Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models

Authors

Sean P. Devan, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, United States.
Xiaoyu Jiang, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
Hakmook Kang, Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.
Guozhen Luo, Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, United States.
Jingping Xie, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States.
Zhongliang Zu, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
Ashley M. Stokes, Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States.
John C. Gore, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States.
Colin D. McKnight, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
Austin N. Kirschner, Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, United States.
Junzhong Xu, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States. Electronic address: junzhong.xu@vanderbilt.edu.

Document Type

Article

Abstract

BACKGROUND: It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting. METHODS: Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow). RESULTS: For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions. CONCLUSION: A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.

Keywords

Brain tumor, MRI, Multi-parametric, Radiation necrosis, mpMRI

Medical Subject Headings

Animals; Multiparametric Magnetic Resonance Imaging; Contrast Media; Rodentia; Brain Neoplasms (diagnostic imaging, radiotherapy); Magnetic Resonance Imaging (methods); Radiation Injuries; Necrosis (diagnostic imaging)

Publication Date

12-1-2022

Publication Title

Magnetic resonance imaging

E-ISSN

1873-5894

Volume

94

First Page

144

Last Page

150

PubMed ID

36209946

Digital Object Identifier (DOI)

10.1016/j.mri.2022.10.002

Recommended Citation

Devan, Sean P.; Jiang, Xiaoyu; Kang, Hakmook; Luo, Guozhen; Xie, Jingping; Zu, Zhongliang; Stokes, Ashley M.; Gore, John C.; McKnight, Colin D.; Kirschner, Austin N.; and Xu, Junzhong, "Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models" (2022). Translational Neuroscience. 2295.
https://scholar.barrowneuro.org/neurobiology/2295