|Year : 2019 | Volume
| Issue : 3 | Page : 162-169
Perfusion-weighted imaging in differentiating ring-enhancing lesions in brain
G P Venkat Choudary1, Amancharla Yadagiri Lakshmi1, Vijaya Lakshmi Devi Bodagala1, V V Ramesh Chandra2, Naveen Thota3, Amit Kumar Chowhan4
1 Department of Radiodiagnosis, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
2 Department of Neurosurgery, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
3 Department of Neurology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
4 Department of Pathology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
|Date of Submission||20-May-2019|
|Date of Acceptance||13-Aug-2019|
|Date of Web Publication||17-Oct-2019|
Dr. Amancharla Yadagiri Lakshmi
Department of Radiodiagnosis, Sri Venkateswara Institute of Medical Sciences, Tirupati - 517 507, Andhra Pradesh
Source of Support: None, Conflict of Interest: None
Context: Ring-enhancing lesions in the brain always raise questions among radiologists and given the many possible differential diagnosis, it may sometimes be difficult to reach a diagnosis with conventional magnetic resonance imaging (MRI). The introduction of advanced imaging techniques, such as perfusion- and diffusion-weighted imaging, has contributed to the differentiation.
Aim: The aim of the study was to differentiate various ring-enhancing lesions in the brain using perfusion-weighted imaging (PWI) on the basis of relative cerebral blood volume (rCBV).
Materials and Methods: This prospective study was conducted on 39 consecutive patients, from rural and suburban areas of Tirupati, in the time frame of 14 months and included neoplastic (24 cases) and infectious (15 cases) lesions showing ring-shaped contrast enhancement on conventional MRI. PWI was performed and the rCBV values were obtained. The final diagnosis was made by histopathology of surgical specimen (in operated cases) and on response to treatment in the form of symptom improvement and lesion clearance on follow-up imaging (computed tomography or MRI) done at 1–6 months after appropriate therapy in unoperated cases.
Results: Neoplastic lesions had higher rCBV values (5.04 ± 1.86) than infectious lesions (0.90 ± 0.43) (P < 0.001). When using an rCBV value, 1.95 as the parameter to define neoplastic lesions, the sensitivity of the method was 95.8% and the specificity was 93.3%, with a positive predictive value of 96%, a negative predictive value of 94%, and an accuracy of 97.4%.
Statistical Analysis: We compared the rCBV values between the two groups using Student's t-test. We used receiver operating characteristic curve analysis to assess the performance of the diagnostic test.
Conclusion: PWI is an efficient advanced imaging technique in ring-enhancing brain lesions and is a useful complementary tool to routine structural MRI in distinguishing between infectious and neoplastic brain lesions.
Keywords: Infectious lesions, metastases, neoplasms, perfusion-weighted imaging, relative cerebral blood volume, ring-enhancing lesions
|How to cite this article:|
Choudary G P, Lakshmi AY, Bodagala VL, Chandra V V, Thota N, Chowhan AK. Perfusion-weighted imaging in differentiating ring-enhancing lesions in brain. J NTR Univ Health Sci 2019;8:162-9
|How to cite this URL:|
Choudary G P, Lakshmi AY, Bodagala VL, Chandra V V, Thota N, Chowhan AK. Perfusion-weighted imaging in differentiating ring-enhancing lesions in brain. J NTR Univ Health Sci [serial online] 2019 [cited 2020 Sep 28];8:162-9. Available from: http://www.jdrntruhs.org/text.asp?2019/8/3/162/269489
| Introduction|| |
Ring-enhancing lesions in the brain always raise questions among radiologists and given the many possible differential diagnosis, it may sometimes be difficult to reach a diagnosis with conventional imaging. Differentiating between infectious and neoplastic focal brain lesions that are detected by imaging examinations is important and that information is usually expected from radiologists.
Conditions such as abscesses and granulomas can mimic brain neoplasms on neuroimaging, while some brain neoplasms may be not associated with typical tumefactive lesions.
Conventional structural magnetic resonance imaging (MRI) is an established and useful tool in the diagnosis and evaluation of brain tumors; however, it does not provide information on tumoral vascularity, metabolism, and cellularity. Several types of non-neoplastic brain lesions can be potentially misdiagnosed as brain tumors.
Thus, there is a need for additional imaging modalities such as diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI), which may aid in improving the diagnosis of unknown brain lesions.
