1. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results
- Author
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Thomas E. Yankeelov, Wei Huang, Angela M. Jarrett, David A. Hormuth, Stephanie L. Barnes, and Xinzeng Feng
- Subjects
Adult ,medicine.medical_specialty ,medicine.medical_treatment ,Contrast Media ,Antineoplastic Agents ,Breast Neoplasms ,Article ,030218 nuclear medicine & medical imaging ,Cohort Studies ,03 medical and health sciences ,Drug Delivery Systems ,0302 clinical medicine ,Breast cancer ,Text mining ,Extended model ,Image Processing, Computer-Assisted ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Chemotherapy ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,Middle Aged ,Models, Theoretical ,medicine.disease ,Neoadjuvant Therapy ,Biomechanical Phenomena ,Diffusion Magnetic Resonance Imaging ,Treatment Outcome ,Concordance correlation coefficient ,030220 oncology & carcinogenesis ,Drug delivery ,Female ,Radiology ,business ,Diffusion MRI - Abstract
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p
- Published
- 2018