Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Abstract

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

Dynamic 11C-Methionine PET-CT: Prognostic Factors for Disease Progression and Survival in Patients with Suspected Glioma Recurrence

“…In the last decade, the application of artificial intelligence and radiomics has gained increasing attention in medical imaging, including functional dynamic PET-CT. Indeed, the ability of principal component analysis to extract meaningful parametric maps from dynamic 11 C-methionine PET-CT images has been recently investigated in glioma, highlighting the added value of dynamic over static PET acquisition in oncology [ 57 ].…”

Effects of Carbidopa Premedication on 18F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis

“…), using initial dynamic frames to identify early vascular phases [ 19 ]. The spill-out coefficient was estimated at 0.35 by simulation and used to correct from partial volume effect on blood TACs [ 20 ]. These blood TACs were then fitted to the peak, using linear interpolation followed by a tri-exponential function after the peak [ 21 ].…”

Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

“…TACs representing the evolution of the arterial blood activity were obtained from internal carotid VOIs and were fitted using linear interpolation to the peak followed by a tri-exponential function after the peak ( 31 ). The fitted blood TAC was then corrected for the spill-out effect, the coefficient of which had been estimated as 0.51 ( 32 ). In the case of 18 F-FDOPA, the plasma 18 F-FDOPA TAC was obtained after correcting for OMFD and other METS generated in the peripheral tissues ( 19 ).…”