Original Research  n  Neuroradiology

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Atypical Imaging Features of Primary Central Nervous System Lymphoma That Mimics Glioblastoma: Utility of Intravoxel Incoherent Motion MR Imaging1 Chong Hyun Suh, MD Ho Sung Kim, MD Seung Soo Lee, MD Namkug Kim, PhD Hee Mang Yoon, MD Choong-Gon Choi, MD Sang Joon Kim, MD

1

 From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, Korea. From the 2013 RSNA Annual Meeting. Received August 13, 2013; revision requested October 7; revision received December 23; accepted January 28, 2014; final version accepted January 31. Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (grant 2011-0002629). Address correspondence to H.S.K. (e-mail: radhskim@ gmail.com).

Purpose:

To determine the utility of intravoxel incoherent motion (IVIM)–derived perfusion and diffusion parameters for differentiation of atypical primary central nervous system lymphoma (PCNSL) from glioblastoma in patients who do not have acquired immunodeficiency syndrome.

Materials and Methods:

The institutional review board approved this retrospective study and waived the informed consent requirement. Sixty patients with either pathologic analysis–confirmed atypical PCNSLs (n = 19) or glioblastomas (n = 41) were assessed by using maximum IVIM-derived perfusion fraction (f) and minimum true IVIM diffusion parameter (D). Two readers independently calculated IVIM parameters and maximum normalized cerebral blood volume (nCBV) and minimum apparent diffusion coefficient. Leave-oneout cross-validation and intraclass correlation coefficients were assessed to determine reliability and reproducibility of the parameters, respectively.

Results:

Mean maximum f was significantly higher in the glioblastoma group than in the atypical PCNSL group (reader 1, 0.101 6 0.016 [standard deviation] vs 0.021 6 0.010; P , .001; reader 2: 0.107 6 0.024 vs 0.027 6 0.015; P , .001). Mean minimum D did not significantly differ between the two groups (reader 1, P = .202; reader 2, P = .091). By using maximum f as a discriminative index, respective sensitivity and specificity were 89.5% and 95.1% for reader 1 and 84.2% and 95.1% for reader 2. There was a significant positive correlation between maximum f and the corresponding nCBV (r = 0.68; P , .001). The intraclass correlation coefficient between readers was highest for measurement of maximum f (intraclass correlation coefficient, 0.92).

Conclusion:

IVIM imaging can be used as a noninvasive imaging method to differentiate malignant brain tumors that show similar conventional MR imaging features.  RSNA, 2014

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 RSNA, 2014

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A

ccurate preoperative differentiation between primary central nervous system lymphoma (PCNSL) and glioblastoma is important to determine appropriate treatment strategies. However, despite some characteristic conventional magnetic resonance (MR) imaging features, it may be difficult to distinguish PCNSL from glioblastoma. When viewed by using conventional MR imaging, PCNSL in an immunocompetent patient usually manifests as single or multiple relatively homogeneously enhanced mass lesions. However, irregular rim-like enhancement is frequently observed in immunocompromised patients with PCNSL because of tumor necrosis, and it is reported in up to 75% of patients who have acquired immunodeficiency syndrome (AIDS) (1). The imaging features (including necrosis, hemorrhage, and irregular rim-like enhancement) are usually atypical of PCNSL in patients without AIDS because they more closely resemble glioblastoma. Therefore, the differential diagnosis between PCNSL and glioblastoma is challenging in non-AIDS patients with necrosis, hemorrhage, and irregular rim-like enhancement compared with AIDS patients with these imaging findings. That is why a cohort of nonAIDS patients was chosen to differentiate between glioblastoma and atypical PCNSL in this study. On advanced imaging studies, as shown in previous reports (2,3), the diffusion and perfusion characteristics within solid tumor areas might provide additional information in the differential diagnosis between PCNSL with atypical

Advance in Knowledge nn Intravoxel incoherent motion (IVIM)–derived perfusion parameter can provide a reliable distinction between primary central nervous lymphoma (PCNSL) with atypical imaging features and glioblastoma in immunocompetent patients (glioblastoma vs PCNSL, 0.101–0.107 vs 0.021– 0.027, respectively; P , .001).

