Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Publication Preview--Ahead of Print
    • Past Issue Archive
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
    • COVID-19 Content and Resources
  • For Authors
  • About Us
    • About AJNR
    • Editors
    • American Society of Neuroradiology
  • Submit a Manuscript
  • Podcasts
    • Subscribe on iTunes
    • Subscribe on Stitcher
  • More
    • Subscribers
    • Permissions
    • Advertisers
    • Alerts
    • Feedback
  • Other Publications
    • ajnr

User menu

  • Subscribe
  • Alerts
  • Log in

Search

  • Advanced search
American Journal of Neuroradiology
American Journal of Neuroradiology

American Journal of Neuroradiology

  • Subscribe
  • Alerts
  • Log in

Advanced Search

  • Home
  • Content
    • Current Issue
    • Publication Preview--Ahead of Print
    • Past Issue Archive
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
    • COVID-19 Content and Resources
  • For Authors
  • About Us
    • About AJNR
    • Editors
    • American Society of Neuroradiology
  • Submit a Manuscript
  • Podcasts
    • Subscribe on iTunes
    • Subscribe on Stitcher
  • More
    • Subscribers
    • Permissions
    • Advertisers
    • Alerts
    • Feedback
  • Follow AJNR on Twitter
  • Visit AJNR on Facebook
  • Follow AJNR on Instagram
  • Join AJNR on LinkedIn
  • RSS Feeds
Research ArticlePEDIATRICS

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors

S. Vajapeyam, C. Stamoulis, K. Ricci, M. Kieran and T. Young Poussaint
American Journal of Neuroradiology January 2017, 38 (1) 170-175; DOI: https://doi.org/10.3174/ajnr.A4949
S. Vajapeyam
aFrom the Departments of Radiology (S.V., C.S., T.Y.P.)
eHarvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for S. Vajapeyam
C. Stamoulis
aFrom the Departments of Radiology (S.V., C.S., T.Y.P.)
bNeurology (C.S.), Boston Children's Hospital, Boston, Massachusetts
eHarvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for C. Stamoulis
K. Ricci
cCancer Center (K.R.), Massachusetts General Hospital, Boston, Massachusetts
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for K. Ricci
M. Kieran
dDepartment of Pediatric Oncology (M.K.), Dana-Farber Cancer Center, Boston, Massachusetts
eHarvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for M. Kieran
T. Young Poussaint
aFrom the Departments of Radiology (S.V., C.S., T.Y.P.)
eHarvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for T. Young Poussaint
  • Article
  • Figures & Data
  • Info & Metrics
  • References
  • PDF
Loading

Abstract

BACKGROUND AND PURPOSE: Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade.

MATERIALS AND METHODS: Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated.

RESULTS: Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (Ktrans), rate constant from extracellular extravascular space back into blood plasma (Kep), and extracellular extravascular volume fraction (Ve) were all significantly correlated with tumor grade; high-grade tumors showed higher Ktrans, higher Kep, and lower Ve. Although all 3 parameters had high specificity (range, 82%–100%), Kep had the highest specificity for both grades. Optimal sensitivity was achieved for Ve, with a combined sensitivity of 76% (compared with 71% for Ktrans and Kep).

CONCLUSIONS: Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.

ABBREVIATIONS:

IAUGC60
initial area under gadolinium curve at 60 seconds
DCE
dynamic contrast-enhanced
Kep
rate constant from extracellular extravascular space back into blood plasma
Ktrans
transfer constant from the blood plasma into the extracellular extravascular space
Ve
extracellular extravascular volume fraction
Vp
fractional blood plasma volume

Pediatric brain tumors are the most common cause of death from solid tumors, with an incidence rate of 5.57 cases per 100,000.1 Recent advances in the molecular characterization and treatment of brain tumors2 have made their proper classification by using imaging techniques critical. Conventional MR imaging is the technique of choice for preoperative diagnosis and evaluation of the child with an intracranial neoplasm because of its multiplanar capability and superior anatomic detail and resolution. Advanced imaging techniques such as MR perfusion are used to complement structural imaging, providing further insight into tumor physiology. In adults, dynamic contrast-enhanced (DCE) MR perfusion has been used to determine tumor grade3⇓–5 and to distinguish pseudoprogression from tumor recurrence,6 thus affecting treatment.

