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DOI: 10.1148/radiol.2432060493
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(Radiology 2007;243:539-550.)
© RSNA, 2007

Special Reports

Intraaxial Brain Masses: MR Imaging–based Diagnostic Strategy—Initial Experience1

Riyadh N. Al-Okaili, MD, Jaroslaw Krejza, MD, PhD, John H. Woo, MD, Ronald L. Wolf, MD, PhD, Donald M. O'Rourke, MD, Kevin D. Judy, MD, Harish Poptani, PhD, and Elias R. Melhem, MD, PhD

1 From the Departments of Radiology (R.N.A., J.K., J.H.W., R.L.W., H.P., E.R.M.) and Neurosurgery (D.M.O., K.D.J.), University of Pennsylvania School of Medicine, 3400 Spruce St, Dulles 2, Philadelphia, PA 19104; and Department of Nuclear Medicine, Medical University of Gdansk, Gdansk, Poland (J.K.). Received March 17, 2006; revision requested May 17; revision received July 31; accepted August 29; final version accepted November 1. Address correspondence to E.R.M. (e-mail: Elias.Melhem{at}uphs.upenn.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Purpose: To develop and retrospectively determine the accuracy of a magnetic resonance (MR) imaging strategy to differentiate intraaxial brain masses, with histologic findings or clinical diagnosis as the reference standard.

Materials and Methods: The study was HIPAA compliant and was approved by the institutional review board. A waiver of informed consent was obtained. A strategy was developed on the basis of conventional MR imaging, diffusion-weighted MR imaging, perfusion MR imaging, and proton MR spectroscopy to classify intraaxial masses as low-grade primary neoplasms, high-grade primary neoplasms, metastatic neoplasms, abscesses, lymphomas, tumefactive demyelinating lesions (TDLs), or encephalitis. The strategy was evaluated by using data from 111 patients (46 women, 65 men; mean age, 48.9 years) with imaging results available on a departmental picture archiving and communication system from a 5-year search period. Bayesian statistics of the strategy elements and three clinical tasks were calculated.

Results: Search results identified 44 patients with high-grade and 14 with low-grade primary neoplasms, 24 with abscesses, 12 with lymphoma, 11 with TDLs, five with metastases, and one with encephalitis who had undergone conventional and advanced MR imaging. However, only 40 patients (25 women, 15 men; mean age, 45 years) had undergone all studies and had data to allow completion of the entire strategy. Accuracy, sensitivity, and specificity of the strategy, respectively, were 90%, 97%, and 67% for discrimination of neoplastic from nonneoplastic diseases, 90%, 88%, and 100% for discrimination of high-grade from low-grade neoplasms, and 85%, 84%, and 87% for discrimination of high-grade neoplasms and lymphoma from low-grade neoplasms and nonneoplastic diseases.

Conclusion: An integrated MR imaging–based strategy, which is accurate in differentiation of several intraaxial brain masses, was proposed.

© RSNA, 2007


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Differentiation of neoplastic from nonneoplastic brain masses by using conventional (defined as obtaining structural cross-sectional images that predominantly provide anatomic information) computed tomography (CT) or magnetic resonance (MR) imaging is frequently difficult, and many cases require biopsy or follow-up imaging. Advanced MR imaging techniques, such as diffusion-weighted MR imaging, MR spectroscopy, and perfusion MR imaging, can further improve the diagnostic accuracy of conventional CT and MR imaging (1–14). Integration of diagnostic information from various MR imaging techniques would provide more reliable differentiation of intraaxial brain masses (1,8,9,14). To our knowledge, no such general diagnostic strategy has been proposed to date. Thus, the purpose of our study was to develop and retrospectively determine the accuracy of an MR imaging–based strategy to differentiate intraaxial brain masses, with histologic findings or clinical diagnosis as the reference standard.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
The retrospective component of our study was performed within a Health Insurance Portability and Accountability Act–compliant protocol and was approved by the institutional review board of the University of Pennsylvania School of Medicine. A waiver of informed consent was obtained.

Development of MR Imaging Strategy
Strategy.—On the basis of our experience and review of the literature, we propose an imaging strategy (Fig 1) to classify unknown intraaxial brain tumors and address discrimination of neoplastic from nonneoplastic diseases, high-grade from low-grade neoplasms, and high-grade neoplasms and lymphoma from low-grade neoplasms and nonneoplastic diseases. The most common intraaxial masses representing diagnostic end points are low-grade primary brain neoplasms, high-grade primary brain neoplasms, metastatic brain neoplasms, abscesses, lymphomas, TDLs, and encephalitis (15,16).


