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apparent_diffusion_coefficient_in_meningioma

Apparent diffusion coefficient in meningioma

Generally, it is not possible to confidently distinguish benign (WHO grade I) and atypical (WHO grade II) from anaplastic (WHO grade III) meningiomas on general morphology.

Diffusion weighted imaging (DWI) along with the calculation of apparent diffusion coefficient (ADC), is a, non-invasive, and reliable technique of choice for accurate assessment and for the treatment planning of different types of brain tumors. It is more advantageous in the distinction and differentiation of benign from malignant meningiomas on the basis of ADC values (reflecting higher cellularity) 1) 2). 3).

For Surov et al., Grade II/III tumors had lower ADC mean values than grade I meningiomas. ADCmean correlated negatively with tumor proliferation index and ADCmin with tumor cell count. These associations were different in several meningiomas. ADCmean can be used for distinguishing between benign and atypical/malignant tumors 4).

The estimated threshold ADC value of 0.85 can differentiate grade I meningioma from grade II and III tumors. The same ADC value is helpful for detecting tumors with high proliferation potential 5).

There were several reports describing features of meningiomas on DWI; however, the provided data were inconsistent 6) 7) 8) 9).

Whereas some authors found an association between ADC and histological parameters of meningiomas 10) 11) 12). others did not 13) 14).

In addition, in the analysis of Ginat et al., no association between ADC and Ki-67 level was found 15), whereas other authors reported a statistically significant correlation between these parameters 16).

Because of the fact that meningioma is the most frequent intracranial tumor and is often an incidental finding on magnetic resonance imaging (MRI), it is important to correctly estimate tumor grade and proliferation potential on imaging 17).

ADC values in tumor parenchyma and peritumoral edema can provide helpful information that is otherwise not available from conventional MRI to differentiate hemangiopericytoma (HPC) from angiomatous and anaplastic meningioma 18).

Case series

2018

Ko et al., retrospectively investigated the preoperative CT and MR imaging features for the prediction of progression/recurrence (P/R) in skull base meningiomas, with emphasis on quantitative ADC values. Only patients had postoperative MRI follow-ups for more than 1 year (at least every 6 months) were included. From October 2006 to December 2015, total 73 patients diagnosed with benign (WHO grade I) skull base meningiomas were included (median follow-up time 41 months), and 17 (23.3%) patients had P/R (median time to P/R 28 months). Skull base meningiomas with spheno-orbital location, adjacent bone invasion, high DWI, and lower ADC value/ratio were significantly associated with P/R (P < 0.05). The cut-off points of ADC value and ADC ratio for prediction of P/R are 0.83 × 10- 3 mm2/s and 1.09 respectively, with excellent area under curve (AUC) values (0.86 and 0.91) (P < 0.05). In multivariate logistic regression, low ADC values (< 0.83 × 10- 3 mm2/s) and adjacent bone invasion are high-risk factors of P/R (P < 0.05), with odds ratios of 31.53 and 17.59 respectively. The preoperative CT and MRI features for prediction of P/R offered clinically vital information for the planning of treatment in skull base meningiomas 19).


Pretreatment ADC volumes of 37 meningioma patients (28 low-grade, 9 high-grade) were used for histogram profiling. WHO grade, Ki-67 expression, and progesterone receptor status were evaluated. Comparative and correlative statistics investigating the association between histogram profiling and neuropathology were performed.

The entire ADC profile (p10, p25, p75, p90, mean, median) was significantly lower in high-grade versus low-grade meningiomas. The lower percentiles, mean, and modus showed significant correlations with Ki-67 expression. Skewness and entropy of the ADC volumes were significantly associated with progesterone receptor status and Ki-67 expression. ROC analysis revealed entropy to be the most accurate parameter distinguishing low-grade from high-grade meningiomas.

ADC histogram profiling provides a distinct set of parameters, which help differentiate low-grade versus high-grade meningiomas. Also, histogram metrics correlate significantly with histological surrogates of the respective proliferative potential. More specifically, entropy revealed to be the most promising imaging biomarker for presurgical grading. Both, entropy and skewness were significantly associated with progesterone receptor status and Ki-67 expression and therefore should be investigated further as predictors for prognostically relevant tumor biological features. Since absolute ADC values vary between MRI scanners of different vendors and field strengths, their use is more limited in the presurgical setting 20).

2014

MRI examinations and histopathology of 68 surgically treated meningiomas were retrospectively reviewed. Mean ADC values were derived from diffusion imaging. Correlation coefficients were calculated for mean ADC and Ki-67 proliferation index values using linear regression. An independent unpaired Student t test was used to compare the ADC and Ki-67 proliferation index values from low-grade and more aggressive meningiomas.