While dynamic susceptibility contrast perfusion and DCE-MR perfusion in adult brain tumors have been extensively studied in the literature, particularly for monitoring tumor antiangiogenesis treatments,7⇓⇓⇓–11 DCE-MR imaging studies in pediatric brain tumors have been scarce12⇓⇓⇓⇓⇓–18 and have not focused on tumor grading.

Multiparametric methods to characterize and monitor brain tumors have also shown great promise.19,20 DCE-MR imaging is particularly suited to multiparametric analyses that require image registration between modalities because it does not have geometric distortion due to susceptibility effects, unlike other advanced MR imaging modalities such as dynamic susceptibility contrast perfusion imaging and diffusion imaging.

In this study, we retrospectively reviewed DCE perfusion data from children with newly diagnosed brain tumors during a 2-year period at our institution and analyzed the pharmacokinetic tumor permeability perfusion parameters correlating with tumor grade.

Materials and Methods

Subjects

The study was performed with the approval of the institutional review board at the Dana Farber Cancer Institute. Children who presented with a brain mass and had undergone DCE perfusion studies were included. Of 52 patients identified with brain masses who had undergone DCE imaging, 6 patients had final diagnoses that were not brain tumors, 6 had nonenhancing tumors and therefore were not eligible for DCE-MR imaging analysis, and 2 patients were excluded due to motion. Thirty-eight patients were included in this study: 14 girls and 24 boys; age range, 0.30–18.14 years (median age, 6.01 years; mean age, 7.83 years).

MR Imaging Acquisition

All MR imaging studies were performed on a 3T scanner (Siemens, Erlangen, Germany). Standard MR imaging in all patients consisted of sagittal T1, axial T2-weighted, axial T2 FLAIR, axial diffusion-weighted, and multiplanar precontrast and postcontrast T1 images. All patients underwent a dynamic contrast-enhanced MR imaging protocol as follows:

  1. Variable flip angle echo-spoiled gradient echo T1-mapping sequences by using flip angles of 15°, 10°, 5°, and 2°; TR = 5 seconds; TE = minimum; FOV = 240 mm; section thickness = 5 mm.

  2. DCE-MR imaging sequence consisting of 50 phases, 7 seconds apart, with flip angle = 15°, TR = 4 seconds, TE = minimum. FOV, section thickness, and scan locations were identical to those in the T1 mapping sequences. A single bolus of gadobutrol (Gadavist, 0.1 mmol/kg body weight; Bayer Schering Pharma, Berlin, Germany) was injected 20 seconds after the start of scanning at an injection rate of 2 mL/s.

MR Imaging Postprocessing

MR images were transferred to a VersaVue workstation (iCAD, Nashua, New Hampshire) for automated processing by using OmniLook software (iCad). T1 maps were automatically calculated from the variable flip angle images21 to yield native T1 of the tissue. The 2-compartment Tofts model22 was used for the voxelwise calculation of advanced pharmacokinetic parameters such as the transfer constant from the blood plasma into the extracellular extravascular space (Ktrans), rate constant from extracellular extravascular space back into blood plasma (Kep), extracellular extravascular volume fraction (Ve), fractional blood plasma volume (Vp), and initial area under gadolinium curve at 60 seconds (IAUGC60). The model of Weinmann et al23 for blood plasma concentration was used along with a relaxivity of 5.1 L · mmol−1 · s−1 for the contrast agent.

ROIs were drawn on each section of tumor around contrast-enhancing portions of the tumor by an imaging data analyst or by a PhD scientist and verified by a Certificate of Added Qualification–certified neuroradiologist, and the mean (over voxels) and SDs of each of the variables were recorded for statistical analysis. We included only voxels that could be fit to the model in the computation of mean and SD, excluding areas of cyst, and we took care to exclude vessels from the ROI.

