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Contribution of Diffusion MRI in the differentiation of intracranial meningiomas and correlation with immunohistochem

Contribution of Diffusion MRI in the differentiation of intracranial meningiomas and correlation with immunohistochemistry. L Castelletti, M Bendini , L Saitta , L Bonzano , F Di Paola, L Castellan. Meningiomas.

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Contribution of Diffusion MRI in the differentiation of intracranial meningiomas and correlation with immunohistochem

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  1. Contribution of Diffusion MRI in the differentiation of intracranial meningiomas and correlation with immunohistochemistry L Castelletti, M Bendini, L Saitta, L Bonzano, F Di Paola, L Castellan Departments of Neuroradiology S. Martino University Hospital - Genoa Cà Foncello Hospital - Treviso Italy

  2. Meningiomas • the most frequent extra-axial brain tumours (24-30% of all primary intracranial tumors); • symptoms highly dependent on tumor site and size; • 30% asymptomatic (first diagnosis on CT scans obtained for different reasons).

  3. WHO classification and recurrence rate recurrence I Typical 70-80% 7-20% II Atypical 15-20% 29-43% III Anaplastic 1-3% <80%

  4. Histological grade is the most relevant prognostic factor and is also associated with survival period • Atipical with a mortality rate of 21% at 5 years • Anaplastic with a median survival time of 2 years Bruna J, Brell M, Ferrer I, Gimene-Bonafe P, Tortosa A Ki-67 proliferative indexpredictsclinicaloutcome in patientwithatypical or anaplasticmeningiomaNeuropathology 2007; 27,114-120

  5. Neuroradiological conventional imaging • High sensibility (99%) • Low specificity for histolgical grading Neuroradiological non conventional imaging The contribution of diffusion-weighted MR imaging (DWI) in differentianting typical meningiomas from atypical/malignant meningiomas varies in the literature

  6. The purpose of our study was to investigate the contribution of diffusion-weighted MR imaging to differentiating typical and atypical meningiomas. A preoperative reliable characterization of meningiomas would be of paramount importance for a tailored surgical or treatment planning.

  7. Materials and Methods DWI b values: 0/500/1000 ADC ADC maps were automatically generated on Siemens and manually with the Functool software program on GE. The mean ADC values was calculated by means of software Analyze Mayo Clinic.

  8. The mean ADC value was calculated from a region of interest (ROI), manually drawn within the solid part of the tumour. ROIs were traced avoiding cystic and necrotic areas, which were identified on CT, T2* and post-contrast T1 images on MR. ROI area ranged between 0.5 and 30 mm2.

  9. Pathological data • the WHO 2007 grading criteria (mostly based on morphological cellular atypia) [qualitative analysis]; • proliferative index determined by Ki 67 (antigen identified by monoclonal Ab)[semi-quantitative analysis] • Statistical Analysis • ADC WHO Grading (U Mann-Whitney) • ADC Ki-67 (Spearman’s rho)

  10. Results *24 meningothelial; 13 transitional; 11 fibrous; 3 angiomatous, 3 psammomatosus; 1 mixomatous and 1 secretory

  11. ADC 0.94x10-3 mm2/sec WHO I ADC 0.64x10-3 mm2/sec WHO II

  12. Test U Mann-Whitney: ADC significantly different between typical and atypical/malignant (p<0.001)

  13. Spearman’s rho: significant linear correlation between ADC and KI67(%) (r = -0.65, p<0.001)

  14. In these studies the different results of these could be related to: • limited series of patients and a few high grade meningiomas in all studies • different MR equipment • different post-processing techniques (ROI placement) • only our study has investigated correlation with Ki-67

  15. Conclusions • Our series suggests significant correlation between ADC and tumor grading as reported in some previous study • ADC values correlate with Ki67 index • Data could be of paramount importance for treatment planning and prediction of prognosis

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