imaging features and other malignant brain tumors, such as glioblastoma. There has been a resurgent interest in intravoxel incoherent motion (IVIM) MR imaging, in which diffusion is modeled by a Gaussian function for obtaining perfusion and diffusion information regarding lesions. IVIM was introduced by Le Bihan et al (4,5) as a method for simultaneously measuring perfusion and diffusion. By using the IVIM theory, both true molecular diffusion and perfusion in the capillary network can be estimated by using a single diffusion imaging acquisition technique. Several research groups have shown encouraging results regarding promising perfusion measurements with IVIM, mostly in animal brains (6–8), but occasionally in human brains (5,9–11). In one pioneering study (12), IVIM was used to quantify perfusion in the human brain. This study demonstrated the potential for IVIM MR imaging to be used for the quantitative measurement of brain perfusion in humans. Compared with dynamic susceptibility contrast agent–enhanced perfusion MR imaging, IVIM has a unique capillary dependence that is not sensitive to coherent laminar flow of arteries and veins. The measurement of IVIM is intrinsically local, and the encoding and readout is performed at the same location (4,5). Both diffusion and perfusion characteristics, which respectively reflect tumor cellularity and vascularity, are important in the diagnosis and treatment response of brain tumors. In our clinical experience, the major advantage of IVIM MR imaging is that it allows the simultaneous acquisition of diffusion and perfusion parameters, and therefore can provide both measurements within corresponding solid lesions

Implication for Patient Care nn IVIM MR imaging allows noninvasive, reliable distinction as part of the diagnostic workup for patients suspected of having malignant brain tumors.

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without the requirement for a further coregistration processing step. To our knowledge, IVIM MR imaging is rarely used for imaging brain tumors. In this study, we attempted to validate the IVIM-derived perfusion fraction (f) by using both the pathologic correlation and cerebral blood volume (CBV) that was normalized (nCBV) derived from dynamic susceptibility contrast-enhanced MR perfusion imaging, which is commonly used as a perfusion parameter for assessing brain tumor vascularity. On the other hand, recently published reports have shown that the apparent diffusion coefficient (ADC) derived from a monoexponential model of diffusionweighted imaging can differentiate glioblastoma from PCNSL (2,3,13– 15). We tested whether the true IVIM diffusion parameter (D) derived from a biexponential model (ie, a model that separates perfusion effects) differs in atypical PCNSL and glioblastoma compared with ADC in the same lesions. The purpose of our study was to determine the utility of IVIM-derived perfusion and diffusion parameters for differentiation of atypical

Published online before print 10.1148/radiol.14131895  Content code: Radiology 2014; 272:504–513 Abbreviations: ADC = apparent diffusion coefficient AIDS = acquired immunodeficiency syndrome CBV = cerebral blood volume D = true IVIM diffusion parameter f = IVIM-derived perfusion fraction IVIM = intravoxel incoherent motion nCBV = normalized CBV PCNSL = primary central nervous system lymphoma Author contributions: Guarantors of integrity of entire study, C.H.S., H.S.K.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, C.H.S., H.S.K., H.M.Y.; clinical studies, C.H.S., H.S.K., S.S.L., H.M.Y., C.G.C., S.J.K.; experimental studies, C.H.S., H.S.K., N.K.; statistical analysis, C.H.S., H.S.K.; and manuscript editing, C.H.S., H.S.K., H.M.Y. Conflicts of interest are listed at the end of this article.

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PCNSL from glioblastoma in nonAIDS patients.

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Figure 1

Materials and Methods Patient Population Our institutional review board approved this retrospective study and waived the informed consent requirement. A retrospective review of our institution’s database identified 477 patients who underwent MR examination for brain tumor evaluation between August 2011 and July 2013. Among these patients, 60 patients were included as the study patient group according to the following criteria: (a) they were immunocompetent patients; (b) they were confirmed by pathologic analysis to have with glioblastoma or PCNSL before any treatment; (c) in the patients with PCNSL, they received an initial diagnosis of glioblastoma or metastasis because the initial conventional MR imaging findings were atypical for PCNSL in non-AIDS patients, which prompted some clinical confusion before histologic confirmation of the diagnosis; (d) they did not have corticosteroid administration at the time of the IVIM MR imaging; and (e) their studies had adequate image acquisition and quality without patient motion or susceptibility artifact. None of the included patients had neurologic disorders other than a primary neoplasm. Excluded from the study were 292 patients with pathologic analyses that showed tumor other than glioblastoma and PCNSL and 42 patients who showed typical findings of PCNSL and PCNSL was initially diagnosed on conventional MR images. In the remaining 143 patients, five patients with AIDS, 53 patients without IVIM studies, and 19 patients who were treated with a steroid at the time of IVIM imaging study were excluded. Six patients were finally excluded because of poor image quality associated with hemorrhage or patient motion. Histopathologic confirmation was obtained in all patients, in 45 patients (30 patients with glioblastoma; 15 patients with atypical PCNSL) at gross total or partial surgical resection and 506

