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Cancer Res. 2015 Nov 15;75(22):4697-707. doi: 10.1158/0008-5472.CAN-14-2945. Epub 2015 Sep 2.

Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.

Author information

1
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. jared.a.weis@Vanderbilt.Edu thomas.yankeelov@vanderbilt.edu.
2
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. Department of Neurosurgery, Vanderbilt University, Nashville, Tennessee. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee.
3
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee.
4
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee. Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, Tennessee.
5
Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee. Department of Radiation Oncology, Vanderbilt University, Nashville, Tennessee.
6
Department of Radiation Oncology, Vanderbilt University, Nashville, Tennessee.
7
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee. Department of Physics, Vanderbilt University, Nashville, Tennessee. Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee. jared.a.weis@Vanderbilt.Edu thomas.yankeelov@vanderbilt.edu.

Abstract

Although there are considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically relevant oncologic models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathologic response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction-diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.

PMID:
26333809
PMCID:
PMC4651826
DOI:
10.1158/0008-5472.CAN-14-2945
[Indexed for MEDLINE]
Free PMC Article

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