This repository provide demo codes and data of the modeling pipeline for patient-specific prediction of breast cancer response to neoadjuvant therapy. 
- The framework is illustrated in the document, "codestructure.pdf".
- The folder "data and code" provide a demo of the modeling pipeline, including functions and scripts used for the calibration and prediction, and data required as inputs for one example patient.
- The folder "outputs of calibration" provide results of calibration in this example patient, including the calibrated parameters, and associated metrics measuring performance of the calibration (for example, sum of residual between the modeled and measured quantity of interest).


Please refers to this paper for more details on the methodology and associated image processing pipeline:

Angela M. Jarrett, Anum S. Kazerouni, Chengyue Wu, John Virostko, Anna G. Sorace, Julie C. DiCarlo, David A. Hormuth, II, David A. Ekrut, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nature Propotol, 2021 (in press).

Further questions and discussion are welcome through the repository, or via email (ajarrett@utexas.edu, cw35926@utexas.edu).