Figure 1:  Flowchart of the study population.

in 15 patients (11 patients with glioblastoma; four patients with atypical PCNSL) at stereotactic biopsy. Of the 60 study patients, 33 were men (mean age, 56.0 years; age range, 35.0–83.0 years) and 27 were women (mean age, 52.2 years; age range, 25.0–67.0 years), with an overall mean age of 54.1 years (age range, 25.0–83.0 years). The study patient registration process is summarized in Figure 1.

IVIM Model IVIM MR imaging is a concept and a method that was initially introduced and developed by Le Bihan et al (5,16) to quantitatively assess all the microscopic translational motions that might contribute to the signal acquired by using diffusion-weighted imaging. In biologic tissue, these motions are essentially the molecular diffusion of water and microcirculation of blood in the capillary network (ie, perfusion). The concept introduced by Le Bihan et al is that water flowing in randomly oriented capillaries (at the voxel level) mimics a random walk. In the presence of the magnetic field gradient pulses of a diffusion-weighted imaging sequence, the MR imaging signal gets attenuated due to diffusion and perfusion effects. The relation between signal variation and b factors with an IVIM-type sequence

can be expressed by using the following Equation (16):

where S is the mean signal intensity, b is b value, S0 is the signal intensity without diffusion, and D* is D representing incoherent microcirculation within the voxel (ie, perfusion-related diffusion or the fast component of diffusion) (Fig 2).

MR Acquisition Protocols MR imaging was performed by using a 3-T system (Achieva; Philips Medical Systems, Best, the Netherlands) with an eight-channel sensitivity-encoding head coil and a standard, monopolar, pulsed-gradient, spin-echo, echo-planar imaging sequence (9) that is routinely used for diffusion-weighted imaging. We acquired 16 different b values (0, 10, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 300, 500, 700, and 900 sec/ mm2) in three orthogonal directions. The distribution of b values was chosen to cover both the initial pseudodiffusion decay (b , 200 sec/mm2) and the molecular diffusion decay (b  200 sec/ mm2) (17). We used a large number of lower b values in our study to improve the accuracy of the pseudodiffusion.

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CBV value independently defined by two radiologists (H.S.K., with 9 years of clinical experience in neuro-oncologic imaging, and C.H.S., with 3 years of clinical experience in image analysis).

Figure 2

Figure 2:  Graph of principle of IVIM shows decomposition of the biexponential relative signal decay as a function of b values. S = mean signal intensity.

The dependence of the diffusionweighted signal (in log plots) on the b value is not straight (which would have been expected for free diffusion), it is curved, which reflects the multiplicity of the underlying process (18). Given the relative values (ie, the diffusion parameter that represents incoherent microcirculation within the voxel and the diffusion parameter that represents pure molecular diffusion), perfusion is expected to contribute to this curvature in a biexponential mode (5) for b values in the very low range (0–200 sec/mm2, or higher for very slow flow). The detailed imaging parameters for the IVIM study were as follows: repetition time msec/echo time msec, 3000/72; flip angle, 90°; field of view, 24 cm; section thickness, 5 mm; gap, 2 mm; matrix, 136 3 138; number of sections, 20. The total acquisition time was 261 seconds. Diffusion-weighted imaging with multiple b values was performed before contrast-enhanced MR imaging. Dynamic susceptibility contrastenhanced MR perfusion imaging was performed by using a gradient-echo echo-planar sequence during administration of a standard (0.1 mmol/kg) dose of gadoterate meglumine (Dotarem; Guerbet, Paris, France) per kilogram of body weight at a rate of 4 mL/ sec by using an MR imager–compatible power injector (Spectris; Medrad, Pittsburgh, Pa). The bolus of contrast material was followed by a 20-mL bolus of saline administered at the same

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injection rate. The detailed imaging parameters for the dynamic susceptibility contrast-enhanced study are as follows: 1407/40; flip angle, 35°; field of view, 24 cm; matrix, 128 3 128; number of sections, 20. The total dynamic susceptibility contrast-enhanced MR image acquisition time was 90 seconds.

