Submit manuscript...
Journal of
eISSN: 2373-633X

Cancer Prevention & Current Research

Research Article Volume 6 Issue 1

Minimum Apparent Diffusion Coefficient (ADC) Value in Differentiation of High and Low Grade Gliomas, Does it Make a Difference?

Osama Mohammed Ebied,1 Mohamed Shawky,2 Shaimaa abdel-hamid hassanein2

1Assistant professor of Radiology department, Menoufia University, Egypt
2Lecturer of Radiology department, Menoufia University, Egypt

Correspondence: Shaimaa Abdel-hamid Hassanein MD, lecturer of radiology, Radiology Department, Faculty of Medicine, Menoufia University, Shebin-el-kom, Menoufia, Egypt, Tel 2.01007E+11

Received: January 11, 2016 | Published: November 10, 2016

Citation: Ebied OM, Shawky M, hassanein SA (2016) Minimum Apparent Diffusion Coefficient (ADC) Value in Differentiation of High and Low Grade Gliomas, Does it Make a Difference? J Cancer Prev Curr Res 6(1): 00193. DOI: 10.15406/jcpcr.2016.06.00193

Download PDF

Abstract

Objectives: This study aimed to study the minimum and mean ADC values in the differentiation of high and low grade gliomas.

Background: Diffusion-weighted imaging (DWI) has greatly enhanced the ability of MRI to differentiate high and low grade gliomas where the ADC values inversely correlated with the tumor grade.

Methods: This retrospective study included 50 patients (M/F 23/27) with pathologically proven gliomas (30 patients with high grade and 20 with low grade gliomas) who underwent MRI with diffusion weighted sequence ((b-value 1000s/mm2) acquired on a 1.5T scanner at menoufia university hospital.

Results: Diffusion restriction was found in 93.3% of cases of high grade gliomas (n=28/30) with mean calculated ADC value of calculated mean and minimum ADC values were 0.87±0.3 x 10-3mm2/sec and 0.82±0.2 x10-3 mm2/sec respectively. In low grade gliomas diffusion restriction was identified in 7 cases (n=7/20, 35%) with a mean calculated ADC values of 1.3±0.3 x 10-3mm2/sec and 1.15±0.2 x 10-3mm2/sec for the mean and minimum ADC respectively. Statistical significance was found between the calculated ADC values of the high and low grade gliomas when using the minimum and mean ADC values between the high and low grade gliomas (p value <0.001)

Conclusion: We have demonstrated that both mean and minimum ADC values measurements can be used when trying to differentiate high and low grade gliomas with both showing statistical significance.

Keywords:DWI, gliomas, grade, MRI, ADC, ischaemic infarctions, vasculopathies, neoplasms, peritumoral edema

Abbreviations

DWI, diffusion weighted image; MRI, magnetic resonance image; ADC, apparent diffusion coefficient; T, tesla; FOV, field of view; ROI, region of interest

Introduction

Diffusion weighted imaging (DWI) is a relatively new method. It was introduced in clinical practice in 1990's; initially much of its role was described in acute ischaemic stroke.1,2 It provides information about physiologic state of the brain and is particularly sensitive for ischaemic infarctions.3 It provides image contrast that is dependent on the molecular motion of water, which may be substantially altered by disease.2

This technique exploits the phenomenon of diffusion, which is related to Brownian motion at the molecular level. DWI takes advantage of the fact that intracellular water molecules are much more limited in their movement than extracellular ones, because they quickly bump into the cell membrane that contains them. The more restricted the movement of water, the brighter it will be on DWI sequences.4 DWI consists of a DW image, also called the diffusion trace, and an apparent diffusion coefficent (ADC) map. DW image is a T2-weighted echoplanar background image attenuated by the rate of apparent diffusion. DW image, together with qualitative and quantitative assessment of the ADC map has been widely used in the diagnosis of acute cerebral infarction, owing to the reliable distinction of cytotoxic and vasogenic edema.2 DWI is highly sensitive in detecting early cerebral ischemic changes in acute stroke patients.5

DWI has very recently been described and suggested in the differential diagnosis of various non-infarct lesions of the brain which are hyperintense in the diffusion trace image, such as infectious, neoplastic and demyelinating diseases, encephalopathies, leukodystrophies, vasculitis and vasculopathies, hemorrhage and trauma.6 Gliomas are the most common primary neoplasms of the brain in adults ranging in grade from low to high.7 Glioma grading is based on the histopathologic assessment of the tumor and is critical for planning therapeutic approaches and assessing prognosis and response to therapy.8 Advanced MR imaging techniques such as DWI provide physiologic information that complements the anatomic information obtained from conventional MR imaging.9,10