Figure 1
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  		Figure 1: Flowchart illustrates order of methods used to diagnose and differentiate intraaxial masses. Each method provides an answer to a specific question, which is used as a discriminator to distinguish lesions. 1.1/100MM2/ADC = 1.1 x 10–3 mm2/sec, ADC = apparent diffusion coefficient, CE = contrast material enhanced, Cho = choline, NAA = N-acetylaspartate, r/CBV = relative cerebral blood volume, TDL = tumefactive demyelinating lesion.

 
The strategy contains several questions (nodes) that require MR imaging–derived information to be answered (Fig 1).

Question 1: Does the lesion enhance at conventional MR imaging with contrast material?—The general purpose at this point is to discriminate low-grade neoplasms and encephalitis from abscesses and lymphomas because abscesses, lymphomas, TDLs, high-grade primary neoplasms, and metastatic brain neoplasms typically enhance (17,18). Admittedly, contrast enhancement is not specific because it represents disruption of the blood-brain barrier; however, it is a commonly used initial discriminator that can improve lesion detection and differentiation (17). Visual inspection of MR images can easily determine presence of contrast enhancement.

Enhancing Intraaxial Masses
Question 2: Is diffusion facilitated, as determined with diffusion-weighted MR imaging?—The primary purpose at this point is to separate lymphomas from TDLs. ADC is inversely proportional to cellular density (19). Higher ADC values reflect diffusion facilitation. Although there is no universally accepted ADC threshold to determine whether diffusion is facilitated, we adopted a value of 1.1 x 10–3 mm2/sec after a meta-analysis of publications from different centers (1–5,19–39). Diffusion is usually facilitated in TDLs and primary neoplasms but not in abscesses and lymphomas (24,36).

Question 3: Nonfacilitated or reduced diffusion—is there rim enhancement at conventional contrast material–enhanced MR imaging?—This question helps differentiate lymphomas, which typically lack rim enhancement in immunocompetent patients before therapy, from abscesses and high-grade neoplasms, in which central necrosis and rim enhancement are common (40). Presence of necrosis is indicated by a nonenhancing area, which is dark on T1-weighted images, bright on T2-weighted images, and surrounded by enhancing tissue.

Question 4: Facilitated diffusion—is perfusion increased?—The purpose at this point is to differentiate TDLs and abscesses from high-grade neoplasms and metastases. Primary high-grade neoplasms and metastases are expected to have elevated rCBV compared with TDLs, abscesses, and low-grade neoplasms; this is most likely because of differences in angiogenesis induction (5–9,14,41). There is no universally accepted threshold for rCBV to discriminate among the above entities; however, in a cohort of TDL cases, the highest TDL rCBV was 1.79 (6). For the sake of simplicity, we adopted the minimally lower cutoff of 1.75.

Question 5: Facilitated diffusion and increased perfusion—is there infiltration of surrounding tissue?—Differentiation of primary from secondary neoplasms has been addressed in many studies with MR spectroscopy, perfusion MR imaging, and diffusion-weighted MR imaging (1,5,10–14,42–54). ADCs, fractional anistropy, and perfusion MR imaging have weak discriminatory ability in comparison to MR spectroscopy. Contrary to high-grade neoplasms, metastases tend not to infiltrate (5,49,50,54); thus, spectroscopic interrogation of surrounding tissue can be more effective for differential diagnosis than measurements of enhancing tissue (5,49,50). The increased Cho peak is attributed to accelerated cell membrane turnover (1,5,10,12–14,43–53). Higher tumor grades have higher Cho and lower NAA peaks. Absence of a universal threshold for the Cho/NAA ratio led us to adopt a ratio of 1, which showed 100% accuracy in discrimination of primary from secondary neoplasms in one study (49).

Question 6: Nonfacilitated or decreased diffusion and necrosis—is perfusion increased?—Perfusion MR imaging helps to differentiate necrotic enhancing neoplasms from abscesses because high-grade neoplasms induce angiogenesis and have increased metabolism, which cause rCBV elevation (9,14,55). Absence of a universal threshold led us to adopt a threshold of 1.75, as suggested by Law et al (14).