Image Processing For quantitative analysis, all raw imaging data were transferred from the MR imager to an independent personal computer. An in-house program with software (Matlab 2010b; Mathworks, Natick, Mass) was developed for IVIM processing by using the biexponential model. We implemented simplified biexponential models, including the Le Bihan simplified method, the Luciani method, and the Sigmund method. A full biexponential model was also implemented by an image processing expert (N.K., with 10 years of clinical experience in image processing) to evaluate the exact IVIM diffusion parameters. The nCBV maps were obtained by using a commercial software package (Nordic Ice; NordicNeuroLab, Bergen, Norway). After eliminating recirculation of the contrast agent by using g-variate curve fitting and contrast agent leakage correction, the relative CBV was computed by numeric integration of the curve. The nCBV maps were calculated by dividing each relative CBV value by an unaffected white matter–relative

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Image Analysis For quantitative analysis, contrastenhanced lesion volumes were segmented on three-dimensional contrastenhanced T1-weighted images by using a semiautomated, adaptive thresholding technique. The entire contrast-enhanced lesion volumes that occurred as a result were then coregistered and mapped to the f, D, nCBV, and ADC maps. For pathologic correlation, maximum f, minimum D, maximum nCBV, and minimum ADC were calculated by using the hot-spot method according to the following steps: (a) Five regions of interest were manually independently constructed by the experienced radiologists (H.S.K. and C.H.S.) for segmented nonnecrotic enhancing tumor as a reference; (b) the size of the regions of interest remained constant (radius, 2.0 mm); and (c) hot-spot regions of interest were obtained by visually choosing the highest (maximum f, maximum nCBV) and the lowest (minimum D, minimum ADC) tumor parametric values. For correlation of IVIM parameters with nCBV and ADC, we also calculated D, nCBV, and ADC values in all of the corresponding regions of interest for f. We then correlated f with nCBV and assessed the difference between ADC and D according to the increase of nCBV in the same region of interest. Statistical Analyses All data were expressed as mean 6 standard deviation. To assess the significant differences of the maximum f, minimum D, maximum nCBV, and minimum ADC between the glioblastoma group and the atypical PCNSL group, student t tests were used. For maximum f, minimum D, maximum nCBV, and minimum ADC, a receiver operating characteristic curve was constructed to differentiate atypical PCNSL from glioblastoma. An optimum cutoff value 507

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for each parameter was determined by maximizing the sum of the sensitivity and specificity. Areas under the receiver operating characteristic curves were compared by using the method developed by DeLong et al (19). A leave-one-out cross-validation method was used to evaluate the performance of the independent variables. In each round of the leave-one-out validation, one participant was selected as a testing sample. The remaining participants were used as training samples to construct the classifier. The testing sample was then classified with the trained classifier. The procedure was repeated until each participant was tested once. Association of f and corresponding nCBV and association of difference between ADC and D and corresponding nCBV were assessed by using partial correlation analysis, with adjustments made for the final pathologic diagnosis. Statistical analysis was performed by using software (SPSS 19.0 for Windows; SPSS, Chicago, Ill). Interreader agreement was assessed by using the intraclass correlation coefficient with 95% confidence intervals and by applying a two-way intraclass correlation coefficient with random rater assumption. The intraclass correlation coefficient ranged between 0 and 1.00, and values closer to 1.00 represented better reproducibility. Intraclass correlation coefficients were interpreted as follows: 0.00–0.20, slight reproducibility; 0.21–0.40, fair reproducibility; 0.41–0.60, moderate reproducibility; 0.61–0.80, substantial reproducibility; and greater than 0.80, almost perfect reproducibility. P values less than .05 indicated statistical significance.

Results The mean interval between MR imaging and histopathologic analysis was 17.7 days. The mean times, after image processing, of maximum f and minimum D of IVIM MR imaging were 229 seconds for reader 1 and 267 seconds for reader 2. Descriptive statistics regarding the demographic data obtained in both the 508

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Table 1 Comparison of Patient Demographic Data Variable Sex distribution   No. of men   No. of women Mean age (y)* Surgical extent   No. of stereotactic biopsy   No. of gross total or partial resection

Glioblastoma

Atypical PCNSL

24 (58.5) 17 (41.5) 53.9 6 9.7

9 (47.4) 10 (52.6) 54.7 6 8.1

11 30

4 15

P Value .511 … … .726 .701 … …

Note.—Data in parentheses are percentages. * Data are 6 standard deviation.

glioblastoma and the atypical PCNSL patients are summarized in Table 1.