The signal intensity of gliomas on DW images is variable (hyper-iso-, or hypointense).11 Glioma grade correlates inversely with minimum ADC values that can be explained on the basis of increasing tumor cellularity with grade.12 Tumors with higher cellularity or higher grade show increased signal on the DW image and a marked reduction in ADC values; In addition to the hypercellularity which causes increased intracellular water, the low ADC values are also related to the decreased extracellular fluid. Low grade gliomas, because of their low cellularity, have a significantly higher ADC values compared to high grade gliomas.13

Measurement of ADC values should be done from the maximally restricted diffusion areas, because histologically the actual grade of the tumor is determined from the areas with the highest grade. DWI can provide information about peritumoral neoplastic cell infiltration, perhaps even help discriminate the boundaries between tumor, infiltrating tumor, peritumoral edema, and normal brain parenchyma.14 The aim of this study was to evaluate the diagnostic accuracy of DWI and ADC calculation in the characterization of the grade of different gliomas. Our objectives were the following: 1) to evaluate the diffusion characteristics in high and low grade gliomas, 2) to establish whether there is any difference in the calculated ADC values in gliomas grading, 3) to investigate whether the use of minimum ADC values improve the diagnostic accuracy of DW MR imaging.

Patients and methods

Study Population

We retrospectively reviewed data obtained from 50 patients with a histopathologically proved diagnosis of gliomas (30 patients with high grade and 20 patients with low grade gliomas) who underwent an MR imaging study at our department (menoufia university hospital, MRI unit) from January 2013 till January 2015. Diagnoses were histologically confirmed by surgical resection or biopsy.

Magnetic resonance imaging

All of the patients underwent MR imaging study on a 1.5 T clinical scanner (Toshiba Vantage) using standard head coil (NV SPDR) with 240x240-mm FOV. Conventional MR images consisted of axial T1WI, axial T2WI, axial FLAIR, sagittal T1WI, coronal T2WI and contrast-enhanced images T1-weighted images after intravenous contrast injection (gadopentetate dimeglumine, 0.1mmol/kg). DW MR imaging was acquired in the axial plane by using a single-shot, spin-echo echo-planar imaging sequence (with b-values of 1000second/mm2) in 3 orthogonal directions.

DWI data analysis

Isotropic (trace, i.e, the summation of 3 orthogonal directions) DW images & ADC maps were visually inspected and classified as restricted and free diffusion compared with normal white matter. ADC maps and values were calculated by using the inbuilt manufacturer’s software with the ROIs. ROIs were manually constructed and placed over the region of the maximum hypointensity corresponding to the highest diffusion restriction of the solid portion of tumor, Figure 1. Afterward, the minimum and mean ADC obtained was selected for the analysis. Control ADC values were obtained from normal-appearing white matter on the normal brain tissue unaffected by pathology. ROIs values were expressed as 10-3mm2/s. The ROIs were carefully placed to avoid cystic, necrotic, and hemorrhagic regions that might influence ADC values. Both values were compared using the 2-sample t test with statistical significance set at P<0.05. Minimum and mean ADC values of both low and high grade gliomas were also compared together using U test with statistical significance set at P<0.001.

Figure 1a & 1b Axial isotropic DWI and ADC map of a pilocytic astrocytoma showing no diffusion restriction of the solid part of the tumor.
Figure 1c & 1d axial isotropic DWI and ADC map of a right frontal lobe glioblastoma multiform showing diffusion restriction.

Results

Patient population

The studied group consisted of 50 patients (23 males and 27 females) with ages ranging from 7 to 77years with a mean age of 36. 4years. the age and gender distribution is summarized at Table 1, that shows low grade gliomas to be more common in those under the age of 20 years (n=13/20, 65%), while high grade glioma was commoner in those aged >30-50years (n=15/30, 50%).

