Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data

Overview

Journal JCO Clin Cancer Inform

Specialty Medical Informatics

Date 2019 Feb 27

PMID 30807209

Citations 22

Authors

David A Hormuth 2nd

Angela M Jarrett

Ernesto A B F Lima

Matthew T McKenna

David T Fuentes

Thomas E Yankeelov

Affiliations

Soon will be listed here.

Abstract

Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [F]fluorodeoxyglucose positron emission tomography, and [F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.

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References

Lima E, Oden J, Wohlmuth B, Shahmoradi A, Hormuth 2nd D, Yankeelov T . Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data. Comput Methods Appl Mech Eng. 2017; 327:277-305. PMC: 5734134. DOI: 10.1016/j.cma.2017.08.009. View

Koh D, Collins D . Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol. 2007; 188(6):1622-35. DOI: 10.2214/AJR.06.1403. View

Bondiau P, Clatz O, Sermesant M, Marcy P, Delingette H, Frenay M . Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging. Phys Med Biol. 2008; 53(4):879-93. DOI: 10.1088/0031-9155/53/4/004. View

Mosayebi P, Cobzas D, Murtha A, Jagersand M . Tumor invasion margin on the Riemannian space of brain fibers. Med Image Anal. 2011; 16(2):361-73. DOI: 10.1016/j.media.2011.10.001. View

Chenevert T, Malyarenko D, Newitt D, Li X, Jayatilake M, Tudorica A . Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling. Transl Oncol. 2014; 7(1):65-71. PMC: 3998685. DOI: 10.1593/tlo.13811. View

Virostko J, Hainline A, Kang H, Arlinghaus L, Abramson R, Barnes S . Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis. J Med Imaging (Bellingham). 2017; 5(1):011011. PMC: 5701084. DOI: 10.1117/1.JMI.5.1.011011. View

Mi H, Petitjean C, DuBray B, Vera P, Ruan S . Prediction of lung tumor evolution during radiotherapy in individual patients with PET. IEEE Trans Med Imaging. 2014; 33(4):995-1003. DOI: 10.1109/TMI.2014.2301892. View

Korenblum D, Rubin D, Napel S, Rodriguez C, Beaulieu C . Managing biomedical image metadata for search and retrieval of similar images. J Digit Imaging. 2010; 24(4):739-48. PMC: 3138941. DOI: 10.1007/s10278-010-9328-z. View

Eisenhauer E, Therasse P, Bogaerts J, Schwartz L, Sargent D, Ford R . New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2008; 45(2):228-47. DOI: 10.1016/j.ejca.2008.10.026. View

10.

Chen X, Summers R, Yao J . FEM-based 3-D tumor growth prediction for kidney tumor. IEEE Trans Biomed Eng. 2011; 58(3):463-7. PMC: 3421459. DOI: 10.1109/TBME.2010.2089522. View

11.

Rockne R, Trister A, Jacobs J, Hawkins-Daarud A, Neal M, Hendrickson K . A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET. J R Soc Interface. 2014; 12(103). PMC: 4305419. DOI: 10.1098/rsif.2014.1174. View

12.

Sorace A, Wu C, Barnes S, Jarrett A, Avery S, Patt D . Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging. 2018; . PMC: 6151298. DOI: 10.1002/jmri.26011. View

13.

Galban C, Chenevert T, Meyer C, Tsien C, Lawrence T, Hamstra D . Prospective analysis of parametric response map-derived MRI biomarkers: identification of early and distinct glioma response patterns not predicted by standard radiographic assessment. Clin Cancer Res. 2011; 17(14):4751-60. PMC: 3139775. DOI: 10.1158/1078-0432.CCR-10-2098. View

14.

Hormuth 2nd D, Weis J, Barnes S, Miga M, Rericha E, Quaranta V . A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict glioma growth. J R Soc Interface. 2017; 14(128). PMC: 5378136. DOI: 10.1098/rsif.2016.1010. View

15.

Weis J, Miga M, Arlinghaus L, Li X, Chakravarthy A, Abramson V . A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Phys Med Biol. 2013; 58(17):5851-66. PMC: 3791925. DOI: 10.1088/0031-9155/58/17/5851. View

16.

Hormuth 2nd D, Weis J, Barnes S, Miga M, Quaranta V, Yankeelov T . Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys. 2018; 100(5):1270-1279. PMC: 5934308. DOI: 10.1016/j.ijrobp.2017.12.004. View

17.

Baldock A, Rockne R, Boone A, Neal M, Hawkins-Daarud A, Corwin D . From patient-specific mathematical neuro-oncology to precision medicine. Front Oncol. 2013; 3:62. PMC: 3613895. DOI: 10.3389/fonc.2013.00062. View

18.

Padhani A, Liu G, Koh D, Chenevert T, Thoeny H, Takahara T . Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009; 11(2):102-25. PMC: 2631136. DOI: 10.1593/neo.81328. View

19.

Yan D . Adaptive radiotherapy: merging principle into clinical practice. Semin Radiat Oncol. 2010; 20(2):79-83. DOI: 10.1016/j.semradonc.2009.11.001. View

20.

Weis J, Miga M, Arlinghaus L, Li X, Abramson V, Chakravarthy A . Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res. 2015; 75(22):4697-707. PMC: 4651826. DOI: 10.1158/0008-5472.CAN-14-2945. View