[Nosological analysis of MRI tissue perfusion parameters obtained using the unicompartmental and pharmacokinetic models in cerebral glioblastomas].
| Autor: | Revert Ventura AJ; Servicio de Radiología, Hospital de Manises, Manises, Spain. ajrevert@telefonica.net, Sanz-Requena R, Martí-Bonmatí L, Jornet J, Piquer J, Cremades A, Carot JM |
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| Jazyk: | Spanish; Castilian |
| Zdroj: | Radiologia [Radiologia] 2010 Sep-Oct; Vol. 52 (5), pp. 432-41. Date of Electronic Publication: 2010 Jul 23. |
| DOI: | 10.1016/j.rx.2010.03.017 |
| Abstrakt: | Objectives: To classify the tumor areas in patients with grade IV astrocytoma by calculating and statistically analyzing quantitative MRI perfusion parameters. Material and Methods: We applied two models of MRI perfusion, the unicompartmental and the pharmacokinetic models, in 15 patients diagnosed with grade IV astrocytoma. In the unicompartmental model, we quantified cerebral blood volume (CBV), mean transit time (MTT), and cerebral blood flow (CBF). In the pharmacokinetic model, we measured the permeability constant (K(trans)), the extraction coefficient (k(ep)), the fraction of the volume in the interstitial space (v(e)), the fraction of the volume in the vessels (v(p)), the permeability in the first pass (K(fp)), and the vascular volume in the first pass (v(pfp)). For each parameter, histograms were obtained for the total tumor area, for the peritumoral area, and for the healthy tissue. The statistical analysis included an analysis of variance for each parameter and a discriminant analysis. Results: The most significant differences between the regions were obtained with CBV, CBF, K(trans), and v(pfp); of these, CBV had the best results. The best classificatory function on the discriminant analysis was the combination of K(trans) and CBV. The analysis of the shape of the histogram showed statistically significant differences for the kurtosis of K(trans) and k(ep), as well as for the skewness of CBV, CBF, K(trans), and v(pfp). Conclusion: When parameters are considered individually, CBV is the one that best enables differentiation between tumor, peritumoral, and healthy tissue. The classificatory function generated from CBV and K(trans) results in improved classification by areas. (Copyright © 2009 SERAM. Published by Elsevier Espana. All rights reserved.) |
| Databáze: | MEDLINE |
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