Nonenhancing Intraaxial Masses
Question 7: Is there elevation of Cho/NAA ratio?—Despite overlap of MR spectroscopic ratios for encephalitis, TDLs, and gliomas (1,5,10–14,42–53,56,57), we used a lesional Cho/NAA ratio of 2.2 to separate primary high-grade neoplasms from mimicking lesions such as encephalitis and TDLs. Although this value may not be universal, it was calculated from evaluation of 232 published studies with overlapping Cho/NAA ratios for high-grade gliomas, TDLs, and encephalitis (1,5,10–14,42–53,56,57). Migrational abnormalities and hamartomas of tuberous sclerosis can result in nonenhancing masslike lesions, but these were not included because they commonly manifest in childhood. Tumors with a "no" response to this question pose an imaging challenge, and clinical and imaging follow-up or biopsy are frequently required for further discrimination.

Question 8: Is perfusion increased?—Perfusion MR imaging has better accuracy than MR spectroscopy and ADCs in grading primary brain neoplasms (1,10,11,14,43,44,46). Absence of a universal threshold led us to adopt a value of 1.75 rCBV, as suggested by Law et al (14).

Evaluation of MR Imaging Strategy
We retrospectively reviewed reports from the picture archiving and communication system database from January 2000 to October 2004 with the following keywords: mass, abscess, lymphoma, tumefactive multiple sclerosis, neoplasm, perfusion, and MR spectroscopy. A neuroradiologist (R.N.A., 6 years of experience) reviewed clinical charts and conventional MR images to include patients with any intraaxial mass with a histologic or clinical diagnosis. World Health Organization neoplasm grades were employed (58). We excluded patients with extraaxial lesions, those with human immunodeficiency virus, those with prior therapy that included steroids, and those younger than 20 years. Patients younger than 20 years were not included because some pediatric brain tumors are known to have different imaging characteristics. For instance, grade I astrocytomas can enhance vividly, have elevated perfusion parameters, and have elevated Cho/NAA ratios (59,60). Thus, these specific MR imaging features are not accounted for by the proposed strategy.

With our search criteria, we found records for 111 patients (46 women, 65 men; mean age, 48.9 years; age range, 20–86 years) with 44 high-grade primary neoplasms, 24 abscesses, 12 lymphomas, 14 low-grade primary neoplasms, 11 TDLs, five metastases, and one case of encephalitis. All cases were histologically proved except three abscesses, seven TDLs, and three metastases. The diagnosis for the three abscesses was based on laboratory findings (leukocytosis, elevated erythrocyte sedimentation rate, elevated serum C-reactive protein concentration), positive response of patient to antibiotics, and typical imaging findings (ring enhancement on T1-weighted MR images, hypointense rim separating hyperintense lesion from surrounding hyperintense edema on T2-weighted images, restricted diffusion on diffusion-weighted MR images). TDLs in seven patients were diagnosed based on the presence of focal neurologic symptoms (two or more episodes lasting more than 24 hours and less than 1 month apart) and disseminated white matter lesions on MR images. Metastases in three patients were diagnosed based on identification of a primary tumor or additional secondary lesions at presentation or at follow-up.

Conventional MR imaging.—All patients underwent conventional MR imaging with a 1.5-T system (Signa, GE Medical Systems, Milwaukee, Wis; Symphony, Siemens, Erlangen, Germany). Transverse and sagittal T1-weighted spin-echo (repetition time msec/echo time msec, 600/11; field of view, 220 mm; section thickness, 5 mm; number of signals acquired, one), transverse T2-weighted fast spin-echo (3000–4000/98; field of view, 220 mm; section thickness, 5 mm; number of signals acquired, one), transverse fast fluid-attenuated inversion-recovery (10 000/120; inversion time msec, 2200; field of view, 220 mm; section thickness, 5 mm; number of signals acquired, two), and contrast-enhanced transverse and coronal T1-weighted spin-echo (600/11; field of view, 220 mm; section thickness, 5 mm; number of signals acquired, one) images were obtained after the administration of 0.1–0.2 mmol gadopentetate dimeglumine (Magnevist; Schering, Berlin, Germany) per kilogram of body weight. Transmit-receive GE and Siemens head coils were used.

