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• W2518032605 abstract "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." @default.
• W2518032605 created "2016-09-16" @default.
• W2518032605 creator A5027619317 @default.
• W2518032605 creator A5036352350 @default.
• W2518032605 creator A5053709484 @default.
• W2518032605 date "2017-02-01" @default.
• W2518032605 modified "2023-10-14" @default.
• W2518032605 title "Three-dimensional image-based mechanical modeling for predicting the response of breast cancer to neoadjuvant therapy" @default.
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• W2518032605 doi "https://doi.org/10.1016/j.cma.2016.08.024" @default.
• W2518032605 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5193147" @default.
• W2518032605 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28042181" @default.
• W2518032605 hasPublicationYear "2017" @default.
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