The purpose of this study is to differentiate various ring-enhancing lesions using relative cerebral blood volume (rCBV) on the basis of differences in vascularity on 1.5 T MRI.
| Materials and Methods|| |
The study was conducted as a prospective study in the Department of Radiodiagnosis, in our institute, a tertiary care teaching hospital in South India, in the time frame of March 2016 to June 2017 after obtaining clearance from the Institutional Ethics Committee.
The patients were referred to the radiology department for MRI with clinical suspicion of intracranial space-occupying lesion and who on postcontrast imaging found to have ring-like enhancement were included in the study.
A total of 50 cases were enrolled for the study.
Eleven cases were excluded from the study due to various reasons such as lost to follow-up in five cases (three cases of suspected tuberculomas, one suspected case of neurocysticercosis, and one case of suspected pyogenic abscess), two cases of tuberculomas in whom prior Anti-tubercular therapy (ATT) was given, one patient with suspected neoplastic lesion did not undergo surgery, two cases of gliomas in whom poor quality perfusion maps were generated, and one case of metastases in whom radiotherapy was given prior to MRI.
All the MRI studies were done using Siemens MAGNETOM Aera machine (1.5 T). All the patients were asked to get rid of any metallic objects as well as they are asked about any contraindication to MRI examination (artificial heart valve, cardiac pacemaker, metallic stents, or joint prosthesis except those made of titanium). The patients were informed about the duration of the examination, the position of patient, and the importance of being motionless.
MRI study was done with the patients in supine position using the standard head coil. The examination was done before contrast administration, a scout sagittal T1-weighted view was obtained to verify the precise position of the patient and to act as a localizer for subsequent slices, and then multiple pulse sequences were used to obtain axial images followed by coronal and/or sagittal images based on the location of the pathology encountered.
All the cases were examined using the following protocol: sagittal T1-WI as a localizer (TE = 10–12 ms, TR = 400–600 ms), axial and sagittal spin-echo sequences, short TR/TE (T1-weighted images): (TE (time to echo) = 10–12 ms, TR (Repetition time) = 400–600 ms), axial, coronal fast spin-echo, long TR/TE (T2-weighted imaging): (TE = 70–90 ms, TR = 2800–3500 ms), postcontrast axial, sagittal, and coronal spin-echo sequences, short TR/TE (T1-weighted imaging): TE = 10–12, ms, TR = 400–600 ms; field of view (FOV) =24–18 cm in axial images and 30–22 cm in coronal images; matrix size 192 × 160; slice thickness = 6 mm with 2 mm interval (in all sequences). DWI was acquired in the axial plane using single-shot echoplanar spin-echo sequence using the following parameters: TR = 6300 ms, TE = 80–100 ms, FOV = 32–40 cm, matrix size = 230 × 230, slice thickness = 5 mm, number of excitations (NEX) =3; diffusion gradient encoding in three orthogonal planes at b = 0 and b = 1000 s/mm2.
PWI study was performed with a T2-weighted echoplanar spin-echo sequence (TR = 1680, TE = 30, matrix = 128 × 128, slice thickness = 5 mm) with a duration of 84 s. After the conventional MRI, we started the scan and injected the contrast at the eighth measurement. The scan has 50 time points (measurements) of ~2 s each resulting in total time just below 2 min. A normal contrast dose of 0.1 mmol/kg (Gadodiamide, OMNISCAN) is used and injected with an automatic pressure injector at a flow velocity of 5 mL/s through an 18- to 20-gauge needle cannula.
Color maps of the cerebral blood volume were generated, and the mean of the maximum CBV was obtained by placing a region of interest in the peripheral solid areas showing a color at the upper end of the color scale. Data are then compared with those of the normal-appearing contralateral white matter. The area under the corrected contrast agent concentration–time curve is proportional to the CBV and does not yield an absolute value. It is therefore necessary to express CBV relative to that of a standard reference area, usually the contralateral white matter. We refer this as rCBV which is expressed as a ratio [ratio = CBV (lesion)/CBV (contralateral white matter)].