Visual Analysis of the IVIM MR Parameters Among 60 study patients, all glioblastoma patients and five (26%) atypical PCNSL patients showed that the signal decay curve as a function of the diffusion b values (which ranged from 0 to 900 sec/mm2) was biexponential for both readers. Glioblastomas show more rapid signal decay in the range of lower b values (b , 200 sec/mm2) than does atypical PCNSL (Fig 3). In 14 of 19 patients (74%) with atypical PCNSL, the signal decay curve was similar to monoexponential pattern (Fig 4). IVIM versus Dynamic Susceptibility Contrast-enhanced MR Imaging Studies in Relation to Pathologic Diagnosis Mean 6 standard deviation of maximum f, minimum D, maximum nCBV, and minimum ADC in both the glioblastoma and the atypical PCNSL groups are shown in Table 2, and representative cases of each patient group are shown in Figures 3 and 4. By using the t test, we found a significant difference between the two tumor pathologic analyses for both mean maximum f and the mean maximum nCBV. The mean maximum f was significantly higher in the glioblastoma group (reader 1, 0.101 6 0.016; reader 2, 0.107 6 0.024) than in the atypical PCNSL group (reader 1, 0.021 6 0.010; reader 2, 0.027 6 0.015) (P , .001 for each reader). Maximum

nCBV was also significantly higher in the glioblastoma group (reader 1, 7.05 6 0.98; reader 2, 6.79 6 1.22) than in the atypical PCNSL group (reader 1, 3.02 6 1.09; reader 2, 2.97 6 0.97) (P , .001 for each reader). Mean minimum ADC was significantly lower in the atypical PCNSL group (reader 1, 0.85 [1023 mm2 ∙ sec21] 6 0.09; reader 2, 0.86 [1023 mm2 ∙ sec21] 6 0.07) than in the glioblastoma group (reader 1, 0.91 [1023 mm2 ∙ sec21] 6 0.11; reader 2, 0.93 [1023 mm2 ∙ sec21] 6 0.14) (reader 1, P = .021; reader 2, P = .009). However, the mean minimum D did not significantly differ in the two groups (glioblastoma group: reader 1, 0.87 [1023 mm2 ∙ sec21] 6 0.12; reader 2, 0.89 [1023 mm2 ∙ sec21] 6 0.14; atypical PCNSL group: reader 1, 0.84 [1023 mm2 ∙ sec21] 6 0.09; reader 2, 0.85 [1023 mm2 ∙ sec21] 6 0.11) (reader 1, P = .202; reader 2, P = .091). The results of the receiver operating characteristic analyses of the imaging parameters derived from IVIM and dynamic susceptibility contrast-enhanced MR perfusion imaging used to distinguish atypical PCNSL from glioblastoma are summarized in Table 3. Although the maximum nCBV showed larger areas under the receiver operating characteristic curves than did the maximum f, the difference of the area under the receiver operating characteristic curve between them was not significant. Table 3 also shows the sensitivities and specificities of the imaging parameters to distinguish atypical PCNSL from

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Figure 3

Figure 3:  A–F, Images obtained in a 53-year-old woman with glioblastoma. A, On contrast-enhanced T1-weighted image obtained before surgery, the necrotic contrast-enhanced mass is seen in the right parietal lobe. B, nCBV map shows markedly increased nCBV value in the corresponding area of the contrast-enhanced lesion. C, Diffusion-weighted image and, D, ADC map show decreased ADC value in the corresponding area of the contrast-enhanced lesion. Maps of f (E) and D (F) show visual increase of f value and visual decrease of D value in the corresponding area (arrows) of the contrast-enhanced lesion, respectively. G, Graph shows log signal intensity decay as a function of different b values, it is biexponential at the regions of interest (arrows) of solid tumor portion. There is a steeper slope of signal attenuation for b values near 0 sec/mm2, which can be attributed to microcapillary perfusion.

glioblastoma when all 60 study patients were regarded as a training set. By using the maximum f as a discriminative index, the respective sensitivity and specificity were 89.5% and 95.1% for reader 1 and 84.2% and 95.1% for reader 2. By using the maximum nCBV as a discriminative index, the sensitivity and specificity were 94.7% and 92.7% for reader 1 and 94.7% and 90.2% for reader 2, respectively.

