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Feng Y, Clayton EH, Okamoto RJ, Engelbach J, Bayly PV, Garbow JR. A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy. Phys Med Biol 2016; 61:6121-31. [PMID: 27461395 DOI: 10.1088/0031-9155/61/16/6121] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.
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Affiliation(s)
- Y Feng
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, People's Republic of China. Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, People's Republic of China. School of Computer Science and Engineering, Soochow University, Suzhou, Jiangsu, People's Republic of China
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Feng Y, Guo H, Zhang H, Li C, Sun L, Mutic S, Ji S, Hu Y. A modified fuzzy C-means method for segmenting MR images using non-local information. Technol Health Care 2016; 24 Suppl 2:S785-93. [DOI: 10.3233/thc-161208] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yuan Feng
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China
- Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China
| | - Hao Guo
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China
- Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China
| | - Hongmiao Zhang
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China
- Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China
| | - Chungang Li
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China
- Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China
| | - Lining Sun
- School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China
- Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University, St. Louis, MO, USA
| | - Songbai Ji
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
| | - Yanle Hu
- Department of Radiation Oncology, Washington University, St. Louis, MO, USA
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA
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