Statistical analysis was performed using Microsoft Excel version 16 (Microsoft Corporation, USA). Data were described in terms of range, mean and standard deviation (±SD), frequencies (number of cases), and relative frequencies (%). We compared the rCBV values between the two groups (neoplastic versus infectious) using Student's t-test. The rCBV values are presented on the basis of the averages and SDs. We also calculated the cutoff point of the rCBV values for PWI that would allow differentiation of neoplastic and infectious lesions. We used receiver operating characteristic (ROC) curve analysis to assess the performance of a simple diagnostic test designed to correctly classify a lesion as neoplastic rather than infectious using a given rCBV value in relation to the defined cutoff. We then calculated sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of this diagnostic test when using the retrieved cutoff rCBV value (all values are presented as %). A P value < 0.05 was considered statistically significant.
| Results|| |
This study included 39 patients (23 male and 16 female) (age range 6–68 years, mean age 41 years) with evidence of ring enhancement in postcontrast T1WI, and they were classified into two groups based on the their final diagnosis: the infectious group included 15 patients (7 male and 8 female) (age range 6–54 years, mean age 32 years) and the neoplastic group included 24 patients (16 male and 8 female) (age range 9–68 years, mean age 48 years). The mean duration of follow-up in the unoperated cases (n = 21) was 55 days.
In the neoplastic group, 10 cases were metastases (6 male and 4 female) (age range 34–68 years, mean age 52 years) and 14 cases were primary neoplasms (10 male and 4 female) (age range 9–63 years, mean age 44 years). Infectious lesions (n = 15) include pyogenic abscess (3 patients), tubercular abscess (2 patients), neurocysticercosis (4 patients), tuberculomas (5 cases), and toxoplasmosis (1 patient). The primary tumors (n = 14) were glioblastomas (7 patients), low-grade astrocytoma (1 patient), Dysembryoplastic neuroepithelial tumor (DNET) (1 patient), anaplastic oligodendroglioma (2 patients), and oligodendrogliomas (3 patients). In metastases group (n = 10), the most common primary malignancy causing brain metastases in our study is carcinoma lung (6 cases), followed by carcinoma breast (2 cases), carcinoma ovary, and carcinoma gastroesophageal junction (1 case each).
In the PWI study, the mean rCBV in the capsular portion of infectious lesions was 0.90 ± 0.43 (range 0.39–2.13). The mean rCBV for neoplastic lesions was 5.04 ± 1.86. In the neoplastic lesions, the rCBV for primary neoplasms was 5.43 ± 1.95 (range 2.70–8.75), and for metastases it was 4.50 ± 1.66 (range 0.97–6.58).
Univariate analysis of rCBV values for each group of lesions and specifically for the two subsets within the neoplastic group is summarized in [Table 1] (ranges, means, and SDs).
|Table 1: Ranges, Means, And Standard Deviations Of Rcbv Values In Various Groups|
Click here to view
The analysis of the rCBV values using unpaired t-test (two-tailed Student's t-test) indicates a significant difference between neoplastic and infectious lesions, that is, neoplastic lesions had higher rCBV values than infectious lesions (P < 0.001) [Table 2].
|Table 2: Analysis Of The rCBV Values Using Unpaired T-Test Between Infectious And Neoplastic Groups|
Click here to view
No significant difference was found regarding rCBV values when comparing primary and metastatic lesions within the neoplastic group (P = 0.112) [Table 3].
|Table 3: Analysis Of The Rcbv Values Using Unpaired T-Test Between Primary Neoplasms And Metastases Groups|
Click here to view
The mean rCBV values according to each grade of primary neoplasm are detailed in [Table 4], showing higher rCBV values for Grade 4 gliomas compared with grade 1 tumors.
Evaluation of discriminatory capability of the PWI method through ROC analysis defined the rCBV value that better allowed a correct differentiation of neoplastic from infectious lesions, retrieving a threshold of 1.95. The ROC curve representing the discriminatory capability of an rCBV value of >1.95 to correctly classify a lesion as neoplastic is shown in [Graph 1].
[Table 5] demonstrates the corresponding sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of this diagnostic test. The discriminant function analysis misclassified one case of neoplastic lesion as being infectious because its rCBV value was <1.95, and one case of infectious lesion as being neoplastic because its value was >1.95. [Figure 1], [Figure 2], [Figure 3] demonstrates classical lesions and their characteristics in the three groups (infectious, primary neoplasm and metastases).
