Advertisement

Journal of Neuro-Oncology

, Volume 137, Issue 2, pp 259–268 | Cite as

A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model

  • Kai Wang
  • Tingting Ha
  • Xuzhu Chen
  • Shaowu Li
  • Lin Ai
  • Jun Ma
  • Jianping DaiEmail author
Laboratory Investigation

Abstract

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.0-T MRI scanner. The fractional anisotropy (FA) ratios were correlated to the Ki-67 labeling index using the Spearman correlation analysis. A receiver operating characteristic curve (ROC) analysis was established to evaluate parameters with sensitivity and specificity in order to identify the threshold values for predicting tumor infiltration. Significant correlations were observed between the FA ratios and Ki-67 labeling index (r = − 0.865, p < 0.001). The ROC analysis demonstrated that the apparent diffusion coefficient (ADC) and FA ratios could predict 50% of the proliferating cells in the regions of interest (ROI), with a sensitivity of 88.1 and 81.3%, and a specificity of 86.2 and 90.2%, respectively (p < 0.001). Meanwhile, the two ratios could also predict 10% of the proliferating cells in the ROI, with a sensitivity of 82.5 and 94.9%, and a specificity of 100 and 88.9%, respectively (p < 0.001). The present study demonstrated that the FA ratios are closely correlated with the Ki-67 labeling index. Furthermore, both ADC and FA ratios, derived from DTI, were useful for quantitatively predicting the Ki-67 labeling of glioma cells.

Keywords

Glioma Tumor infiltration Rat model Diffusion tensor imaging Ki-67 

Notes

Acknowledgements

We would like to thank Dr. Y. Ren and Dr. X. Shi for their assistance in the animal glioma model establishment and immunohistochemistry staining. This work was supported by National Natural Science Foundation of China (81271541), and Youth Research Fund of Beijing Tiantan Hospital (2016-YQN-02).

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

11060_2017_2734_MOESM1_ESM.tif (451 kb)
Supplementary Figure 1. The T2 image of C6 glioma (F344 rat). It was showed that the C6 glioma had a relative clear border and no peritumoral edema was observed. (TIF 450 KB)
11060_2017_2734_MOESM2_ESM.tif (399 kb)
Supplementary Figure 2. The T2 image of F98 glioma (F344 rat). It was showed that the F98 glioma had a blurred tumor border which could indicate a more infiltrative property, and the peritumoral edema was obvious. (TIF 398 KB)