Interreader Agreement Table 4 summarizes the interreader agreement by using the corresponding intraclass correlation coefficients. Interreader agreement was highest for measurement of maximum f (intraclass correlation coefficient, 0.92). The intraclass correlation coefficients between readers were higher for calculations of the perfusion parameters including maximum f and maximum nCBV (intraclass correlation coefficient range, 0.87–0.92) than for calculations of the diffusion parameters, including

minimum D and minimum ADC (intraclass correlation coefficient range, 0.71–0.77).

Correlation between Maximum f and Corresponding nCBV and between Difference of ADC and D and Corresponding nCBV Partial correlation analysis showed a significantly positive correlation between maximum f and corresponding nCBV (r = 0.68; P , .001) for all cases with tumor pathologic analysis as the controlling variable. Difference in ADC and D was significantly associated with corresponding nCBV value (r = 0.57; P , .001), which indicates that ADC is more contaminated by increased nCBV in glioblastomas than in atypical PCNSLs. In subgroup analyses, the correlation between maximum f and corresponding nCBV was more significant in the glioblastoma group (r = 0.74 [95% confidence interval: 0.55, 0.85]; P , .001) than in the atypical PCNSL

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group (r = 0.60 [95% confidence interval: 0.20, 0.83]; P , .007) (Fig 5). The difference in ADC and D was more significantly associated with corresponding nCBV value in the glioblastoma group (r = 0.71 [95% confidence interval: 0.52, 0.84]; P , .001) than in the atypical PCNSL group (r = 0.43 [95% confidence interval: 20.29, 0.60]; P = .426) (Fig 6).

Discussion Our study showed that all of the atypical PCNSLs showed significant maximum f decreases in solid-enhancing areas compared with glioblastomas. However, the minimum D was not significantly different between the two tumor groups. The maximum f values obtained in solid-enhancing areas were significantly correlated with the corresponding nCBV values. The value of the difference in ADC and D that was obtained in solid-enhancing areas was also significantly correlated with the 509

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Figure 4

Figure 4:  A–F, Images obtained in a 50-year-old woman with atypical PCNSL, which was initially diagnosed as glioblastoma on conventional MR image. A, Contrast-enhanced T1-weighted image obtained before surgery shows necrotic contrast-enhanced mass in the right frontal lobe. B, nCBV map shows slightly increased nCBV value in the corresponding area of the contrast-enhanced lesion (arrow). C, Diffusion-weighted image and, D, ADC map show decreased ADC value in the corresponding area of the contrast-enhanced lesion. Maps of f (E) and D (F) show no increase of f value and visual decrease of D value in the corresponding area of the contrast-enhanced lesion, respectively. G, Graph shows log signal intensity decay as a function of different b values. It is monoexponential at the regions of interest (arrows) of solid tumor portion.

Table 2 Differences in the Imaging Parameters in Patients with Glioblastoma and Those with Atypical PCNSL Reader 1 Parameter fmax Dmin (1023 mm2 ∙ sec21) nCBVmax ADCmin (1023 mm2 ∙ sec21)

Reader 2

Glioblastoma

Atypical PCNSL

P Value

Glioblastoma

Atypical PCNSL

P Value

0.101 6 0.016 0.87 6 0.12 7.05 6 0.98 0.91 6 0. 11

0.021 6 0.010 0.84 6 0.09 3.02 6 1.09 0.85 6 0. 09

,.001 .202 ,.001 .021

0.107 6 0.024 0.89 6 0.14 6.79 6 1.22 0.93 6 0. 14

0.027 6 0.015 0.85 6 0.11 2.97 6 0.97 0.86 6 0. 07

,.001 .091 ,.001 .009

Note.—Data are mean 6 standard deviation. ADCmin = minimum ADC, Dmin = minimum D, fmax = maximum f, nCBVmax = maximum nCBV.

corresponding nCBV values. In our study, maximum f and maximum nCBV have similar diagnostic performance for differentiation of atypical PCNSLs from glioblastomas. The statistically significant difference of maximum f between the atypical PCNSL and the glioblastoma groups correlates with the results of previous studies, which showed that 510

PCNSL had lower mean relative CBV (20–22) or lower maximum relative CBV values (2,23–25) than glioblastoma. Ma et al (26) reported that it is possible to distinguish glioblastoma from PCNSL by using whole-tumor histogram analysis of nCBV. Compared with glioblastoma, tumor neovascularization is poor in PCNSL, which is well known for its angiocentric growth

pattern in which PCNSL cells tend to cluster around pre-existing brain vessels (27). This may explain the lower maximum f in the PCNSL patients in our study. As shown in previous reports (2,20–26) and in our results, perfusion parameters including maximum f and nCBV can be used to accurately differentiate atypical PCNSL from glioblastoma.