A statistically significant inverse correlation was found between ADC and Ki-67 proliferation index for low-grade and aggressive meningiomas (r(2) = -0.33, p = 0.0039). ADC values (± SD) of low-grade meningiomas (0.84 ± 0.14 × 10(-3) mm(2)/s) and aggressive (atypical or anaplastic) meningiomas (0.75 ± 0.03 × 10(-3) mm(2)/s) were significantly different (p = 0.0495). Using an ADC cutoff value of 0.70 × 10(-3) mm(2)/s, the sensitivity for diagnosing aggressive meningiomas was 29%, specificity was 94%, positive predictive value was 67%, and negative predictive value was 75%.

ADC values correlate inversely with Ki-67 proliferation index and help differentiate low-grade from aggressive meningiomas 21).

1)
Filippi CG, Edgar MA, Uluğ AM et-al. Appearance of meningiomas on diffusion-weighted images: correlating diffusion constants with histopathologic findings. AJNR Am J Neuroradiol. 2001;22 (1): 65-72. AJNR Am J Neuroradiol
2)
Toh CH, Castillo M, Wong AM et-al. Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. AJNR Am J Neuroradiol. 2008;29 (9): 1630-5. doi:10.3174/ajnr.A1170
3)
Bano S, Waraich MM, Khan MA, Buzdar SA, Manzur S. Diagnostic value of apparent diffusion coefficient for the accurate assessment and differentiation of intracranial meningiomas. Acta Radiol Short Rep. 2013 Nov 23;2(7):2047981613512484. doi: 10.1177/2047981613512484. eCollection 2013. PubMed PMID: 24349716; PubMed Central PMCID: PMC3863968.
4)
Surov A, Gottschling S, Mawrin C, Prell J, Spielmann RP, Wienke A, Fiedler E. Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters. Transl Oncol. 2015 Dec;8(6):517-23. doi: 10.1016/j.tranon.2015.11.012. PubMed PMID: 26692534; PubMed Central PMCID: PMC4700293.
5)
Surov A, Ginat DT, Sanverdi E, Lim CC, Hakyemez B, Yogi A, Cabada T, Wienke A. Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis. World Neurosurg. 2016 Apr;88:598-602. doi: 10.1016/j.wneu.2015.10.049. Epub 2015 Oct 31. PubMed PMID: 26529294.
6) , 13)
Sanverdi SE, Ozgen B, Oguz KK, Mut M, Dolgun A, Soylemezoglu F, Cila A. Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? Eur J Radiol. 2012;81(9):2389–2395.
7) , 10)
Hakyemez B, Yildirim N, Gokalp G, Erdogan C, Parlak M. The contribution of diffusion-weighted MR imaging to distinguishing typical from atypical meningiomas. Neuroradiology. 2006;48(8):513–520.
8) , 11)
Nagar VA, Ye JR, Ng WH, Chan YH, Hui F, Lee CK, Lim CC. Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. AJNR Am J Neuroradiol. 2008;29(6):1147–1152.
9) , 14)
Pavlisa G, Rados M, Pazanin L, Padovan RS, Ozretic D, Pavlisa G. Characteristics of typical and atypical meningiomas on ADC maps with respect to schwannomas. Clin Imaging. 2008;32(1):22–27.
12) , 16) , 17) , 21)
Tang Y, Dundamadappa SK, Thangasamy S, Flood T, Moser R, Smith T, Cauley K, Takhtani D. Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol. 2014 Jun;202(6):1303-8. doi: 10.2214/AJR.13.11637. PubMed PMID: 24848829.
15)
Ginat DT, Mangla R, Yeaney G, Wang HZ. Correlation of diffusion and perfusion MRI with Ki-67 in high-grade meningiomas. AJR Am J Roentgenol. 2010;195(6):1391–1395.
18)
Liu L, Yin B, Geng DY, Li Y, Zhang BY, Peng WJ. Comparison of ADC values of intracranial hemangiopericytomas and angiomatous and anaplastic meningiomas. J Neuroradiol. 2014 Jul;41(3):188-94. doi: 10.1016/j.neurad.2013.07.002. Epub 2014 Feb 10. PubMed PMID: 24524869.
19)
Ko CC, Lim SW, Chen TY, Chen JH, Li CF, Shiue YL. Prediction of progression in skull base meningiomas: additional benefits of apparent diffusion coefficient value. J Neurooncol. 2018 May;138(1):63-71. doi: 10.1007/s11060-018-2769-9. Epub 2018 Jan 20. PubMed PMID: 29353434.
20)
Gihr GA, Horvath-Rizea D, Garnov N, Kohlhof-Meinecke P, Ganslandt O, Henkes H, Meyer HJ, Hoffmann KT, Surov A, Schob S. Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status. Mol Imaging Biol. 2018 Feb 1. doi: 10.1007/s11307-018-1166-2. [Epub ahead of print] PubMed PMID: 29392542.
apparent_diffusion_coefficient_in_meningioma.txt · Last modified: 2018/05/02 21:28 by administrador