Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data

Arnaout, 2018, Neoadjuvant therapy for breast cancer:updates and proceedings from the seventh annual meeting of the canadian consortium for locally advanced breast cancer, Curr Oncol, 25, e490, 10.3747/co.25.4153 Thompson, 2012, Neoadjuvant treatment of breast cancer, Ann Oncol, 23, x231, 10.1093/annonc/mds324 Pinkel, 1958, The use of body surface area as a criterion of drug dosage in cancer chemotherapy, Cancer Res, 18, 853 Dunnwald, 2007, Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients, Breast Cancer Res, 9, R6, 10.1186/bcr1639 Yang, 2018, Clinical significance and prognostic value of receptor conversion in hormone receptor positive breast cancers after neoadjuvant chemotherapy, World J Surg Oncol, 16, 51, 10.1186/s12957-018-1332-7 Olopade, 2008, Advances in breast cancer: pathways to personalized medicine, Clin Cancer Res, 14, 7988, 10.1158/1078-0432.CCR-08-1211 Ludwig, 2005, Biomarkers in cancer staging, prognosis and treatment selection, Nat Rev Cancer, 5, 845, 10.1038/nrc1739 Chen, 2013, Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model, IEEE Trans Biomed Eng, 60, 169, 10.1109/TBME.2012.2222027 Hormuth, 2019, Mechanism-based modeling of tumor growth and treatment response constrained by multiparametric imaging data, Jco Clin Cancer Inform, 10 Swan, 2017, A patient-specific anisotropic diffusion model for brain tumour spread, Bull Math Biol, 80, 1259, 10.1007/s11538-017-0271-8 Roque, 2018, A DCE-MRI driven 3-D reaction-diffusion model of solid tumor growth, IEEE Trans Med Imaging, 37, 724, 10.1109/TMI.2017.2779811 Le, 2016, MRI based bayesian personalization of a tumor growth model, IEEE Trans Med Imaging, 35, 2329, 10.1109/TMI.2016.2561098 Yankeelov, 2013, Clinically relevant modeling of tumor growth and treatment response, Sci Transl Med, 5, 10.1126/scitranslmed.3005686 Rockne, 2010, Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach, Phys Med Biol, 55, 3271, 10.1088/0031-9155/55/12/001 Clatz, 2005, Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation, IEEE Trans Med Imaging, 24, 1334, 10.1109/TMI.2005.857217 Baldock, 2013, From patient-specific mathematical neuro-oncology to precision medicine, Front Oncol, 3, 62, 10.3389/fonc.2013.00062 Mi, 2014, Prediction of lung tumor evolution during radiotherapy in individual patients with PET, IEEE Trans Med Imaging, 33, 995, 10.1109/TMI.2014.2301892 Mi, 2015, Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images, Med Image Anal, 23, 84, 10.1016/j.media.2015.04.016 Liu, 2014, Patient specific tumor growth prediction using multimodal images, Med Image Anal, 18, 555, 10.1016/j.media.2014.02.005 Wong, 2015, Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusion, Med Image Anal, 25, 72, 10.1016/j.media.2015.04.002 Weis, 2013, A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy, Phys Med Biol, 58, 5851, 10.1088/0031-9155/58/17/5851 Weis, 2015, Predicting the response of breast cancer to neoadjuvant therapy using a mechanically coupled reaction-diffusion model, Cancer Res, 75, 4697, 10.1158/0008-5472.CAN-14-2945 Weis, 2017, Three-dimensional image-based mechanical modeling for predicting the response of breast cancer to neoadjuvant therapy, Comput Methods Appl Mech Eng, 314, 494, 10.1016/j.cma.2016.08.024 Atuegwu, 2013, Parameterizing the logistic model of tumor growth by DW-MRI and DCE-MRI data to predict treatment response and changes in breast cancer cellularity during neoadjuvant chemotherapy, Transl Oncol, 6, 256, 10.1593/tlo.13130 Jarrett, 