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Table 3 Diagnostic Performance of IVIM MR Imaging Parameters, Maximum nCBV, and Minimum ADC Values for Differentiation of Glioblastoma from Atypical PCNSL Parameter Reader 1   fmax   Dmin (1023 mm2 sec21)  nCBVmax  ADCmin (1023 mm2 sec21) Reader 2   fmax   Dmin (1023 mm2 sec21)  nCBVmax  ADCmin (1023 mm2 sec21)

AUC Value

Cutoff

Sensitivity (%)

Specificity (%)

0.962 (0.879, 0.991) 0.589 (0.459, 0.721) 0.969 (0.889, 0.993) 0.709 (0.573, 0.819)

0.042 0.85 4.02 0.87

89.5 34.2 94.7 60.5

95.1 90.5 92.7 78.9

0.949 (0.856, 0.982) 0.601 (0.472, 0.739) 0.961 (0.876, 0.989) 0.727 (0.585, 0.839)

0.045 0.86 3.95 0.87

84.2 36.8 94.7 68.4

95.1 90.2 90.2 82.9

Note.—Data in parentheses are 95% confidence intervals. ADCmin = minimum ADC, AUC = area under the receiver operating characteristic curve, Dmin = minimum D, fmax = maximum f, nCBVmax = maximum nCBV.

Table 4 Interreader Intraclass Correlation Coefficient for Measurements of Imaging Parameters Parameter fmax Dmin nCBVmax ADCmin

Interreader ICC 0.92 (0.83, 0.97) 0.77 (0.65, 0.87) 0.87 (0.75, 0.94) 0.71 (0.57, 0.84)

Note.—Data in parentheses are 95% confidence intervals. ADCmin = minimum ADC, Dmin = minimum D, fmax = maximum f, ICC = intraclass correlation coefficient, nCBVmax = maximum nCBV.

Several previous studies (2,3,13– 15) showed that the ADC value, measured from diffusion-weighted imaging with monoexponential model, can help to differentiate glioblastoma from PCNSL. Guo et al (3) reported that the mean ADC values relative to the normal white matter in the PCNSL were lower than those in glioblastoma, which therefore indicated that high cellularity in the PCNSL contributes to the restricted diffusion. Yamasaki et al (13) and Wang et al (28) also reported that ADC values in PCNSL were significantly lower than those in glioblastoma. In our study, we also found a statistically significant difference of minimum ADC between the two pathologic groups. However, our

study showed that the difference of minimum D was not statistically significant between the glioblastoma and the atypical PCNSL groups. Although the exact pathophysiologic mechanism for the difference between minimum ADC and minimum D results is unclear, we speculate that there is a perfusion difference between the glioblastoma and the atypical PCNSL groups. We also found a statistically significant correlation between nCBV and difference of ADC and D within the same regions of interest. Any ADC estimation with only two b values (eg, 0 and 1000 sec/mm2), which is usually performed for clinical studies, would miss the curvature, include perfusion effects, and result in an ADC that is an overestimation of D (18,29). Further studies are needed to support our speculations. Despite some characteristic conventional MR imaging findings, it may be difficult to distinguish PCNSL from glioblastoma (30). However, accurate preoperative differentiation between these two tumors is important for the determination of appropriate treatment strategies. As seen by using conventional MR imaging, PCNSL in an immunocompetent patient usually manifests as a homogeneously enhanced parenchymal mass. The imaging features, including necrosis, hemorrhage, and irregular rim-like enhancement, are