|Table 5: Diagnostic Performance Of rCBV For The Diagnosis Of Neoplastic Lesions With A Cutoff Point Of 1.95|
Click here to view
|Figure 1: Axial MR images in a 6 year male child showing lesion in left frontal lobe with hypointense centre on T1 (a) hyperintense on T2 (b), suppressed on FLAIR (c) restricted diffusion on DWI (d) and ADC (e) and ring enhancement on post contrast T1WI. CBV values were calculated from the peripheral portion as shown (g). (rCBV = 0.77). Histopathologically it was found to be a pyogenic abscess|
Click here to view
|Figure 2: 50 year male patient with histopathologically proven glioblastoma in left temporal lobe. Axial MR images shows iso to hyperintense centre on T1 (a), heterogeneously hyperintense on T2 (b), not suppressed on FLAIR (c) mixed signal intensity on DWI (d) and ADC (e) and post contrast ring enhancement. CBV value is calculated from the peripheral portion as shown (g). (rCBV = 7.04). Ill-defined perilesional edema noted|
Click here to view
|Figure 3: 68 year male patient with multiple ring enhancing lesions in bilateral cerebral hemispheres with pathologically proven primary lung malignancy. Axial MR images showing lesion in right frontal lobe with hypointense centre on T1 (a) hyperintense on T2 (b), suppressed on FLAIR (c) and showing facilitated diffusion on DWI (d) and ADC (e) and ring enhancement on post contrast T1WI. CBV value is calculated from the peripheral portion as shown (g). (rCBV = 5.37)|
Click here to view
| Discussion|| |
It is difficult to differentiate necrotic gliomas, cystic metastases, and abscesses with conventional MRI. All can appear as expansile rim-enhancing masses with prominent perilesional edema.
In this study, we present a prospective analysis of rCBV data in 39 patients with ring-enhancing lesion (15 patients with infectious and 24 cases with neoplastic lesions).
Our results demonstrated statistically significant lower rCBV values in brain infectious lesions than in neoplastic lesions and a good ability of PWI to successfully distinguish between these two conditions, with elevated sensitivity, specificity, positive and negative predictive values, and accuracy value.
Our study is in accordance with a study done by Hakyemez et al., in which a total of 105 patients with varied brain mass lesions were studied (only four cases of infectious lesions, i.e. pyogenic abscesses). They found the mean rCBV values of 5.76 ± 3.35 in high-grade gliomas (n = 26), 5.27 ± 3.22 in metastatic lesions (n = 25), 1.69 ± 0.51 in low-grade gliomas (n = 11), and 0.76 ± 0.12 in pyogenic abscesses; high-grade gliomas and metastases could be successfully differentiated from abscesses on the basis of PWI (P < 0.0001) according to this study.
Haris et al., in a study comprising 103 patients (77 with neoplastic lesions and 26 patients with infectious lesions), found the mean rCBV values of 3.66 ± 0.58 in patients with infectious lesions, which were higher than the values obtained in our study (0.90 ± 0.43). The reason for this difference in rCBV values might be due to more number of tuberculomas (n = 18) in their study compared with our study (n = 5) and due to the different methodology as well as the technique. However, the rCBV values of high-grade gliomas (5.78 ± 1.11) are similar to that found in our study (6.0 ± 1.45).
Hourani et al., although not specifically studying infectious lesions, compared patients with neoplastic brain lesions (n = 36) and non-neoplastic brain lesions (n = 33, including stroke, demyelinating disease, multiple sclerosis, and acute disseminated encephalomyelitis, among others); these authors found higher mean rCBV values in neoplastic lesions (4.11 ± 3.14) in comparison to non-neoplastic lesions (1.00 ± 0.39); however, low-grade tumors (1.5 ± 1.2) could not be differentiated from non-neoplastic lesions based on PWI (P = 0.73).
In this study, a threshold rCBV value of 1.5 was suggested for differentiating between neoplastic and non-neoplastic lesions, with a sensitivity, specificity, positive predictive value, and negative predictive value of 77.8%, 91.7%, 93.3%, and 91.7%, respectively.
This suggested cutoff value, which is slightly lower than that found in our own study (1.95), and the distinct mean rCBV values found in several different studies may be more useful as qualitative indicators of trends and should not be taken into an absolute or definitive numeric perspective. The numeric variations may have been affected by multiple factors, including those inherent to the sample (size, heterogeneity, and variability of included lesions), subjects (age, immunological status), lesions (type, biological behavior, morphology, dimensions, degree of angiogenesis), scanning protocols, perfusion technique (T1, gradient-echo or spin-echo sequences, arterial spin labeling), administration or not of the preload of paramagnetic contrast agent, and imaging processing (used software), among others.
Several studies have demonstrated a direct correlation between the mean rCBV values and histological grading of gliomas, with high-grade gliomas showing higher rCBV values than low-grade gliomas and non-neoplastic lesions,,, which are in accordance with our study.
