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.
View Article and Find Full Text PDFReaction-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).
View Article and Find Full Text PDFRecent 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.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
June 2021
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 [F]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.