Statistical Analysis

Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. All the pharmacokinetic parameters described above, along with T1 of the tissue, were compared between the 2 groups by using linear regression models with each parameter as a dependent variable (the outcome) and tumor grade as a categoric independent variable (low-grade = 0, high-grade = 1). For parameters significantly distinct between the 2 groups, sensitivity and specificity were also estimated.

Given the non-normal distribution of all parameters, summary statistics reported throughout included median and interquartile ranges. In addition, confidence intervals were estimated via bootstrapping with replacement (2000 draws).

Sensitivity and specificity were estimated as follows: First, the CIs for individual parameter medians were used for thresholding. For each parameter, there were 2 confidence intervals, 1 for the median of high-grade tumors and 1 for the median of low-grade tumors. The lower CI for intervals of statistically higher values and the upper CI for intervals of statistically lower values were used as thresholds. For example, if a parameter median was significantly higher for high-grade than low-grade tumors, then any high-grade parameter value at or above the lower CI for the group median was considered a true-positive and any value below this CI was considered a false-negative (or a false-positive for low-grade). Similarly, any low-grade parameter value at or below the upper CI for the group median was considered a true-positive, and any value above this CI was considered a false-negative (or a false-positive for high-grade).

Results

Of the 38 patients who had enhancing tumors confirmed by biopsy, 18 tumors were classified as low-grade (7 pilocytic astrocytomas, 3 low-grade gliomas with piloid features, 3 low-grade gliomas, 1 low-grade ependymoma, 1 atypical meningioma World Health Organization grade II, 1 hemangioblastoma grade I, 1 ganglioglioma grade I–II, 1 low-grade histiocytic sarcoma) and 20 were classified as high-grade (11 medulloblastomas, 3 glioblastoma multiformes, 2 anaplastic ependymomas, 1 high-grade sarcoma, 1 choroid plexus carcinoma, 1 germinomatous germ cell tumor, and 1 high-grade glioma).

There was no statistically significant difference (P = .8) between patient age and tumor grade. For low-grade tumors, the median patient age was 5.52 years (25th to 75th quartiles = 2.62–12.97 years), and for high-grade tumors, the median patient age was 6.88 years (25th to 75th quartiles = 3.72–19.38 years).

The linear regression model results of the pharmacokinetic parameters are summarized in Table 1. The regression coefficient corresponding to tumor grade, its confidence intervals, standard error, significance (P value), and Wald statistics are included for parameters that were found to be significantly correlated with tumor grade. These included Ktrans, Kep, and Ve. Specifically, Ktrans was statistically higher for high-grade tumors (median = 0.89, 25th to 75th quartiles = 0.46–2.67) than for low-grade tumors (median = 0.09, 25th to 75th quartiles = 0.04–0.13). Kep was statistically higher for high-grade tumors (median = 6.76, 25th to 75th quartiles = 3.77–16.88) than for low-grade tumors (median = 0.66, 25th to 75th quartiles = 0.29–1.04). Ve was statistically lower for high-grade tumors (median = 0.12, 25th to 75th quartiles = 0.11–0.15) than for low-grade tumors (median = 0.23, 25th to 75th quartiles = 0.19–0.26). Information on the range, sensitivity, and specificity of these parameters is provided in Table 2.

View this table:
  • View inline
  • View popup
Table 1:

Summary of model permeability parameters for all imaging measures compared between high- and low-grade pediatric tumors

View this table:
  • View inline
  • View popup
Table 2:

Summary statistics, sensitivity, and specificity of permeability parameters statistically correlated with tumor grade

Ktrans

For low-grade tumors, Ktrans was in the range of 0.02–0.52 (median = 0.09; 95% CI for the median = 0.06–0.13). For high-grade tumors, it was in the range of 0.09–6.19 (median = 0.89; 95% CI = 0.57–1.85). Based on the CI thresholds, there were 14 high-grade and 13 low-grade true-positives, resulting in a 71% (27/38) combined sensitivity of this parameter to detect high- or low-grade tumors. Individually, the sensitivity of this parameter to detect high-grade tumors was 70% (14/20), and for low-grade tumors, it was 72% (13/18). In addition, there were 2 high-grade tumors with values below the threshold for low-grade. These were considered false-positives for low-grade. There were no low-grade tumors with values above the threshold for high-grade. Consequently, the specificity of this parameter was 100% (18/18) for high-grade tumors and 90% (18/20) for low-grade tumors.