Differentiating between high-grade gliomas and solitary metastasis may require analysis of peritumoral areas. Hyperintense peritumoral regions on T2-W images may represent vasogenic edema secondary to increased capillary endothelium permeability in metastatic tumors but could also be the result of tumoral infiltration in primary gliomas. The mean rCBV values of metastatic lesions in our study were based only on the analyses of solid peripheral areas without inclusion of perilesional regions; thus, we did not find statistically significant differences between these two conditions.
Comparison of rCBV values, cutoff values, and diagnostic performance of PWI in our study with other studies in literature showed similar trends as shown in [Table 6] and [Table 7].
|Table 6: Comparison Of Rcbv Values Of Our Study With Other Studies In Literature|
Click here to view
|Table 7: Comparison Of Cutoff Values And Diagnostic Performance Of Pwi In Our Study With Other Studies In Literature|
Click here to view
However, in this study, we found differences in the perfusion characteristics between tuberculous and metastatic lesions. The mean rCBV ratio calculated in cases of tuberculous lesions was 1.04 ± 0.52, while that of metastases was 4.50 ± 1.66. The mean rCBV for metastases was similar to that reported in the literature, although the rCBV ratio for tubercular lesions was lower than those reported by Batra and Tripathi and Haris et al. The difference might be due to the different methodology as well as the technique.
| Conclusion|| |
In conclusion, perfusion-weighted MRI is an advanced MR technique that is used to add important physiological information to that obtained with conventional MRI. This study demonstrates that PWI (rCBV values of peripheral regions) can be used to demonstrate differences in neoplastic and non-neoplastic lesions. On PWI, the presence of high rCBV values in the peripheral portion of the ring-enhancing lesion is strongly suggestive of a neoplasm and presence of low rCBV values is highly suggestive of an infectious etiology. For these reasons, PWI and calculation of rCBV values should be performed in all cases of ring-enhancing lesions.
| Limitations|| |
The sample size was less in our study, and further validation is required with higher samples.
The mean rCBV values of metastatic lesions in our study were based only on the analyses of solid peripheral areas without inclusion of perilesional regions; thus, we did not find statistically significant differences between these two conditions. Differentiating between high-grade gliomas and metastasis may require analysis of peritumoral areas.
Histopathological proof was not available in some cases. However, follow-up imaging was done in all these cases after appropriate therapy (medical management for infectious and chemo- or radiotherapy for metastatic lesions).
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Omuro AM, Leite CC, Mokhtari K, Delattre JY. Pitfalls in the diagnosis of brain tumours. Lancet Neurol 2006;5:937-48.
Burger PC. Malignant astrocytic neoplasms: Classification, pathologic anatomy, and response to treatment. Semin Oncol 1986;13:16-26.
Al-Okaili RN, Krejza J, Woo JH, Wolf RL, O'Rourke DM, Judy KD, et al.
Intraaxial brain masses: MR imaging based on diagnostic strategy—Initial experience. Radiology 2007;243:539-50.
Toh CH, Wei KC, Ng SH, Wan YL, Lin CP, Castillo M. Differentiation of brain abscesses from necrotic glioblastoma and cystic metastatic brain tumours with diffusion tensor imaging. AJNR 2011;32:1646-51.
Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M. Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. J Magn Reson Imaging 2006;24:817-24.
Haris M, Gupta RK, Singh A, Husain N, Husain M, Pandey CM, et al.
Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI. Neuroradiology 2008;50:531-40.
Hourani R, Brant LJ, Rizk T, Weingart JD, Barker PB, Horská A. Can proton MR spectroscopic and perfusion imaging differentiate between neoplastic and nonneoplastic brain lesions in adults? AJNR Am J Neuroradiol 2008;29:366-72.
Rollin N, Guyotat J, Streichenberger N, Honnorat J, Tran Minh VA, Cotton F. Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology 2006;48:150-9.
Covarrubias DJ, Rosen BR, Lev MH. Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 2004;9:528-37.
Al-Okaili RN, Krejza J, Wang S, Woo JH, Melhem ER. Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics 2006;26(Suppl 1):S173-89.
Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW. High-grade gliomas and solitary metastases: Differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002;222:715-21.
Batra A, Tripathi RP. Perfusion magnetic resonance imaging in intracerebral parenchymal tuberculosis: Preliminary findings. J Comput Assist Tomogr 2003;27:882-8.
Floriano VH, Torres US, Spotti AR, Ferraz-Filho JRL, Tognola WA. The role of dynamic susceptibility contrast-enhanced perfusion MR imaging in differentiating between infectious and neoplastic focal brain lesions: Results from a cohort of 100 consecutive patients. PLoS One 2013;8:e81509.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]