References

  1. 1.
    Deorah S, Lynch CF, Sibenaller ZA, Ryken TC (2006) Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001. Neurosurg Focus 20:E1.  https://doi.org/10.3171/foc.2006.20.4.E1 CrossRefPubMedGoogle Scholar
  2. 2.
    Stancheva G, Goranova T, Laleva M, Kamenova M, Mitkova A, Velinov N, Poptodorov G, Mitev V, Kaneva R, Gabrovsky N (2014) IDH1/IDH2 but not TP53 mutations predict prognosis in Bulgarian glioblastoma patients. Biomed Res Int 2014:654727.  https://doi.org/10.1155/2014/654727 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO, European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups, National Cancer Institute of Canada Clinical Trials Group (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996.  https://doi.org/10.1056/NEJMoa043330 CrossRefPubMedGoogle Scholar
  4. 4.
    Chan JL, Lee SW, Fraass BA, Normolle DP, Greenberg HS, Junck LR, Gebarski SS, Sandler HM (2002) Survival and failure patterns of high-grade gliomas after three-dimensional conformal radiotherapy. J Clin Oncol 20:1635–1642CrossRefGoogle Scholar
  5. 5.
    Field KM, Drummond KJ, Yilmaz M, Tacey M, Compston D, Gibbs P, Rosenthal MA (2013) Clinical trial participation and outcome for patients with glioblastoma: multivariate analysis from a comprehensive dataset. J Clin Neurosci 20:783–789.  https://doi.org/10.1016/j.jocn.2012.09.013 CrossRefPubMedGoogle Scholar
  6. 6.
    Kong J, Cooper LA, Wang F, Gutman DA, Gao J, Chisolm C, Sharma A, Pan T, Van Meir EG, Kurc TM, Moreno CS, Saltz JH, Brat DJ (2011) Integrative, multimodal analysis of glioblastoma using TCGA molecular data, pathology images, and clinical outcomes. IEEE Trans Biomed Eng 58:3469–3474.  https://doi.org/10.1109/TBME.2011.2169256 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Ma X, Lv Y, Liu J, Wang D, Huang Q, Wang X, Li G, Xu S, Li X (2009) Survival analysis of 205 patients with glioblastoma multiforme: clinical characteristics, treatment and prognosis in China. J Clin Neurosci 16:1595–1598.  https://doi.org/10.1016/j.jocn.2009.02.036 CrossRefPubMedGoogle Scholar
  8. 8.
    Zhang GB, Cui XL, Sui DL, Ren XH, Zhang Z, Wang ZC, Lin S (2013) Differential molecular genetic analysis in glioblastoma multiforme of long- and short-term survivors: a clinical study in Chinese patients. J Neurooncol 113:251–258.  https://doi.org/10.1007/s11060-013-1102-x CrossRefPubMedGoogle Scholar
  9. 9.
    Burger PC, Dubois PJ, Schold SC Jr, Smith KR Jr, Odom GL, Crafts DC, Giangaspero F (1983) Computerized tomographic and pathologic studies of the untreated, quiescent, and recurrent glioblastoma multiforme. J Neurosurg 58:159–169.  https://doi.org/10.3171/jns.1983.58.2.0159 CrossRefPubMedGoogle Scholar
  10. 10.
    Burger PC, Heinz ER, Shibata T, Kleihues P (1988) Topographic anatomy and CT correlations in the untreated glioblastoma multiforme. J Neurosurg 68:698–704CrossRefGoogle Scholar
  11. 11.
    Lunsford LD, Martinez AJ, Latchaw RE (1986) Magnetic resonance imaging does not define tumor boundaries. Acta Radiol Suppl 369:154–156PubMedGoogle Scholar
  12. 12.
    Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34:463–469CrossRefGoogle Scholar
  13. 13.
    Wallner KE, Galicich JH, Krol G, Arbit E, Malkin MG (1989) Patterns of failure following treatment for glioblastoma multiforme and anaplastic astrocytoma. Int J Radiat Oncol Biol Phys 16:1405–1409CrossRefGoogle Scholar
  14. 14.
    Deng Z, Yan Y, Zhong D, Yang G, Tang W, Lu F, Xie B, Liu B (2010) Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neurosci 17:1530–1536.  https://doi.org/10.1016/j.jocn.2010.03.060 CrossRefPubMedGoogle Scholar
  15. 15.
    Durst CR, Raghavan P, Shaffrey ME, Schiff D, Lopes MB, Sheehan JP, Tustison NJ, Patrie JT, Xin W, Elias WJ, Liu KC, Helm GA, Cupino A, Wintermark M (2014) Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 56:107–115.  https://doi.org/10.1007/s00234-013-1308-9 CrossRefPubMedGoogle Scholar
  16. 16.
    Law M, Yang S, Babb JS, Knopp EA, Golfinos JG, Zagzag D, Johnson G (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25:746–755PubMedGoogle Scholar
  17. 17.
