters as markers. One hurdle to performing this is that for majority of patients only 1 or 2 scans would be available for a specific tumor. We first take an existing diffusion-advectionreaction dynamic tumor growth model and generate series of synthetic tumors. Then we try to invert the model and recover the coefficient for one or multiple target scans, minimizing the sum-squared-error using APPSPACK. We find that for this idealized case we could recover the diffusion-coefficient and growth-rate parameters with ∼2%-3% error whether we used the entire time series or a single time point. However, in general, for either case (multiple or single time scan) some additional parameters such the tumor time scan and starting locations maybe necessary in the minimization. In a second (novel) approach, we hypothesize that over a short time scale the tumor density/volume change is small (or undetectable). Thus the cell birth and death and diffusion terms are in near-equilibrium. This steady-state model diffusion-coefficient and growth parameters may then be extracted from even a single CT or MRI scan available for each patient. Our steady state forward simulation and inversion could recover steady-state diffusion and growth-rate parameters with near-perfect (∼0.6% ) error for no-noise case and ∼(0,7%) error for a high-noise case. The steady-state model fitted excellently to a lung-tumor (1-d) profile of an (anonymized) patient with only 1.74% fitting error in a sum-squared sense. © 2013 SPIE.
Publication Source (Journal or Book title)
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Dey, J., & Licho, R. (2013). Toward improved quantification of disease progression and treatment monitoring using dynamic or static steady-state tumor-model parameters. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 8676 https://doi.org/10.1117/12.2006470