Kep

For low-grade tumors, Kep was in the range of 0.1–3.13 (median = 0.66; 95% CI = 0.33–0.97). For high-grade tumors, Kep was in the range of 1.01–29.67 (median = 6.76; 95% CI = 4.99–13.95). Based on the CI thresholds, there were 14 high-grade and 13 low-grade true-positives, resulting in a combined sensitivity of 71% (27/38). Individually, the sensitivity of this parameter to detect high-grade tumors was 70% (14/20) and 72% (13/18) for low-grade tumors. There were no false-positives in either group; thus, specificity was 100% (18/18) for high-grade tumors and 100% (20/20) for low-grade tumors.

Ve

For low-grade tumors, Ve was in the range of 0.11–0.48 (median = 0.23; 95% CI = 0.19–0.26). For high-grade tumors, it was in the range of 0.04–0.18 (median = 0.12; 95% CI = 0.11–0.15). Based on the CI thresholds, there were 15 high-grade and 14 low-grade true-positives, resulting in a combined sensitivity of 76% (29/38). Individually, the sensitivity of this parameter to detect high-grade tumors was 75% (15/20) and 78% (14/18) for low-grade tumors. There were 4 low-grade tumors with values below the threshold for high-grade. These were considered false-positives for high-grade. There were no false-positives for low-grade. Consequently, the specificity of this parameter for high-grade was 82% (18/22) and 100% (20/20) for low-grade.

Discussion

Pediatric brain tumors encountered in a clinical setting differ significantly in tumor type from those seen in adults; therefore, predicting tumor grade by using MR imaging in a pediatric clinical setting presents a unique set of issues. While vessel permeability metrics derived from DCE-MR imaging have been associated with tumor grade in adult populations,24⇓–26 such studies in pediatric brain tumors have been lacking.

Dynamic susceptibility contrast perfusion MR imaging has been studied in children by Ho et al27 to associate tumor grade with maximal relative cerebral blood volume and with the postbolus shape of the enhancement curve.28 Koob et al19 used a multiparametric approach to show that the highest grading accuracy was achieved by using a combination of parameters derived from diffusion and DSC perfusion imaging. Yeom et al29 used arterial spin-labeling to measure perfusion and found that maximal relative tumor blood flow of high-grade tumors was significantly higher than that of low-grade tumors.

Our results suggest that the transfer constants, both Ktrans and Kep, are significantly distinct between the low-grade and high-grade groups. Several studies have examined the role of Ktrans and have shown Ktrans correlates well with tumor grade, particularly in gliomas in adults.24⇓–26,30⇓–32 The role of angiogenesis in promoting leakiness of the tumor vasculature and development of new vessels is well-documented, and our findings of increased Ktrans in higher grade tumors supports that hypothesis. Ktrans in gliomas has also been shown to be a marker of progression31,33 in adults.

Our study shows that pediatric low-grade tumors in fact have a higher Ve compared with high-grade tumors, contrary to findings in adult tumors showing lower Ve in low-grade adult tumors.24⇓–26 In fact, the optimal sensitivity appears to be achieved for Ve, with a combined sensitivity of 76% (compared with 71% for Ktrans and Kep) and individual sensitivities of 75% and 78%, respectively, for high- and low-grade tumors. The role of Ve, which is an indicator of extracellular extravascular space, is poorly understood in the brain tumor literature. Our findings concur with the theory that the higher cellularity in high-grade tumors would lead to a decreased extracellular space due to the closely packed tumor cells, and hence lower Ve. As seen in Figs 1 and 2, the areas of decreased Ve also correlate with areas of decreased ADC, further confirming our hypothesis. Mills et al34 however failed to find the expected correlation in a voxelwise analysis between Ve and ADC in adult glioblastoma multiformes, possibly due to the confounding effects of the heterogeneous nature of those tumors.

