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Recent works have demonstrated the added value of dynamic amino acid positronemission tomography (PET) for glioma grading and genotyping, biopsy targeting,and recurrence diagnosis. However, most of these studies are exclusively basedon hand-crafted qualitative or semi-quantitative dynamic features extractedfrom the mean time activity curve (TAC) within predefined volumes. Voxelwisedynamic PET data analysis could instead provide a better insight intointra-tumour heterogeneity of gliomas. In this work, we investigate the abilityof the widely used principal component analysis (PCA) method to extractmeaningful quantitative dynamic features from high-dimensional motion-correcteddynamic [S-methyl-11C]methionine PET data in a first cohort of 20 gliomapatients. By means of realistic numerical simulations, we demonstrate therobustness of our methodology to noise. In a second cohort of 13 gliomapatients, we compare the resulting parametric maps to these provided bystandard one- and two-tissue compartment pharmacokinetic (PK) models. We showthat our PCA model outperforms PK models in the identification of intra-tumouruptake dynamics heterogeneity while being much less computationally expensive.Such parametric maps could be valuable to assess tumour aggressiveness locallywith applications in treatment planning as well as in the evaluation of tumourprogression and response to treatment. This work also provides furtherencouraging results on the added value of dynamic over static analysis of[S-methyl-11C]methionine PET data in gliomas, as previously demonstrated forO-(2-[18F]fluoroethyl)-L-tyrosine.

Voxelwise principal component analysis of dynamic [S-methyl-11C]methionine PET data in glioma patients