Diffusion-weighted MR imaging.—In 79 patients, a two-dimensional single-shot echo-planar diffusion-weighted MR imaging sequence was performed with the same 1.5-T MR imagers (10 000/118; field of view, 300 mm; section thickness, 5 mm; number of signals acquired, one) by using sequential application of diffusion-sensitizing gradients (b values, 0 and 1000 sec/mm2) in three orthogonal (x, y, and z) directions. Postprocessing of ADC maps was performed with standard software at a single workstation (FuncTool, version 2; GE Medical Systems). Fifteen patients underwent imaging with a 3.0-T system (Trio; Siemens) with the following parameters: 6500/99; field of view, 220 mm; section thickness, 3 mm; b values, 0 and 1000 sec/mm2. Diffusion-weighting gradients were applied in 12 directions. Postprocessing was performed at a workstation (Leonardo; Siemens). ADC maps were generated by using software (DTI Task Card; Massachusetts General Hospital, Boston, Mass). The lowest ADC value (expressed in x 10–3 mm2/sec) from five to 10 regions of interest (ROIs) of enhancing and nonenhancing parts of a lesion was recorded (R.N.A.). Each ROI was at least 20 mm2 in size, and its upper limit was determined by the size of the targeted homogeneous area of the lesion, regardless of whether that part was solid or cystic.

Perfusion MR imaging.—Dynamic contrast-enhanced single-shot spin-echo echo-planar images were acquired (after administration of a 0.1-mmol/kg loading dose of a gadolinium-based agent to reduce the effect of contrast material leakage on rCBV measurements) during the first pass after a rapid injection of a gadolinium-based MR imaging contrast agent (40 mL over 10 seconds) (Magnevist; Schering). Seven sections were obtained through the lesion (2000/75; field of view, 240 mm; section thickness, 5–10 mm; number of signals acquired, one). Data processing was performed by using standard software on a workstation (FuncTool, version 2; GE Medical Systems). After construction of cerebral blood volume maps (approximated by using the negative enhancement integral), five to 10 ROIs over bright aspects of the lesion were recorded and compared with ROIs over white matter (R.N.A.). Each ROI was at least 20 mm2 in size, and its upper limit was determined by the size of the targeted homogeneous area of the lesion, regardless of whether that part was solid or cystic. The highest rCBV was recorded.

MR spectroscopy.—The Cho/NAA ratio was measured in all nonenhancing lesions with available single-voxel point-resolved spatially localized proton MR spectroscopy (1500/144; number of signals acquired, eight; field of view, 240 mm; voxel size, 15 x 15 x 15 mm). In patients with enhancing lesions with findings of facilitated diffusion and elevated perfusion, Cho/NAA ratios were measured in mass surroundings if multivoxel two-dimensional chemical-shift imaging proton MR spectroscopy had been performed (1000/144; field of view, 240 mm; section thickness, 5 mm) with a matrix of 18 x 18 on the GE system and a matrix of 16 x 16 on the Siemens system. The highest Cho/NAA ratio was recorded. All measurements were obtained by using the peak heights in which the distances from the spectral baseline (defined by drawing a straight horizontal line along the horizontal flat segment of the spectrum as it approaches 0 ppm), and the peaks (Cho peak at 3.2 ppm and NAA peak at 2.02 ppm) of interest were measured and divided (R.N.A.).

Statistical Analysis
We (J.K., R.N.A.) calculated the mean and standard deviation, median, and minimum and maximum values of the ADC, rCBV, and perienhancement Cho/NAA ratio by using software (Excel 2000; Microsoft, Redmond, Wash). Differences between two groups divided at node 4, as well as between two groups divided at node 5, were evaluated with a two-sided unpaired t test with help of a Web-based calculator at http://faculty.vassar.edu/lowry/VassarStats.html. A P value of less than .05 was considered to indicate a significant difference.

We determined the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value with corresponding 95% confidence intervals of the strategy at each node on the basis of 2 x 2 tables. The 95% confidence interval (CI) was calculated according to the following formula:

Formula
where p is an accuracy measure and n is the sample size. In the calculations, we did not include entities that are represented on both sides of the node that would add noise to the calculations. That is, in question 1, high-grade primary neoplasms and TDLs were excluded from analysis of this node because these lesions are not a target group for the question. In question 2, we excluded high-grade primary neoplasms, abscesses, and metastases. In question 7, low-grade primary neoplasms were excluded.