Fig 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 1.

A 17-year-old girl with an anaplastic grade III ependymoma is shown. In addition to axial T2-weighted and axial postcontrast T1-weighted images, corresponding maps shown are ADC, IAUGC60, Ktrans, Kep, Ve, and Vp. Axial T2 image demonstrates heterogeneous tumor in the left frontal lobe with regions of hypointensity. Axial T1 postcontrast image demonstrates heterogeneous enhancement. ADC image demonstrates regions of restricted diffusion within the tumor. High Ktrans and Kep are readily apparent in the overlaid color maps, and Ve is low.

Fig 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 2.

A 3-year-old boy with posterior fossa pilocytic astrocytoma is shown. Axial T2 image shows a T2 hyperintense mass in the vermis, which shows enhancement and increased diffusion. Permeability images show that though there is marked enhancement typical of these tumors, Ktrans and Kep are considerably lower, whereas Ve is higher throughout the tumor compared with the high-grade tumor shown in Fig 1.

All 3 parameters had high specificity, in the range 82%–100%. For low-grade tumors, their specificity was 90%–100%, and for high-grade tumors, the specificity was 82%–100%. Kep had the highest specificity (100%) for both grades.

One of the limitations of this study is that DCE-MR imaging–derived pharmacokinetic parameters are heavily dependent on the model and input parameters used12,22 and are thought to be difficult to standardize. Some of these parameters may not be as critical as previously thought. For example, Larsson et al35 recently found that there was no significant difference between using T1 derived from a mapping sequence and using a fixed T1 in high-grade gliomas in adults. Because all our subjects were analyzed by using identical model parameters, this finding may not be that critical in this study. Last, the heterogeneity of tumor types and the relatively small sample in this study are also a limitation. Previous studies, however, have investigated smaller samples, so our findings are based on a comparatively larger sample. Nevertheless, this work may be validated in a larger cohort of children with pediatric brain tumors in future studies.

Conclusions

Dynamic contrast-enhanced perfusion MR imaging is useful in a clinical setting for the differential diagnosis and grading of pediatric brain tumors. Pharmacokinetic parameters such as Ve, Ktrans, and Kep can be used to differentiate low- and high-grade tumors to facilitate treatment planning and determine prognosis and have comparable specificities for tumor grade. In our study, the parameter Kep had the highest specificity for both grades. Of the pharmacokinetic parameters studied, Ve offers the highest sensitivity (overall 76%) for determining tumor grade.

Footnotes

  • Preliminary results previously presented at: Annual Meeting of the Radiological Society of North America, November 30 to December 5, 2014; Chicago, Illinois.

