MR Fingerprinting of Adult Brain Tumors: Initial Experience

Fellows’ Journal Club

MR fingerprinting is a technique in which pseudorandomized acquisition parameters are used to simultaneously quantify multiple tissue properties, including T1 and T2 relaxation times. The authors evaluated the ability of MR fingerprinting–derived T1 and T2 relaxometry to differentiate the 3 common types of intra-axial brain tumors (17 glioblastomas, 6 lower grade gliomas, and 8 metastases). Using these parameters, they explored the T1 and T2 properties of peritumoral white matter in various tumor types. Mean T2 values could differentiate solid tumor regions of lowergrade gliomas from metastases and the mean T1 of peritumoral white matter surrounding lowergrade gliomas differed from peritumoral white matter around glioblastomas.

Abstract

Figure 1 from paper
A study patient, a 45-year-old man presenting with severe headaches and altered sensorium with glioblastoma. A and B, FLAIR and contrast-enhanced T1-weighted images from the clinical scan, which demonstrate a left periatrial enhancing lesion with peritumoral FLAIR hyperintensity. C, Postcontrast T1-weighted image with ROI overlay. The central gray ROI shows a solid enhancing tumor region, the white ROI shows a peritumoral white matter region, and the blank ROI in the contralateral hemisphere denotes the contralateral white matter measurement. D and E, MRF-derived quantitative T1 and T2 maps.

BACKGROUND AND PURPOSE

MR fingerprinting allows rapid simultaneous quantification of T1 and T2 relaxation times. This study assessed the utility of MR fingerprinting in differentiating common types of adult intra-axial brain tumors.

MATERIALS AND METHODS

MR fingerprinting acquisition was performed in 31 patients with untreated intra-axial brain tumors: 17 glioblastomas, 6 World Health Organization grade II lower grade gliomas, and 8 metastases. T1, T2 of the solid tumor, immediate peritumoral white matter, and contralateral white matter were summarized within each ROI. Statistical comparisons on mean, SD, skewness, and kurtosis were performed by using the univariate Wilcoxon rank sum test across various tumor types. Bonferroni correction was used to correct for multiple-comparison testing. Multivariable logistic regression analysis was performed for discrimination between glioblastomas and metastases, and area under the receiver operator curve was calculated.

RESULTS

Mean T2 values could differentiate solid tumor regions of lower grade gliomas from metastases (mean, 172 ± 53 ms, and 105 ± 27 ms, respectively; P = .004, significant after Bonferroni correction). The mean T1 of peritumoral white matter surrounding lower grade gliomas differed from peritumoral white matter around glioblastomas (mean, 1066 ± 218 ms, and 1578 ± 331 ms, respectively; P = .004, significant after Bonferroni correction). Logistic regression analysis revealed that the mean T2 of solid tumor offered the best separation between glioblastomas and metastases with an area under the curve of 0.86 (95% CI, 0.69–1.00; P < .0001).

CONCLUSIONS

MR fingerprinting allows rapid simultaneous T1 and T2 measurement in brain tumors and surrounding tissues. MR fingerprinting–based relaxometry can identify quantitative differences between solid tumor regions of lower grade gliomas and metastases and between peritumoral regions of glioblastomas and lower grade gliomas.

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MR Fingerprinting of Adult Brain Tumors: Initial Experience
jross
Jeffrey Ross • Mayo Clinic, Phoenix

Dr. Jeffrey S. Ross is a Professor of Radiology at the Mayo Clinic College of Medicine, and practices neuroradiology at the Mayo Clinic in Phoenix, Arizona. His publications include over 100 peer-reviewed articles, nearly 60 non-refereed articles, 33 book chapters, and 10 books. He was an AJNR Senior Editor from 2006-2015, is a member of the editorial board for 3 other journals, and a manuscript reviewer for 10 journals. He became Editor-in-Chief of the AJNR in July 2015. He received the Gold Medal Award from the ASSR in 2013.