We also calculated the accuracy, defined here as the number of correctly classified "true" cases divided by the number of all cases, in (a) discrimination of neoplastic from nonneoplastic diseases, in which a true-positive finding is defined as a neoplasm classified as a neoplasm, a true-negative finding is defined as a nonneoplasm classified as a nonneoplasm, a false-positive finding is defined as a nonneoplasm classified as a neoplasm, and a false-negative finding is defined as a neoplasm classified as a nonneoplasm; (b) discrimination of high-grade from low-grade neoplasms, in which a true-positive finding is defined as a high-grade neoplasm classified as a high-grade neoplasm, a true-negative finding is defined as a low-grade neoplasm classified as a low-grade neoplasm, a false-positive finding is defined as a low-grade neoplasm classified as a high-grade neoplasm, and a false-negative finding is defined as a high-grade neoplasm classified as a low-grade neoplasm; (c) discrimination of high-grade neoplasms and lymphoma from low-grade neoplasms and nonneoplastic diseases, in which a true-positive finding is defined as a high-grade neoplasm or lymphoma classified as a high-grade neoplasm or lymphoma, a true-negative finding is defined as a low-grade neoplasm or nonneoplastic disease classified as a low-grade neoplasm or nonneoplastic disease, a false-positive finding is defined as a low-grade neoplasm or nonneoplastic disease classified as a high-grade neoplasm or lymphoma, and a false-negative finding is defined as a high-grade neoplasm or lymphoma classified as a low-grade neoplasm or nonneoplastic disease.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 ADVANCES IN KNOWLEDGE
 References
 
Results of Picture Archiving and Communication System Database Search
The estimation of accuracy of the MR imaging strategy is based on data from only 40 patients (25 women, 15 men; mean age, 45 years; range, 20–86 years) with results from the required studies to complete the strategy (Fig 2). The remaining patients lacked results from one or more studies required to follow the strategy paths, because some patients underwent conventional and advanced studies at multiple institutions before referral to our hospital and others did not undergo all advanced techniques. Those 40 patients had 13 high-grade neoplasms, 12 lymphomas, six TDLs, six low-grade neoplasms, two abscesses, and one diagnosis of encephalitis. All had histologic findings except five patients with TDLs.


Figure 2
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  		Figure 2: Strategy and flow diagram proposed by Al-Okaili et al (61) and modified on basis of present study data. Diagram of participants consists of several nodes, or questions. It is validated by using data from our patients with common intraaxial masses. Patient data are listed and color coded on the top to determine discriminative accuracy. Numbers within color-coded boxes indicate the number of patients with that particular lesion. Data from 111 patients start at the top, but data from only 40 patients continue to the bottom because some patients did not undergo one or more of the pertinent studies. 1.1/100mm2/ADC = 1.1 x 10–3 mm2/sec, Ch = choline, High GN = high-grade primary brain neoplasms, Low GN = low-grade primary brain neoplasms, >Peri-lesion Ch/NAA = Cho/NAA ratio around lesion. (Adapted and reprinted, with permission, from reference 61.)

 
Performance of Strategy
The accuracy of the strategy in discrimination of intraaxial brain masses varied from 85% to 90%, depending on the specific diagnostic question (Table 1). The highest accuracy (90%) and sensitivity (97%) were found in discrimination of neoplastic from nonneoplastic brain lesions. High-grade brain neoplasms can be discriminated from low-grade brain neoplasms with 90% accuracy and 88% sensitivity. High-grade neoplasms can be accurately differentiated from lymphomas (accuracy, 90%; sensitivity, 80%), whereas differentiation of high-grade neoplasms and lymphomas from low-grade neoplasms and nonneoplastic diseases is slightly less accurate (Table 1).


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  		Table 1. Measures of Accuracy of MR Imaging–based Strategy in Discrimination of Types of Intraaxial Brain Lesions

 
With our strategy, we incorrectly classified five enhancing lesions. One lymphoma was diagnosed as an abscess because of necrosis and low rCBV; an abscess was classified as a high-grade neoplasm because of elevated rCBV; a TDL was classified as lymphoma because of nonfacilitated diffusion; and two high-grade neoplasms were classified as lymphomas because of nonfacilitated diffusion and absence of necrosis. With our strategy, we incorrectly classified three of 12 nonenhancing lesions. One high-grade oligodendroglioma was labeled as a low-grade neoplasm because of low rCBV, and two high-grade primary neoplasms were classified as low grade because they had Cho/NAA ratios of less than 2.2.

Performance of Individual Strategy Components
Question 1.—We found that contrast enhancement is highly accurate (91%) in the discrimination of low-grade tumors and encephalitis from abscesses and lymphomas (Table 2).


