PET/CT performed with nonenhanced CT is more sensitive and specific than is contrast-enhanced CT for evaluation of lymph node and organ involvement, especially regarding exclusion of disease, in patients with Hodgkin disease and high-grade non-Hodgkin lymphoma.

Rapidly growing cells show an increased demand for nutrients and vitamins. The objective of our work is to exploit the supply route of vitamin B12 to deliver new derivatives of this vital vitamin to hyperproliferative cells. To date, radiolabeled ( 57 Co and 111 In) vitamin B12 derivatives showed labeling of tumor tissue but also undesired high accumulation of radioactivity in normal tissue. By abolishing the interaction of a tailored vitamin B12 derivative to its transport protein transcobalamin II and therefore interrupting transcobalamin II receptor and megalin mediated uptake in normal tissue, preferential accumulation of a radiolabeled vitamin in cancer tissue could be accomplished. We identified transcobalamin I on tumors as a possible new receptor for this preferential accumulation of vitamin-mediated targeting. The low systemic distribution of radioactivity and the high tumor to blood ratio opens the possibility of a more successful clinical application of vitamin B12 for imaging or therapy.

In patients with FDG-avid bone lesions, FDG-PET is superior to CT alone or in combination with unilateral BMB in detecting bone marrow involvement, leading to upstaging in a relevant proportion of patients.

IntroductionAmino-acids positron emission tomography (PET) is increasingly used in the diagnostic workup of patients with gliomas, including differential diagnosis, evaluation of tumor extension, treatment planning and follow-up. Recently, progresses of computer vision and machine learning have been translated for medical imaging. Aim was to demonstrate the feasibility of an automated 18F-fluoro-ethyl-tyrosine (18F-FET) PET lesion detection and segmentation relying on a full 3D U-Net Convolutional Neural Network (CNN).MethodsAll dynamic 18F-FET PET brain image volumes were temporally realigned to the first dynamic acquisition, coregistered and spatially normalized onto the Montreal Neurological Institute template. Ground truth segmentations were obtained using manual delineation and thresholding (1.3 x background). The volumetric CNN was implemented based on a modified Keras implementation of a U-Net library with 3 layers for the encoding and decoding paths. Dice similarity coefficient (DSC) was used as an accuracy measure of segmentation.ResultsThirty-seven patients were included (26 [70%] in the training set and 11 [30%] in the validation set). All 11 lesions were accurately detected with no false positive, resulting in a sensitivity and a specificity for the detection at the tumor level of 100%. After 150 epochs, DSC reached 0.7924 in the training set and 0.7911 in the validation set. After morphological dilatation and fixed thresholding of the predicted U-Net mask a substantial improvement of the DSC to 0.8231 (+ 4.1%) was noted. At the voxel level, this segmentation led to a 0.88 sensitivity [95% CI, 87.1 to, 88.2%] a 0.99 specificity [99.9 to 99.9%], a 0.78 positive predictive value: [76.9 to 78.3%], and a 0.99 negative predictive value [99.9 to 99.9%].ConclusionsWith relatively high performance, it was proposed the first full 3D automated procedure for segmentation of 18F-FET PET brain images of patients with different gliomas using a U-Net CNN architecture.

H1N1 vaccination can cause false-positive FDG PET/CT findings, when administered less than 14 days before the test, with the highest probability if the vaccination was administered less than 8 days ago. Increased FDG activity in the ipsilateral deltoid muscle is a key finding for accurate interpretation of increased FDG activity in axillary lymph nodes.