    Price SJ, Jena R, Burnet NG, Carpenter TA, Pickard JD, Gillard JH (2007) Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur Radiol 17:1675–1684.  https://doi.org/10.1007/s00330-006-0561-2 CrossRefPubMedGoogle Scholar
  18. 18.
    Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Pena A, Pickard JD, Carpenter TA, Gillard JH (2006) Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 27:1969–1974PubMedGoogle Scholar
  19. 19.
    Provenzale JM, McGraw P, Mhatre P, Guo AC, Delong D (2004) Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 232:451–460.  https://doi.org/10.1148/radiol.2322030959 CrossRefPubMedGoogle Scholar
  20. 20.
    Wright AJ, Fellows G, Byrnes TJ, Opstad KS, McIntyre DJ, Griffiths JR, Bell BA, Clark CA, Barrick TR, Howe FA (2009) Pattern recognition of MRSI data shows regions of glioma growth that agree with DTI markers of brain tumor infiltration. Magn Reson Med 62:1646–1651.  https://doi.org/10.1002/mrm.22163 CrossRefPubMedGoogle Scholar
  21. 21.
    de Groot JF (2015) High-grade gliomas. Continuum (Minneap Minn) 21:332–344.  https://doi.org/10.1212/01.CON.0000464173.58262.d9 CrossRefGoogle Scholar
  22. 22.
    Paw I, Carpenter RC, Watabe K, Debinski W, Lo HW (2015) Mechanisms regulating glioma invasion. Cancer Lett 362:1–7.  https://doi.org/10.1016/j.canlet.2015.03.015 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hoefnagels FW, De Witt Hamer P, Sanz-Arigita E, Idema S, Kuijer JP, Pouwels PJ, Barkhof F, Vandertop WP (2014) Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map. J Neurooncol 120:187–198.  https://doi.org/10.1007/s11060-014-1544-9 CrossRefPubMedGoogle Scholar
  24. 24.
    Price SJ, Burnet NG, Donovan T, Green HA, Pena A, Antoun NM, Pickard JD, Carpenter TA, Gillard JH (2003) Diffusion tensor imaging of brain tumours at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol 58:455–462CrossRefGoogle Scholar
  25. 25.
    Kim S, Pickup S, Hsu O, Poptani H (2008) Diffusion tensor MRI in rat models of invasive and well-demarcated brain tumors. NMR Biomed 21:208–216.  https://doi.org/10.1002/nbm.1183 CrossRefPubMedGoogle Scholar
  26. 26.
    Lope-Piedrafita S, Garcia-Martin ML, Galons JP, Gillies RJ, Trouard TP (2008) Longitudinal diffusion tensor imaging in a rat brain glioma model. NMR Biomed 21:799–808.  https://doi.org/10.1002/nbm.1256 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Wang S, Zhou J (2012) Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology. J Comput Assist Tomogr 36:739–744.  https://doi.org/10.1097/RCT.0b013e3182685436 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Mathieu D, Lecomte R, Tsanaclis AM, Larouche A, Fortin D (2007) Standardization and detailed characterization of the syngeneic Fischer/F98 glioma model. Can J Neurol Sci 34:296–306CrossRefGoogle Scholar
  29. 29.
    Barth RF, Kaur B (2009) Rat brain tumor models in experimental neuro-oncology: the C6, 9L, T9, RG2, F98, BT4C, RT-2 and CNS-1 gliomas. J Neurooncol 94:299–312.  https://doi.org/10.1007/s11060-009-9875-7 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Christoforidis GA, Yang M, Kontzialis MS, Larson DG, Abduljalil A, Basso M, Yang W, Ray-Chaudhury A, Heverhagen J, Knopp MV, Barth RF (2009) High resolution ultra high field magnetic resonance imaging of glioma microvascularity and hypoxia using ultra-small particles of iron oxide. Invest Radiol 44:375–383.  https://doi.org/10.1097/RLI.0b013e3181a8afea CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ (1987) Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg 66:865–874.  https://doi.org/10.3171/jns.1987.66.6.0865 CrossRefPubMedGoogle Scholar
  32. 32.
    Bulakbasi N, Kocaoglu M, Ors F, Tayfun C, Ucoz T (2003) Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR Am J Neuroradiol 24:225–233PubMedGoogle Scholar
  33. 33.
    Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22:1081–1088PubMedGoogle Scholar
  34. 34.
    Lam WW, Poon WS, Metreweli C (2002) Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin Radiol 57:219–225.  https://doi.org/10.1053/crad.2001.0741 CrossRefPubMedGoogle Scholar
  35. 35.
    Stadnik TW, Chaskis C, Michotte A, Shabana WM, van Rompaey K, Luypaert R, Budinsky L, Jellus V, Osteaux M (2001) Diffusion-weighted MR imaging of intracerebral masses: comparison with conventional MR imaging and histologic findings. AJNR Am J Neuroradiol 22:969–976PubMedGoogle Scholar