Age in years

High grade glioma N=30

Low grade glioma N=20

Male

Female

Male

Female

0- 10

-

-

3

3

>10-20

2

-

3

4

> 20- 30

3

-

-

2

>30-40

3

4

-

-

>40-50

2

6

3

1

>50- 60

-

4

-

1

>60-70

2

1

>70

2

1

Total

14

16

9

11

Table 1 The demographics of the study
The table shows the age and gender distribution in the study. N=number

Diffusion characteristics and ADC value analysis

Diffusion restriction was found in 93.3% of cases of high grade gliomas (n=28/30) with mean calculated ADC value of calculated mean and minimum ADC values were 0.87±0.3x10-3mm2/sec and 0.82±0.2x10-3mm2/sec respectively. In low grade gliomas diffusion restriction was identified in 7 cases (n=7/20, 35%) with a mean calculated ADC values of 1.3±0.3x10-3mm2/sec and 1.15±0.2x10-3mm2/sec for the mean and minimum ADC respectively. Statistical significance was found between the calculated ADC values of the high and low grade gliomas and those of the NAWM in the same patients. These findings are summarized in Table 2 & Figure 2.

Figure 2 Boxplots showing the range of minimum and mean ADC values encountered in the studied 30 high grade and 20 low grade gliomas.
HG, high grade gliomas; LG, low grade gliomas

 

True restriction

Mean ADC value±SD (x10-3 mm2/sec)

ADC value±SD of NAWM (x10-3mm2/sec)

Min. ADC value±SD (x10-3mm2/sec)

P value

No.

%

High grade gliomas

28/30

93.30%

0.87±0.3

0.67±0.04

0.82±0.3

<0.05

Low grade gliomas

7/20

35%

1.3±0.3

0.73±0.07

1.15±0.2

<0.05

Table 2 The ADC values
ADC, apparent diffusion coefficient; Min, minimum; No, number; SD, standard deviation

Table 2 summarizes the diffusion characteristics of the studied high and low grade gliomas with the mean calculated ADC values in comparison to those of the normal appearing white matter in the same patients.  Statistical significance was also found between the calculated minimum and mean ADC values between the high and low grade gliomas (p value <0.001), Table 3.

 

High grade glioma N=30

Low grade glioma N=20

ADC

(x10-3 mm2/sec)

(x10-3 mm2/sec)

U test

P value

Min

0.82±0.3

1.15±0.28

3.77

<0.001

Mean

0.87±0.3

1.3±0.3

4.3

<0.001

Table 3 Comparison of the ADC values in the studied high and low grade gliomas
The table shows the statistical significance found between the minimum and mean ADC values of high and low grade gliomas.
ADC, apparent diffusion coefficient; Min, minimum; N, number

Discussion

Gliomas are the most encountered intra-axial brain neoplasm, hence accurate grading of gliomas is of utmost importance because the therapeutic approach and prognosis differ considerably according to tumor grade.9,15,16 Conventional MR imaging provides information on contrast enhancement, mass effect, edema, and necrosis, however it is not always accurate for the precise grading of glioma. Advanced MR imaging techniques such as perfusion and diffusion MR imaging have demonstrated great utility for the assessment of brain tumors.9

Diffusion-weighted magnetic resonance (MR) imaging provides image contrast that is different from that provided by the conventional MR techniques12 as DWI allows assessing the cellularity of tumors in a noninvasive form. As cellular and subcellular elements impede water mobility,16 hence quantitative information from the restriction of water molecule movement can be observed in calculating the ADC13,17,18 Thus, brain neoplasms with higher cellularity or with a higher grade show a significant reduction in ADC values.18

In our series of 50 gliomas, lower ADC values were identified at high grade gliomas, n=30 (0.87±0.3x10-3mm2/sec for mean ADC value) compared to low grade gliomas, n=20 (1.3±0.3x10-3 mm2/sec for mean ADC value). This goes with the findings found by Bulakbasi et al.,19 who studied 8 cases of low grade gliomas and 12 cases of high grade gliomas and showed lower ADC values in high grade gliomas than in low grade gliomas (mean ADC values of 0.87±0.1x10-3 mm2/sec and 1.15±0.116x10-3 mm2/sec respectively).

When using the minimum ADC values also significant difference was identified between high and low grade gliomas with measured values of 0.82±0.3x10-3 mm2/sec and 1.15±0.28 x10-3 mm2/sec respectively. This goes with that found by Hilario et al.,15 who studied the minimum ADC values of 162gliomas. Their measures were 0.78±0.18x10-3 mm2/sec and 1.27±0.29x10-3 mm2/sec for high and low grade gliomas respectively. The difference in numbers is probably attributed to the smaller studied group in our study (n=50).

Similar findings were also reported by previous, however in a smaller study population, Lee et al.,17 (n=16), Kwee et al.,20 (n=17), and Calli et al.,18 (n=31), Also demonstrated increased signal intensity on DWI in high grade gliomas with significant reduction in their ADC values. Our findings also agree with those by Yamasaki et al.,21 who demonstrated significant reduction in ADC values when comparing low grade gliomas versus high grade gliomas.