References

  1. 1.↵
    1. Ostrom QT,
    2. Gittleman H,
    3. Fulop J, et al
    . CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol 2015;17(suppl 4):iv1–iv62 doi:10.1093/neuonc/nov189 pmid:26511214
    FREE Full Text
  2. 2.↵
    1. Pollack IF
    . Multidisciplinary management of childhood brain tumors: a review of outcomes, recent advances, and challenges. J Neurosurg Pediatr 2011;8:135–48 doi:10.3171/2011.5.PEDS1178 pmid:21806354
    CrossRefPubMed
  3. 3.↵
    1. Arevalo-Perez J,
    2. Kebede AA,
    3. Peck KK, et al
    . Dynamic contrast-enhanced MRI in low-grade versus anaplastic oligodendrogliomas. J Neuroimaging 2016;26:366–71 doi:10.1111/jon.12320 pmid:26707628
    CrossRefPubMed
  4. 4.↵
    1. Zhao J,
    2. Yang ZY,
    3. Luo BN, et al
    . Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors. PLoS One 2015;10:e0138573 doi:10.1371/journal.pone.0138573 pmid:26384329
    CrossRefPubMed
  5. 5.↵
    1. Zhang N,
    2. Zhang L,
    3. Qiu B, et al
    . Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas. J Magn Reson Imaging 2012;36:355–63 doi:10.1002/jmri.23675 pmid:22581762
    CrossRefPubMed
  6. 6.↵
    1. Thomas AA,
    2. Arevalo-Perez J,
    3. Kaley T, et al
    . Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma. J Neurooncol 2015;125:183–90 doi:10.1007/s11060-015-1893-z pmid:26275367
    CrossRefPubMed
  7. 7.↵
    1. Schmainda KM,
    2. Prah M,
    3. Connelly J, et al
    . Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma. Neuro Oncol 2014;16:880–88 doi:10.1093/neuonc/not216 pmid:24431219
    Abstract/FREE Full Text
  8. 8.↵
    1. Harris RJ,
    2. Cloughesy TF,
    3. Hardy AJ, et al
    . MRI perfusion measurements calculated using advanced deconvolution techniques predict survival in recurrent glioblastoma treated with bevacizumab. J Neurooncol 2015;122:497–505 doi:10.1007/s11060-015-1755-8 pmid:25773062
    CrossRefPubMed
  9. 9.↵
    1. Arevalo-Perez J,
    2. Thomas AA,
    3. Kaley T, et al
    . T1-weighted dynamic contrast-enhanced MRI as a noninvasive biomarker of epidermal growth factor receptor vIII status. AJNR Am J Neuroradiol 2015;36:2256–61 doi:10.3174/ajnr.A4484 pmid:26338913
    Abstract/FREE Full Text
  10. 10.↵
    1. Jain R,
    2. Poisson LM,
    3. Gutman D, et al
    . Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 2014;272:484–93 doi:10.1148/radiol.14131691 pmid:24646147
    CrossRefPubMed
  11. 11.↵
    1. Pope WB
    . Predictive imaging marker of bevacizumab efficacy: perfusion MRI. Neuro Oncol 2015;17:1046–47 doi:10.1093/neuonc/nov067 pmid:25910842
    FREE Full Text
  12. 12.↵
    1. Lam S,
    2. Lin Y,
    3. Warnke PC
    . Permeability imaging in pediatric brain tumors. Transl Pediatr 2014;3:218–28 doi:10.3978/j.issn.2224-4336.2014.07.01 pmid:26835339
    CrossRefPubMed
  13. 13.↵
    1. Gururangan S,
    2. Fangusaro J,
    3. Poussaint TY, et al
    . Efficacy of bevacizumab plus irinotecan in children with recurrent low-grade gliomas: a Pediatric Brain Tumor Consortium study. Neuro Oncol 2014;16:310–17 doi:10.1093/neuonc/not154 pmid:24311632
    Abstract/FREE Full Text
  14. 14.↵
    1. Zukotynski KA,
    2. Fahey FH,
    3. Vajapeyam S, et al
    . Exploratory evaluation of MR permeability with 18F-FDG PET mapping in pediatric brain tumors: a report from the Pediatric Brain Tumor Consortium. J Nucl Med 2013;54:1237–43 doi:10.2967/jnumed.112.115782 pmid:23801675
    Abstract/FREE Full Text
  15. 15.↵
    1. Thompson EM,
    2. Guillaume DJ,
    3. Dosa E, et al
    . Dual contrast perfusion MRI in a single imaging session for assessment of pediatric brain tumors. J Neurooncol 2012;109:105–14 doi:10.1007/s11060-012-0872-x pmid:22528798
    CrossRefPubMed
  16. 16.↵
    1. Gururangan S,
    2. Fangusaro J,
    3. Young Poussaint T, et al
    . Lack of efficacy of bevacizumab + irinotecan in cases of pediatric recurrent ependymoma: a Pediatric Brain Tumor Consortium study. Neuro Oncol 2012;14:1404–12 doi:10.1093/neuonc/nos213 pmid:23019233
    Abstract/FREE Full Text
  17. 17.↵
    1. Gururangan S,
    2. Chi SN,
    3. Young Poussaint T, et al
    . Lack of efficacy of bevacizumab plus irinotecan in children with recurrent malignant glioma and diffuse brainstem glioma: a Pediatric Brain Tumor Consortium study. J Clin Oncol 2010;28:3069–75 doi:10.1200/JCO.2009.26.8789 pmid:20479404
    Abstract/FREE Full Text
  18. 18.↵
    1. Liu HL,
    2. Chang TT,
    3. Yan FX, et al
    . Assessment of vessel permeability by combining dynamic contrast-enhanced and arterial spin labeling MRI. NMR Biomed 2015;28:642–49 doi:10.1002/nbm.3297 pmid:25880892
    CrossRefPubMed
  19. 19.↵
    1. Koob M,
    2. Girard N,
    3. Ghattas B, et al
    . The diagnostic accuracy of multiparametric MRI to determine pediatric brain tumor grades and types. J Neurooncol 2016;127:345–53 doi:10.1007/s11060-015-2042-4 pmid:26732081