  36. 36.
    Sundgren PC, Fan X, Weybright P, Welsh RC, Carlos RC, Petrou M, McKeever PE, Chenevert TL (2006) Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging 24:1131–1142.  https://doi.org/10.1016/j.mri.2006.07.008 CrossRefPubMedGoogle Scholar
  37. 37.
    Tropine A, Vucurevic G, Delani P, Boor S, Hopf N, Bohl J, Stoeter P (2004) Contribution of diffusion tensor imaging to delineation of gliomas and glioblastomas. J Magn Reson Imaging 20:905–912.  https://doi.org/10.1002/jmri.20217 CrossRefPubMedGoogle Scholar
  38. 38.
    Sternberg EJ, Lipton ML, Burns J (2014) Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors. AJNR Am J Neuroradiol 35:439–444.  https://doi.org/10.3174/ajnr.A3702 CrossRefPubMedGoogle Scholar
  39. 39.
    Fudaba H, Shimomura T, Abe T, Matsuta H, Momii Y, Sugita K, Ooba H, Kamida T, Hikawa T, Fujiki M (2014) Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. AJNR Am J Neuroradiol 35:2091–2098.  https://doi.org/10.3174/ajnr.A4018 CrossRefPubMedGoogle Scholar
  40. 40.
    Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109.  https://doi.org/10.1007/s00401-007-0243-4 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Alexiou GA, Zikou A, Tsiouris S, Goussia A, Kosta P, Papadopoulos A, Voulgaris S, Kyritsis AP, Fotopoulos AD, Argyropoulou MI (2014) Correlation of diffusion tensor, dynamic susceptibility contrast MRI and (99 m)Tc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin Neurol Neurosurg 116:41–45.  https://doi.org/10.1016/j.clineuro.2013.11.003 CrossRefPubMedGoogle Scholar
  42. 42.
    Kinoshita M, Hashimoto N, Goto T, Kagawa N, Kishima H, Izumoto S, Tanaka H, Fujita N, Yoshimine T (2008) Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage 43:29–35.  https://doi.org/10.1016/j.neuroimage.2008.06.041 CrossRefPubMedGoogle Scholar
  43. 43.
    Yin Y, Tong D, Liu XY, Yuan TT, Yan YZ, Ma Y, Zhao R (2012) Correlation of apparent diffusion coefficient with Ki-67 in the diagnosis of gliomas. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 34:503–508.  https://doi.org/10.3881/j.issn.1000-503X.2012.05.012 CrossRefPubMedGoogle Scholar
  44. 44.
    Zikou AK, Alexiou GA, Kosta P, Goussia A, Astrakas L, Tsekeris P, Voulgaris S, Malamou-Mitsi V, Kyritsis AP, Argyropoulou MI (2012) Diffusion tensor and dynamic susceptibility contrast MRI in glioblastoma. Clin Neurol Neurosurg 114:607–612.  https://doi.org/10.1016/j.clineuro.2011.12.022 CrossRefPubMedGoogle Scholar
  45. 45.
    Johannessen AL, Torp SH (2006) The clinical value of Ki-67/MIB-1 labeling index in human astrocytomas. Pathol Oncol Res 12:143–147CrossRefGoogle Scholar
  46. 46.
    Jena R, Price SJ, Baker C, Jefferies SJ, Pickard JD, Gillard JH, Burnet NG (2005) Diffusion tensor imaging: possible implications for radiotherapy treatment planning of patients with high-grade glioma. Clin Oncol (R Coll Radiol) 17:581–590CrossRefGoogle Scholar
  47. 47.
    Kimura T, Ohkubo M, Igarashi H, Kwee IL, Nakada T (2007) Increase in glutamate as a sensitive indicator of extracellular matrix integrity in peritumoral edema: a 3.0-tesla proton magnetic resonance spectroscopy study. J Neurosurg 106:609–613.  https://doi.org/10.3171/jns.2007.106.4.609 CrossRefPubMedGoogle Scholar
  48. 48.
    Lu S, Ahn D, Johnson G, Cha S (2003) Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 24:937–941PubMedGoogle Scholar
  49. 49.
    Grobben B, De Deyn PP, Slegers H (2002) Rat C6 glioma as experimental model system for the study of glioblastoma growth and invasion. Cell Tissue Res 310:257–270.  https://doi.org/10.1007/s00441-002-0651-7 CrossRefPubMedGoogle Scholar
  50. 50.
    Tonn JC (2002) Model systems in neurooncology. Acta Neurochir Suppl 83:79–83PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Nuclear Medicine, Beijing Tiantan HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  2. 2.Department of Radiology, Shougang HospitalPeking UniversityBeijingPeople’s Republic of China
  3. 3.Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  4. 4.Beijing Neurosurgical InstituteCapital Medical UniversityBeijingPeople’s Republic of China

Personalised recommendations