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  		Table 2. Measures of Accuracy in Discrimination of Intraaxial Brain Masses, according to Specific Strategy Questions

 
Question 2.—The ADC threshold of 1.1 x 10–3 mm2/sec was found to be accurate (91%) in discrimination of lymphomas from TDLs, because only two patients with TDLs were classified incorrectly (Tables 2, 3; Figs 3, 4).


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  		Table 3. Descriptive Statistics of ADC Obtained in 111 Patients with Various Intraaxial Brain Lesions

 

Figure 3
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  		Figure 3: Transverse MR images of right frontal region lymphoma in 69-year-old woman. With proposed strategy, this would be lymphoma because of enhancement, restricted diffusion, and absence of necrosis. A, Fluid-attenuated inversion-recovery (10 000/120; inversion time, 2200 msec; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, two) and, B, contrast-enhanced T1-weighted spin-echo (600/11; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, one) transverse images demonstrate enhancing mass (*) with hyperintense surrounding edema (arrowheads). C, ADC map and, D, diffusion-weighted image (10000/118; b values, 0 and 1000 sec/mm2) show restricted diffusion where lowest ADC obtained was 0.86 x 10–3 mm2/sec.

 

Figure 4
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  		Figure 4: Transverse MR images of left frontal region pathologically proved TDL in 40-year-old woman. Lesion was correctly classified because of enhancement and facilitated diffusion but no substantial elevation of perfusion. A, Fluid-attenuated inversion-recovery (10 000/120; inversion time, 2200 msec; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, two) and, B, contrast-enhanced T1-weighted spin-echo (600/11; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, one) transverse images demonstrate enhancing mass (*). C, Diffusion-weighted image and, D, ADC map (10 000/118; b values, 0 and 1000 sec/mm2) show facilitated diffusion where the lowest ADC obtained was 1.67 x 10–3 mm2/sec. E, Perfusion image (2000/75; field of view, 240 mm; section thickness, 5 mm; number of signals acquired, one) through mass (highest measured rCBV value = 1.57).

 
Question 3.—We found rim enhancement to help in separation of lymphomas from abscesses and high-grade neoplasms (Table 2, Fig 5). Only three lesions were misclassified: Two anaplastic astrocytomas were labeled as lymphomas, and one lymphoma was labeled as an abscess.


Figure 5
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  		Figure 5: Transverse MR images of right frontal region glioblastoma multiforme in 69-year-old man. Lesion was correctly classified as high-grade primary tumor because of enhancement, restricted diffusion, necrosis, and elevated perfusion. A, Fluid-attenuated inversion-recovery (10 000/120; inversion time, 2200 msec; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, two) and, B, contrast-enhanced T1-weighted spin-echo (600/11; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, one) transverse images demonstrate enhancement (*) with hyperintense surrounding edema (arrowheads) and necrotic component (arrow). C, Diffusion-weighted image and, D, ADC map (10 000/118; b values, 0 and 1000 sec/mm2) show restricted diffusion where the lowest ADC obtained was 0.88 x 10–3 mm2/sec. E, Perfusion image (2000/75; field of view, 240 mm; section thickness, 5 mm; number of signals acquired, one) shows regions of elevated rCBV with measured ratios above 2.97.

 
Question 4.—On the basis of 14 patients who had perfusion MR imaging data available for analysis, rCBV was 100% accurate in discrimination of TDLs and abscesses from high-grade neoplasms and metastases (Table 2). Mean rCBV values were significantly different between the two groups (P < .001) (Table 4; Figs 4, 6).


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  		Table 4. Descriptive Statistics of rCBV in Patients with Intraaxial Brain Lesions

 

Figure 6
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  		Figure 6: Transverse MR images of right frontal region anaplastic astrocytoma in 74-year-old man. Lesion is high-grade primary neoplasm because of enhancement, facilitated diffusion, elevated perfusion, and evidence of infiltration. A, Fluid-attenuated inversion-recovery (10 000/120; inversion time, 2200 msec; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, two) and, B, postcontrast T1-weighted spin-echo (600/11; field of view, 220 mm; matrix, 256 x 256; section thickness, 5 mm; intersection gap, 0 mm; number of signals acquired, one) transverse images demonstrate enhancing mass (*) with hyperintense surrounding edema (arrowheads). C, Diffusion-weighted image and D, ADC map (10 000/118; b values, 0 and 1000 sec/mm2) show facilitated diffusion where lowest ADC obtained was 1.4 x 10–3 mm2/sec. E, Perfusion image shows elevated rCBV of 2.65 (2000/75; field of view, 240 mm; section thickness, 5 mm; number of signals acquired, one). F, G, Multivoxel spectroscopic (1000/144; field of view, 240 mm; section thickness, 5 mm) images show evidence of infiltration. Boxes 15 and 16, which are beyond the enhancing lesion portion, show Cho/NAA ratio of more than 1.