Conclusion

Our results in a series of 50 patients with gliomas confirmed that DWI with ADC value measurements can be used to differentiate high and low grade gliomas in a noninvasive method for approximating tumor grade. We have demonstrated that both mean and minimum ADC values measurements can be used when trying to differentiate high and low grade gliomas with both showing statistical significance.

Acknowledgments

The authors are very thankful to all the technicians in the MRI unit, Menoufia university hospital who helped in the collection of data.

Conflicts of interest

Author declares there are no conflicts of interest.

References

  1. Karaarslan E, Arslan A. Diffusion weighted MR imaging in non-infarct lesions of the brain. European Journal of Radiology. 2008;65(3):402‒416.
  2. Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR Imaging of the Brain. Radiology. 2000;217(2):331‒145.
  3. Alam M, Sajjad Z, Azeemuddin M, et al. Diffusion Weighted MR Imaging of Ring Enhancing Brain Lesions. Journal of the College of Physicians and Surgeons, Pakistan. 2012;22(7):428‒431.
  4. Seidenwurm DJ. Introduction to Brain Imaging, Fundamentals of Diagnostic Radiology. 3rd ed. Lippincott Williams & Wilkinns: USA; 2007. 49 p.
  5. El-Brashy MA, Mohamed HH, Ebied OM. Role of magnetic resonant diffusion-weighted imaging in evaluation of acute cerebral stroke. Menoufia Med J. 2014;27(4):752‒757.
  6. Mangla R, Kolar B, Almast J, et al. Border Zone Infarcts: Pathophysiologic and Imaging Characteristics. Radio Graphics. 2011;31(5):1201‒1214.
  7. Chishty IA, Rafique MZ, Hussain M, et al. MRI Characterization and Histopathological Correlation of Primary Intra-axial Brain Glioma. JLUMHS. 2010;9(2):64‒69.
  8. Hakyemez B, Erdogan C, Ercan I, et al. High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol. 2005;60(4):493‒502.
  9. Rollin N, Guyotat J, Streichenberger N, et al. Clinical relevance of diffusion and perfusion magnetic resonance in assessing intra-axial brain tumors. Neuroradiology. 2006;48(3):150‒159.
  10. Sadeghi N, D’Haene N, Decaestecker C, et al. Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. AJNR Am J Neuroradiol. 29(3):476‒482.
  11. Cha S. Update on brain tumor imaging: From anatomy to physiology. AJNR. 2006;27(3):475‒487.
  12. Rajeshkannan R, Moorthy S, Sreekumar KP, et al. Clinical applications of Diffusion Weighted MR imaging: A Review. Indian Journal of Radiologic Imaging. 2006;16(4):705‒710
  13. Kitis O, Altay H, Calli C, et al. Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol. 2005;55(3):393‒400.
  14. Hygino LC, Vieira IG, Domingues RC. Diffusion MR imaging: an important tool in the assessment of brain tumors. Neuroimaging Clin N Am. 2011;21(1):27‒49.
  15. Hilario A, Ramos A, Perez-Nunez A, et al. The Added Value of Apparent Diffusion Coefficient to Cerebral Blood Volume in the Preoperative Grading of Diffuse Gliomas. AJNR Am J Neuroradiol. 33(4):701‒707.
  16. Kono K, Inoue Y, Nakayama K, et al. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22(6):1081‒1088.
  17. Lee EJ, Lee SK, Agid R, et al. Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol. 2008;29(10):1872‒1877.
  18.  Calli C, Kitis O, Yunten N, et al. Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol. 2006;58(3):393‒403.
  19. Bulakbasi N, Kocaoglu M, Ors F, et al. Combination of Single-Voxel Proton MR Spectroscopy and Apparent Diffusion Coefficient Calculation in the Evaluation of Common Brain Tumors. AJNR Am J Neuroradiol. 2003;24(2):225‒233.
  20. Kwee TC, Galban CJ, Tsien C, et al. Comparison of apparent diffusion coefficients and distributed diffusion coefficients in high-grade gliomas. J Magn Reson Imaging. 2010;31(3):531‒537.
  21. Yamasaki F, Kurisu K, Satoh K, et al. Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology. 2005;235(3):985‒991.
Creative Commons Attribution License

©2016 Ebied, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.

Citations