    CrossRefPubMed
  20. 20.↵
    1. Law M,
    2. Yang S,
    3. Wang H, et al
    . Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003;24:1989–98 pmid:14625221
    Abstract/FREE Full Text
  21. 21.↵
    1. Fram EK,
    2. Herfkens RJ,
    3. Johnson GA, et al
    . Rapid calculation of T1 using variable flip angle gradient refocused imaging. Magn Reson Imaging 1987;5:201–08 doi:10.1016/0730-725X(87)90021-X pmid:3626789
    CrossRefPubMed
  22. 22.↵
    1. Tofts PS
    . Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997;7:91–101 doi:10.1002/jmri.1880070113 pmid:9039598
    CrossRefPubMed
  23. 23.↵
    1. Weinmann HJ,
    2. Laniado M,
    3. Mützel W
    . Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers. Physiol Chem Phys Med NMR 1984;16:167–72 pmid:6505043
    PubMed
  24. 24.↵
    1. Abe T,
    2. Mizobuchi Y,
    3. Nakajima K, et al
    . Diagnosis of brain tumors using dynamic contrast-enhanced perfusion imaging with a short acquisition time. Springerplus 2015;4:88 doi:10.1186/s40064-015-0861-6 pmid:25793147
    CrossRefPubMed
  25. 25.↵
    1. Li X,
    2. Zhu Y,
    3. Kang H, et al
    . Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging 2015;15:4 doi:10.1186/s40644-015-0039-z pmid:25889239
    CrossRefPubMed
  26. 26.↵
    1. Choi HS,
    2. Kim AH,
    3. Ahn SS, et al
    . Glioma grading capability: comparisons among parameters from dynamic contrast-enhanced MRI and ADC value on DWI. Korean J Radiol 2013;14:487–92 doi:10.3348/kjr.2013.14.3.487 pmid:23690718
    CrossRefPubMed
  27. 27.↵
    1. Ho CY,
    2. Cardinal JS,
    3. Kamer AP, et al
    . Relative cerebral blood volume from dynamic susceptibility contrast perfusion in the grading of pediatric primary brain tumors. Neuroradiology 2015;57:299–306 doi:10.1007/s00234-014-1478-0 pmid:25504266
    CrossRefPubMed
  28. 28.↵
    1. Ho CY,
    2. Cardinal JS,
    3. Kamer AP, et al
    . Contrast leakage patterns from dynamic susceptibility contrast perfusion MRI in the grading of primary pediatric brain tumors. AJNR Am J Neuroradiol 2016;37:544–51 doi:10.3174/ajnr.A4559 pmid:26564438
    Abstract/FREE Full Text
  29. 29.↵
    1. Yeom KW,
    2. Mitchell LA,
    3. Lober RM, et al
    . Arterial spin-labeled perfusion of pediatric brain tumors. AJNR Am J Neuroradiol 2014;35:395–401 doi:10.3174/ajnr.A3670 pmid:23907239
    Abstract/FREE Full Text
  30. 30.↵
    1. Roberts HC,
    2. Roberts TPL,
    3. Ley S, et al
    . Quantitative estimation of microvascular permeability in human brain tumors: correlation of dynamic Gd-DTPA–enhanced MR imaging with histopathologic grading. Acad Radiol 2002;9:S151–55 doi:10.1016/S1076-6332(03)80425-7 pmid:12019855
    CrossRefPubMed
  31. 31.↵
    1. Mills SJ,
    2. Patankar TA,
    3. Haroon HA, et al
    . Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 2006;27:853–58 pmid:16611778
    Abstract/FREE Full Text
  32. 32.↵
    1. Patankar TF,
    2. Haroon HA,
    3. Mills SJ, et al
    . Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 2005;26:2455–65 pmid:16286385
    Abstract/FREE Full Text
  33. 33.↵
    1. Cao Y,
    2. Nagesh V,
    3. Hamstra D, et al
    . The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res 2006;66:8912–17 doi:10.1158/0008-5472.CAN-05-4328 pmid:16951209
    Abstract/FREE Full Text
  34. 34.↵
    1. Mills SJ,
    2. Soh C,
    3. Rose CJ, et al
    . Candidate biomarkers of extravascular extracellular space: a direct comparison of apparent diffusion coefficient and dynamic contrast-enhanced MR imaging–derived measurement of the volume of the extravascular extracellular space in glioblastoma multiforme. AJNR Am J Neuroradiol 2010;31:549–53 doi:10.3174/ajnr.A1844 pmid:19850765
    Abstract/FREE Full Text
  35. 35.↵
    1. Larsson C,
    2. Kleppesto M,
    3. Grothe I, et al
    . T1 in high-grade glioma and the influence of different measurement strategies on parameter estimations in DCE-MRI. J Magn Reson Imaging 2015;42:97–104 doi:10.1002/jmri.24772 pmid:25350816
    CrossRefPubMed
  • Received May 11, 2016.
  • Accepted after revision August 1, 2016.
  • © 2017 by American Journal of Neuroradiology
View Abstract
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 38 (1)
American Journal of Neuroradiology
Vol. 38, Issue 1
1 Jan 2017
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Advertisement
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors
(Your Name) has sent you a message from American Journal of Neuroradiology
(Your Name) thought you would like to see the American Journal of Neuroradiology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors
S. Vajapeyam, C. Stamoulis, K. Ricci, M. Kieran, T. Young Poussaint
American Journal of Neuroradiology Jan 2017, 38 (1) 170-175; DOI: 10.3174/ajnr.A4949

