Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 314, Issue -, Pages 494-512Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2016.08.024
Keywords
Tumor; Mechanics; Mathematical; Computational; Oncology; Finite element
Funding
- National Institutes of Health [NCI K25CA204599, NCI R01CA186193, NCI U01CA174706, NCI U01CA142565, NIBIB R21EB022380, CPRIT RR00016]
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The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an in silico performance study, we show accurate parameter estimation with error less than 3% as compared to ground truth. We apply the approach to patient data from a patient with pathological complete response and a patient with residual tumor burden and demonstrate technical feasibility and predictive potential with direct comparisons between imaging data observation and model predictions of tumor cellularity and volume. Comparisons to our previous two-dimensional modeling framework reflect enhanced model prediction of residual tumor burden through the inclusion of additional imaging slices of patient-specific data. (C) 2016 Elsevier B.V. All rights reserved.
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