Ji Ye Lee1, Atle Bjørnerud2,3, Ji Eun Park4, Bo Eun Lee5, Joo Hyun Kim6, Ho Sung Kim7. 1. Department of Radiology, Eulji Medical Center, Seoul, 01830, Republic of Korea. 2. Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. 3. Department of Physics, University of Oslo, Oslo, Norway. 4. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea. jieunp@gmail.com. 5. Department of Radiology, Boramae Medical Center, Seoul Metropolitan Government -Seoul National University, Seoul, Republic of Korea. 6. NordicNeuroLab, LLC, Seoul, Republic of Korea. 7. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
Abstract
OBJECTIVES: To test if adding permeability measurement to perfusion obtained from dynamic susceptibility contrast MRI (DSC-MRI) improves diagnostic performance in the differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma. MATERIALS AND METHODS: DSC-MRI was acquired in 145 patients with pathologically proven glioblastoma (n = 89) or PCNSL (n = 56). The permeability metrics of contrast agent extraction fraction (Ex), apparent permeability (Ka), and leakage-corrected perfusion of normalized cerebral blood volume (nCBVres) and cerebral blood flow (nCBFres) were derived from a tissue residue function. For comparison purposes, the leakage-corrected normalized CBV (nCBV) and relative permeability constant (K2) were also obtained using the established Weisskoff-Boxerman leakage correction method. The area under the receiver operating characteristics curve (AUC) and cross-validation were used to compare the diagnostic performance of the single DSC-MRI parameters with the performance obtained with the addition of permeability metrics. RESULTS: PCNSL demonstrated significantly higher permeability (Ex, p < .001) and lower perfusion (nCBVres, nCBFres, and nCBV, all p < .001) than glioblastoma. The combination of Ex and nCBVres showed the highest performance (AUC, 0.96; 95% confidence interval, 0.92-0.99) for differentiating PCNSL from glioblastoma, which was a significant improvement over the single perfusion (nCBV: AUC, 0.84; nCBVres: AUC, 0.84; nCBFres: AUC, 0.82; all p < .001) or Ex (AUC, 0.80; p < .001) parameters. CONCLUSIONS: Analysis of the combined permeability and perfusion metrics obtained from a single DSC-MRI acquisition improves the diagnostic value for differentiating PCNSL from glioblastoma in comparison with single-parameter nCBV analysis. KEY POINTS: • Permeability measurement can be calculated from DSC-MRI with a tissue residue function-based leakage correction. • Adding Exto CBV aids in the differentiation of PCNSL from glioblastoma. • CBV and Exmeasurements from DSC-MRI were highly reproducible.
OBJECTIVES: To test if adding permeability measurement to perfusion obtained from dynamic susceptibility contrast MRI (DSC-MRI) improves diagnostic performance in the differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma. MATERIALS AND METHODS: DSC-MRI was acquired in 145 patients with pathologically proven glioblastoma (n = 89) or PCNSL (n = 56). The permeability metrics of contrast agent extraction fraction (Ex), apparent permeability (Ka), and leakage-corrected perfusion of normalized cerebral blood volume (nCBVres) and cerebral blood flow (nCBFres) were derived from a tissue residue function. For comparison purposes, the leakage-corrected normalized CBV (nCBV) and relative permeability constant (K2) were also obtained using the established Weisskoff-Boxerman leakage correction method. The area under the receiver operating characteristics curve (AUC) and cross-validation were used to compare the diagnostic performance of the single DSC-MRI parameters with the performance obtained with the addition of permeability metrics. RESULTS:PCNSL demonstrated significantly higher permeability (Ex, p < .001) and lower perfusion (nCBVres, nCBFres, and nCBV, all p < .001) than glioblastoma. The combination of Ex and nCBVres showed the highest performance (AUC, 0.96; 95% confidence interval, 0.92-0.99) for differentiating PCNSL from glioblastoma, which was a significant improvement over the single perfusion (nCBV: AUC, 0.84; nCBVres: AUC, 0.84; nCBFres: AUC, 0.82; all p < .001) or Ex (AUC, 0.80; p < .001) parameters. CONCLUSIONS: Analysis of the combined permeability and perfusion metrics obtained from a single DSC-MRI acquisition improves the diagnostic value for differentiating PCNSL from glioblastoma in comparison with single-parameter nCBV analysis. KEY POINTS: • Permeability measurement can be calculated from DSC-MRI with a tissue residue function-based leakage correction. • Adding Exto CBV aids in the differentiation of PCNSL from glioblastoma. • CBV and Exmeasurements from DSC-MRI were highly reproducible.
Entities:
Keywords:
Glioblastoma; Lymphoma; Magnetic resonance imaging; Perfusion magnetic resonance imaging; Permeability
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