2018, 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, Phys Med Biol, 63, 10.1088/1361-6560/aac040 Copur, 2016, Impact of the national cancer institute community cancer centers program on clinical trial and related activities at a community cancer center in rural nebraska, J Oncol Pract, 12, 67, 10.1200/JOP.2015.005736 Sorace, 2018, Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting, J Magn Reson Imaging, 10.1002/jmri.26011 Virostko, 2019, Magnetization transfer MRI of breast cancer in the community setting: reproducibility and preliminary results in neoadjuvant therapy, Tomography, 5, 44, 10.18383/j.tom.2018.00019 Whisenant, 2014, Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer, Magn Reson Imaging, 32, 245, 10.1016/j.mri.2013.10.013 Yankeelov, 2007, Dynamic contrast enhanced magnetic resonance imaging in oncology: Theory, data acquisition, analysis, and examples, Current Medical Imaging Reviews, 3, 91, 10.2174/157340507780619179 Atuegwu, 2011, Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy, Magn Reson Med, 66, 1689, 10.1002/mrm.23203 Atuegwu, 2012, Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth, Phys Med Biol, 57, 225, 10.1088/0031-9155/57/1/225 Eisenhauer, 2009, New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1), Eur J Cancer, 45, 228, 10.1016/j.ejca.2008.10.026 Hormuth, 2015, Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data, Phys Biol, 12, 10.1088/1478-3975/12/4/046006 Hormuth, 2017, A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth, J R Soc Interface, 14, 10.1098/rsif.2016.1010 Hormuth, 2018, Mechanically coupled reaction-diffusion model to predict glioma growth: methodological details, Methods Mol Biol, 1711, 225, 10.1007/978-1-4939-7493-1_11 Hormuth, 2018, Biophysical modeling of in vivo glioma response following whole brain radiotherapy in a murine model of brain cancer, Int J Radiat Oncol Biol Phys, 10.1016/j.ijrobp.2017.12.004 Hormuth, 2019, Calibrating a predictive model of tumor growth and angiogenesis with quantitative MRI, Ann Biomed Eng, 47, 1539, 10.1007/s10439-019-02262-9 Barpe, 2010, Pharmacokinetic evaluation of doxorubicin plasma levels in normal and overweight patients with breast cancer and simulation of dose adjustment by different indexes of body mass, Eur J Pharm Sci, 41, 458, 10.1016/j.ejps.2010.07.015 van der Vijgh, 1991, Clinical pharmacokinetics of carboplatin, Clin Pharmacokinet, 21, 242, 10.2165/00003088-199121040-00002 Mori, 2006, Retention of paclitaxel in cancer cells for 1 week in vivo and in vitro, Cancer Chemother Pharmacol, 58, 665, 10.1007/s00280-006-0209-6 Tew, 2016, Paclitaxel, Ref Module Biomed Sci, 10.1016/B978-0-12-801238-3.99393-0 Yang, 2015, Pharmacokinetics and safety of cyclophosphamide and docetaxel in a hemodialysis patient with early stage breast cancer: a case report, BMC Cancer, 15, 917, 10.1186/s12885-015-1932-3 Powis, 1987, Effect of body weight on the pharmacokinetics of cyclophosphamide in breast cancer patients, Cancer Chemother Pharmacol, 20, 219, 10.1007/BF00570489 Bruynseels, 2018, Digital twins in health care: ethical implications of an emerging engineering paradigm, Front Genet, 9, 31, 10.3389/fgene.2018.00031 Efron, 1993, An Introduction to the Bootstrap, 57 Coveney, 2016, Big data need big theory too, Philos Trans A Math Phys Eng Sci, 374 Hormuth, 2020, Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling, Radiat Oncol, 15, 4, 10.1186/s13014-019-1446-2 Jarrett, 2020, Abstract P2-16-17: Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results, Cancer Res, 80