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usually atypical of PCNSL in non-AIDS patients, and more closely resemble glioblastoma. In our study, all of the PCNSLs were initially diagnosed as glioblastomas or metastatic tumors because of their atypical imaging features, such as necrosis. However, IVIM-derived combined diffusion and perfusion informations within solid tumor areas provided additional information for the accurate diagnosis of atypical PCNSL. The characteristics of both diffusion and perfusion, which respectively reflect tumor cellularity and vascularity, are important for the diagnosis and treatment response of brain tumors. In our clinical experience, IVIM MR imaging can allow the simultaneous acquisition of diffusion and perfusion parameters. Therefore, it can provide both measures within corresponding solid lesions without the requirement for a further coregistration processing step. In our study, the visual and quantitative analyses of the IVIM parameters demonstrated results close to those seen in actual clinical situations, with a reasonable amount of time for image and postprocessing analysis. Quantitative brain tumor perfusion measurement remains a challenge with the MR perfusion methods that are currently available. Dynamic susceptibility contrast-enhanced and dynamic contrastenhanced T1-weighted perfusion MR imaging examinations are hampered by their signal nonlinearity and dependence on the arterial input function. In addition to a low signal-to-noise ratio, arterial spin labeling exhibits a strong dependence on transit time. The major advantages compared with other perfusion MR techniques are as follows: direct sensitivity to blood flow within the capillary bed independent of arterial transit and venous blood volume and robustness of the Stejskal-Tanner pulse sequence. These properties can be key advantages for the IVIM method to become a practical perfusion imaging technique in the clinical setting. Our study clarified the different perfusion characteristics of atypical PCNSL and glioblastoma based on the IVIM biexponential model by using multiple b values. According to receiver operating 511

NEURORADIOLOGY: Atypical Imaging Features of Primary Central Nervous System Lymphoma

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motions associated with microcirculation of blood within randomly distributed capillaries. Moreover, spin-echo– based IVIM imaging has a substantially different vessel size sensitivity profile from gradient-echo–based dynamic susceptibility contrast-enhanced MR imaging. Further studies that correlate IVIMderived perfusion fraction with various MR perfusion parameters (including cerebral blood flow and permeability parameters) are needed to understand the exact significance of IVIM-derived perfusion parameter. Our study had several limitations. First, the number of study patients was relatively small. Further prospective analyses of a larger number of patients will be needed to validate our results. Second, the set of b values used in this study was not optimized. To achieve a shorter examination time without sacrificing the precision of the techniques, the b values might be further optimized in a separate study as a function of the available IVIM parameter values in the brain. Last, although we found that maximum f was only elevated in glioblastomas, in practice, all these cases will undergo biopsy, so the findings are more about IVIM imaging than the practical distinguishing of PCNSL and glioblastoma. In conclusion, IVIM MR imaging is directly sensitive to blood flow within the capillary bed, independent of arterial transit and venous blood volume, and can provide simultaneous, noncontrast acquisition of both perfusion and diffusion parameters within a corresponding lesion. These properties can be key advantages for the IVIM method to become a practical perfusion imaging technique in the clinical setting. Therefore, IVIM MR imaging can be a noninvasive imaging method for assessment of the combination of diffusion with the perfusion characteristics of malignant brain tumors that show similar conventional MR imaging features.

Figure 5

Figure 5:  Scatterplot of the correlation between f and corresponding nCBV in patients with glioblastoma and atypical PCNSL. There are significant correlations between f and corresponding nCBV for glioblastoma patients and for atypical PCNSL patients. f max = maximum f.

Figure 6

Figure 6:  Scatterplot of the correlation between nCBV and difference in ADC and D in patients with glioblastoma and PCNSL. There is a significant correlation between nCBV and difference in ADC and D for glioblastoma patients but no significant correlation for PCNSL patients.

characteristic curve analysis, maximum f showed excellent diagnostic accuracy as a predictor for differentiation of atypical PCNSL from glioblastoma. Moreover, maximum f showed a positive correlation with the nCBV that has been commonly 512

used as a perfusion parameter for brain tumor vascularity. However, these two perfusion parameters represent different aspects of tumor vessels. CBV primarily measures microvascular density and f measures microscopic translational

Disclosures of Conflicts of Interest: C.H.S. No relevant conflicts of interest to disclose. H.S.K. No relevant conflicts of interest to disclose. S.S.L. No relevant conflicts of interest to disclose. N.K. No relevant conflicts of interest to disclose. H.M.Y. No relevant conflicts of interest

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NEURORADIOLOGY: Atypical Imaging Features of Primary Central Nervous System Lymphoma

to disclose. C.G.C. No relevant conflicts of interest to disclose. S.J.K. No relevant conflicts of interest to disclose.

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Atypical imaging features of primary central nervous system lymphoma that mimics glioblastoma: utility of intravoxel incoherent motion MR imaging.

To determine the utility of intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters for differentiation of atypical primary cen...
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