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors
S. Vajapeyam, C. Stamoulis, K. Ricci, M. Kieran, T. Young Poussaint
American Journal of Neuroradiology Jan 2017, 38 (1) 170-175; DOI: 10.3174/ajnr.A4949
Reddit logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • Abstract
    • ABBREVIATIONS:
    • Materials and Methods
    • Results
    • Discussion
    • Conclusions
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • References
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Advanced ADC Histogram, Perfusion, and Permeability Metrics Show an Association with Survival and Pseudoprogression in Newly Diagnosed Diffuse Intrinsic Pontine Glioma: A Report from the Pediatric Brain Tumor Consortium
  • Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade
  • Crossref
  • Google Scholar

This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.

More in this TOC Section

  • Neuroimaging Features of Biotinidase Deficiency
  • Medullary Tegmental Cap Dysplasia: Fetal and Postnatal Presentations of a Unique Brainstem Malformation
  • Diagnostic Utility of 3D Gradient-Echo MR Imaging Sequences through the Filum Compared with Spin-Echo T1 in Children with Concern for Tethered Cord
Show more Pediatrics

Similar Articles

Advertisement

News and Updates

  • Lucien Levy Best Research Article Award
  • Thanks to our 2022 Distinguished Reviewers
  • Press Releases

Resources

  • Evidence-Based Medicine Level Guide
  • How to Participate in a Tweet Chat
  • AJNR Podcast Archive
  • Ideas for Publicizing Your Research
  • Librarian Resources
  • Terms and Conditions

Opportunities

  • Share Your Art in Perspectives
  • Get Peer Review Credit from Publons
  • Moderate a Tweet Chat

American Society of Neuroradiology

  • Neurographics
  • ASNR Annual Meeting
  • Fellowship Portal
  • Position Statements

© 2023 by the American Society of Neuroradiology | Print ISSN: 0195-6108 Online ISSN: 1936-959X

Powered by HighWire