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Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion for Differentiating among Glioblastoma, Metastasis, and Lymphoma Focusing on Diffusion-Related Parameter.

Shim WH, Kim HS, Choi CG, Kim SJ - PLoS ONE (2015)

Bottom Line: Using a mono-exponential fitting of diffusion signal decay, the mean ADCmin was significantly lower in PCNSL than in glioblastoma and metastasis.However, using a bi-exponential fitting, the mean Dmin did not significantly differ in the three groups.The mean fmax significantly increased in the glioblastomas (reader 1, 0.103; reader 2, 0.109) and the metastasis (reader 1, 0.105; reader 2, 0.107), compared to the primary CNS lymphomas (reader 1, 0.025; reader 2, 0.023) (P < .001 for each).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

ABSTRACT

Background and purpose: Brain tumor cellularity has been assessed by using apparent diffusion coefficient (ADC). However, the ADC value might be influenced by both perfusion and true molecular diffusion, and the perfusion effect on ADC can limit the reliability of ADC in the characterization of tumor cellularity, especially, in hypervascular brain tumors. In contrast, the IVIM technique estimates parameter values for diffusion and perfusion effects separately. The purpose of our study was to compare ADC and IVIM for differentiating among glioblastoma, metastatic tumor, and primary CNS lymphoma (PCNSL) focusing on diffusion-related parameter.

Materials and methods: We retrospectively reviewed the data of 128 patients with pathologically confirmed glioblastoma (n = 55), metastasis (n = 31), and PCNSL (n = 42) prior to any treatment. Two neuroradiologists independently calculated the maximum IVIM-f (fmax) and minimum IVIM-D (Dmin) by using 16 different b-values with a bi-exponential fitting of diffusion signal decay, minimum ADC (ADCmin) by using 0 and 1000 b-values with a mono-exponential fitting and maximum normalized cerebral blood volume (nCBVmax). The differences in fmax, Dmin, nCBVmax, and ADCmin among the three tumor pathologies were determined by one-way ANOVA with multiple comparisons. The fmax and Dmin were correlated to the corresponding nCBV and ADC using partial correlation analysis, respectively.

Results: Using a mono-exponential fitting of diffusion signal decay, the mean ADCmin was significantly lower in PCNSL than in glioblastoma and metastasis. However, using a bi-exponential fitting, the mean Dmin did not significantly differ in the three groups. The mean fmax significantly increased in the glioblastomas (reader 1, 0.103; reader 2, 0.109) and the metastasis (reader 1, 0.105; reader 2, 0.107), compared to the primary CNS lymphomas (reader 1, 0.025; reader 2, 0.023) (P < .001 for each). The correlation between fmax and the corresponding nCBV was highest in glioblastoma group, and the correlation between Dmin and the corresponding ADC was highest in primary CNS lymphomas group.

Conclusion: Unlike ADC value derived from a mono-exponential fitting of diffusion signal, diffusion-related parametric value derived from a bi-exponential fitting with separation of perfusion effect doesn't differ among glioblastoma, metastasis, and PCNSL.

No MeSH data available.


Related in: MedlinePlus

The image processing steps and workflows of imaging parameters.
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pone.0134761.g001: The image processing steps and workflows of imaging parameters.

Mentions: For pathology and imaging correlations of IVIM parameters, analysis of the voxel-wise, calculated, parametric maps was based on hand-drawn ROIs manually placed by two neuroradiologists in consensus on the tumor area using the hot-spot method. To correlate f and D with nCBV and ADC, respectively, the nCBV and ADC values were recalculated in the corresponding ROIs of fmax and Dmin, respectively. The image processing steps and workflows of imaging parameters are shown in Fig 1.


Comparison of Apparent Diffusion Coefficient and Intravoxel Incoherent Motion for Differentiating among Glioblastoma, Metastasis, and Lymphoma Focusing on Diffusion-Related Parameter.

Shim WH, Kim HS, Choi CG, Kim SJ - PLoS ONE (2015)

The image processing steps and workflows of imaging parameters.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4520473&req=5

pone.0134761.g001: The image processing steps and workflows of imaging parameters.
Mentions: For pathology and imaging correlations of IVIM parameters, analysis of the voxel-wise, calculated, parametric maps was based on hand-drawn ROIs manually placed by two neuroradiologists in consensus on the tumor area using the hot-spot method. To correlate f and D with nCBV and ADC, respectively, the nCBV and ADC values were recalculated in the corresponding ROIs of fmax and Dmin, respectively. The image processing steps and workflows of imaging parameters are shown in Fig 1.

Bottom Line: Using a mono-exponential fitting of diffusion signal decay, the mean ADCmin was significantly lower in PCNSL than in glioblastoma and metastasis.However, using a bi-exponential fitting, the mean Dmin did not significantly differ in the three groups.The mean fmax significantly increased in the glioblastomas (reader 1, 0.103; reader 2, 0.109) and the metastasis (reader 1, 0.105; reader 2, 0.107), compared to the primary CNS lymphomas (reader 1, 0.025; reader 2, 0.023) (P < .001 for each).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

ABSTRACT

Background and purpose: Brain tumor cellularity has been assessed by using apparent diffusion coefficient (ADC). However, the ADC value might be influenced by both perfusion and true molecular diffusion, and the perfusion effect on ADC can limit the reliability of ADC in the characterization of tumor cellularity, especially, in hypervascular brain tumors. In contrast, the IVIM technique estimates parameter values for diffusion and perfusion effects separately. The purpose of our study was to compare ADC and IVIM for differentiating among glioblastoma, metastatic tumor, and primary CNS lymphoma (PCNSL) focusing on diffusion-related parameter.

Materials and methods: We retrospectively reviewed the data of 128 patients with pathologically confirmed glioblastoma (n = 55), metastasis (n = 31), and PCNSL (n = 42) prior to any treatment. Two neuroradiologists independently calculated the maximum IVIM-f (fmax) and minimum IVIM-D (Dmin) by using 16 different b-values with a bi-exponential fitting of diffusion signal decay, minimum ADC (ADCmin) by using 0 and 1000 b-values with a mono-exponential fitting and maximum normalized cerebral blood volume (nCBVmax). The differences in fmax, Dmin, nCBVmax, and ADCmin among the three tumor pathologies were determined by one-way ANOVA with multiple comparisons. The fmax and Dmin were correlated to the corresponding nCBV and ADC using partial correlation analysis, respectively.

Results: Using a mono-exponential fitting of diffusion signal decay, the mean ADCmin was significantly lower in PCNSL than in glioblastoma and metastasis. However, using a bi-exponential fitting, the mean Dmin did not significantly differ in the three groups. The mean fmax significantly increased in the glioblastomas (reader 1, 0.103; reader 2, 0.109) and the metastasis (reader 1, 0.105; reader 2, 0.107), compared to the primary CNS lymphomas (reader 1, 0.025; reader 2, 0.023) (P < .001 for each). The correlation between fmax and the corresponding nCBV was highest in glioblastoma group, and the correlation between Dmin and the corresponding ADC was highest in primary CNS lymphomas group.

Conclusion: Unlike ADC value derived from a mono-exponential fitting of diffusion signal, diffusion-related parametric value derived from a bi-exponential fitting with separation of perfusion effect doesn't differ among glioblastoma, metastasis, and PCNSL.

No MeSH data available.


Related in: MedlinePlus