[18F]-JK-PSMA-7 and [18F]-FDG tumour PET uptake in treated xenograft human prostate cancer model in mice.

PURPOSE: This preclinical study aims to evaluate the extent to which a change in prostate-specific membrane antigen (PSMA) expression of castration-resistant prostate cancer (CRPC) following standard treatment is reflected in [18F]JK-PSMA-7 PET/CT. METHODS: Castrated mice supplemented with testosterone implant were xenografted with human LNCaP CRPC. After appropriate tumour growth, androgen deprivation therapy (ADT) was carried out by the removal of the implant followed by a single injection of docetaxel (400 µg/20-g mouse) 2 weeks later. [18F]JK-PSMA-7 PET/CT were performed before ADT, then before and at days 12, 26, 47 and 69 after docetaxel administration. The [18F]JK-PSMA-7 PET data were compared to corresponding unspecific metabolic [18F]FDG PET/CT and ex vivo quantification of PSMA expression estimated by flow cytometry on repeated tumour biopsies. RESULTS: ADT alone had no early effect on LNCaP tumours that pursued their progression. Until day 12 post-docetaxel, the [18F]JK-PSMA7 uptake was significantly higher than that of [18F]FDG, indicating the persistence of PSMA expression at those time points. From day 26 onwards when the tumours were rapidly expanding, both [18F]JK-PSMA7 and [18F]FDG uptake continuously decreased although the decrease in [18F]JK-PSMA uptake was markedly faster. The fraction of PSMA-positive cells in tumour biopsies decreased similarly over time to reach a non-specific level after the same time period. CONCLUSION: Applying PSMA-based imaging for therapy monitoring in patients with CRPC should be considered with caution since a reduction in [18F]JK-PSMA-7 PET uptake after successive ADT and chemotherapy may be related to downregulation of PSMA expression in dedifferentiated and rapidly proliferating tumour cells.

Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?

Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumors. However, ill-posedness of the initialization at diagnosis time and parameter estimation of such models have restrained their clinical use as a personalized predictive tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address commonly encountered pitfalls in the field. Based on 1200 synthetic tumors grown over real brain geometries derived from magnetic resonance (MR) data of six healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumor cell-density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also demonstrate the ability of DCNNs to accurately estimate the individual diffusivity and proliferation parameters of the model. From this knowledge, the spatio-temporal evolution of the tumor cell-density distribution at later time points can ultimately be precisely captured using the model. We finally show the applicability of our approach to MR data of a real glioblastoma patient. This approach may open the perspective of a clinical application of reaction-diffusion growth models for tumor prognosis and treatment planning.

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

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.

Initial Condition Assessment for Reaction-Diffusion Glioma Growth Models: A Translational MRI-Histology (In)Validation Study.

Reaction-diffusion models have been proposed for decades to capture the growth of gliomas. Nevertheless, these models require an initial condition: the tumor cell density distribution over the whole brain at diagnosis time. Several works have proposed to relate this distribution to abnormalities visible on magnetic resonance imaging (MRI). In this work, we verify these hypotheses by stereotactic histological analysis of a non-operated brain with glioblastoma using a 3D-printed slicer. Cell density maps are computed from histological slides using a deep learning approach. The density maps are then registered to a postmortem MR image and related to an MR-derived geodesic distance map to the tumor core. The relation between the edema outlines visible on T2-FLAIR MRI and the distance to the core is also investigated. Our results suggest that (i) the previously proposed exponential decrease of the tumor cell density with the distance to the core is reasonable but (ii) the edema outlines would not correspond to a cell density iso-contour and (iii) the suggested tumor cell density at these outlines is likely overestimated. These findings highlight the limitations of conventional MRI to derive glioma cell density maps and the need for other initialization methods for reaction-diffusion models to be used in clinical practice.