 
Question 5.—Use of measured Cho/NAA ratios outside enhancing tissue was 82% accurate in separation of primary high-grade neoplasms from metastases (Table 2), despite insignificant differences in mean Cho/NAA ratios between the two groups (P = .372) (Table 5). Only five of 27 lesions were misclassified: Four of 24 primary high-grade neoplasms were labeled as metastases, and only one of three metastatic lesions was labeled as a primary high-grade neoplasm.


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  		Table 5. Descriptive Statistics of Perienhancement Cho/NAA Ratios at Multivoxel MR Spectroscopy in Patients with Primary High-Grade and Secondary Brain Neoplasms

 
Question 6.—In discrimination of abscesses from neoplasms, rCBV was moderately accurate (60%) (Tables 2, 4). However, the analysis was based on five patients who had available perfusion MR imaging data (Fig 5). One abscess with high rCBV and one necrotic lymphoma mass with low rCBV were incorrectly classified.

Question 7.—The accuracy of the Cho/NAA ratio in discrimination of primary high-grade neoplasms from nonneoplastic diseases was moderate (75%) (Table 2), but only eight patients were available for analysis. Two primary high-grade neoplasms were misclassified because of a Cho/NAA ratio of less than 2.2.

Question 8.—Only three patients had perfusion MR imaging data available for analysis. Measured rCBV accuracy was moderate (67%) in differentiation of high-grade from low-grade neoplasms.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
ADVANCES IN KNOWLEDGE
 References
 
Evaluation of our proposed imaging strategy by using data from our center suggests that the strategy has high accuracy in differentiation of intraaxial masses. Prospective evaluation in other centers is warranted, however, before the strategy is recommended for routine use in clinical practice (62).

Most published studies provide accuracy of a single imaging technique in narrowing the differential list, while only few studies (1,8,9,14) have results that suggest combining tests can provide more reliable differentiation. Any proposed strategy requires validation based on a large and heterogeneous group of patients (63). During a 5-year period, only 40 patients evaluated in our institution had undergone all studies necessary to complete the entire strategy despite the fact that our institution is a primary and tertiary center in a densely populated metropolitan area. This is largely because some patients underwent conventional and advanced studies at multiple institutions and some patients did not undergo all advanced imaging techniques. Additionally, some techniques may fail during acquisition. If we had more patients who had undergone all studies, more sophisticated statistical analyses, such as multiple logistic regression or a classification and regression tree (54), could have been performed to assign weights to particular nodes of the strategy and modify the strategy. Despite this limitation, the strategy increases the diagnostic specificity markedly and will likely prove more accurate when combined with clinical data. Furthermore, the strategy can be beneficial even if the full complement of data from studies is not available to apply the strategy in its entirety, because each step reduces or increases the likelihood of correct diagnosis of certain entities.

Accuracy of the strategy in distinguishing neoplastic from nonneoplastic lesions appears to be better than reported accuracies of single MR classification tools. Straightforward comparison with conventional MR imaging alone (64,65) is difficult because we included only patients with intraaxial brain masses, while few other studies (64,65) attempted to determine MR imaging accuracies for all intracranial lesions. Furthermore, differences were not limited to population variations in previous studies but also included differences in study design, which makes comparison of conventional MR imaging to our strategy difficult. Sorby (64) reported 80% accuracy of MR imaging in depicting all brain lesions and 93% accuracy in depicting neoplasms; however, those numbers could be superficially inflated because radiologists had access to all clinical information and other imaging results before MR interpretation. Orrison et al (65) reported 85%–88% accuracy with blind interpretation of normal and abnormal findings at MR studies, depending on the magnet field strength, but the number of confounding nonneoplastic intraaxial masses such as abscesses was small. Interestingly, all abscesses in that study were incorrectly classified.

Performance of our strategy seems to be better than that of the Cho/NAA ratio in the study of Butzen et al (66), who reported a sensitivity of 79% and specificity of 77%. Rand et al (67) reported high accuracy (83%) of blinded MR spectroscopic interpretation, but that was still lower than that of our strategy (90%). Thus, the strategy can substantially advance differentiation of intraaxial masses because the accuracy appears to be better than that in previous reports (66,67) and the population of study patients consisted of patients with lesions that are notoriously difficult to discriminate.

The strategy worked better than any single method in distinguishing high-grade from low-grade neoplasms. Reported sensitivity of conventional MR imaging varies from 55% to 83%, which is lower than the sensitivity of our strategy (88%) (7,13,14,18,68,69). Performance of perfusion MR imaging alone also appeared to be lower than the performance of our strategy (95% sensitivity and 57.5% specificity vs 88% and 100%, respectively, in our study) (14). Law et al (14) used a combination of parameters, such as rCBV, Cho/creatine ratio, and Cho/NAA ratio, to obtain good differentiation of gliomas (93.3% sensitivity and 60% specificity), but differentiation was still lower than that with our strategy.

We encountered challenges in comparing our results with those published by other researchers in the use of combined imaging techniques to improve neoplasm classification (8,9,14). Tsui et al (8) suggested that combining perfusion MR imaging and diffusion-weighted MR imaging results can help in discrimination of a TDL from a mimicking cystic neoplasm or cerebral abscess, but this study is a single case report. Chan et al (9) indicated that ADC and perfusion MR imaging findings are useful in distinguishing abscesses from infected neoplasms; however, no accuracy was provided. Similarly, no accuracy was provided by Bulakbasi et al (1), who suggest usefulness of combining ADC and MR spectroscopic information for grading neoplasms. Though results of some studies suggest benefits of an integrated approach, the experience in such advanced MR imaging combinations to grade intraaxial neoplasms remains limited (1,8,9,14).

Discrimination of high-grade neoplasms and lymphomas from low-grade neoplasms and other nonneoplastic conditions is important because patients with low-grade neoplasms or mimicking conditions are more suited for less aggressive treatment and interval surveillance. Discrimination is difficult, however, because lymphoma can be mistaken for many types of brain lesions. The strategy helped with this clinical context, though the number of our patients with lymphoma was small. To our knowledge, our study is the first to propose an effective strategy to separate these two groups of lesions.

The retrospective nature of the study and the low number of nonenhancing lesions were limitations. Another limitation was that the optimal configuration of all specific nodes of the strategy may be different from that proposed in this strategy, but this needs to be verified in future studies with a larger group of patients. For instance, measurements of rCBV might work better than contrast enhancement in the first node, but perfusion MR imaging is not usually performed first in clinical practice. Nevertheless, validation of each specific question of the strategy suggests that performance of a single node is at least similar to that reported by other researchers. In addition, caution needs to be exercised with respect to the threshold values used in several nodes because they may need to be redefined for magnets with different field strengths.

Another limitation was that the strategy includes common lesion categories and may not work well for rare entities. Cerebrovascular accidents were not included in the strategy because their manifestation usually leaves little room for clinical uncertainty. Multivoxel MR spectroscopy, which offers wide coverage at the expense of a lower signal-to-noise ratio, and single-voxel MR spectroscopy, which offers greater signal-to-noise ratio at the expense of limited coverage, are used for different objectives in the strategy. The use of the Cho/NAA ratio alone at nodes 5 and 7 might be regarded as a limitation, but other commonly used ratios are not fully independent of the Cho/NAA ratio. Furthermore, we intended to keep the strategy as simple and practical as possible.

Our initial results suggest that intraaxial brain masses can be differentiated with 85%–90% accuracy by using the proposed MR imaging–based diagnostic strategy.


   ADVANCES IN KNOWLEDGE
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
References
 


   ACKNOWLEDGMENTS
 
We thank Christian T. Muller, MD, for his critical comments and help in manuscript preparation.


   FOOTNOTES
 

Abbreviations: ADC = apparent diffusion coefficient • Cho = choline • NAA = N-acetylaspartate • rCBV = relative cerebral blood volume • ROI = region of interest • TDL = tumefactive demyelinating lesion

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, R.N.A., D.M.O., H.P., E.R.M.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, R.N.A., D.M.O., H.P.; clinical studies, R.N.A., R.L.W., D.M.O., H.P., K.D.J., E.R.M.; statistical analysis, R.N.A., J.K., H.P.; and manuscript editing, R.N.A., J.K., J.H.W., R.L.W., D.M.O., H.P., E.R.M.


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 DISCUSSION
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