Axel Van Der Gucht

Gastrin-releasing peptide receptor antagonists have promise in theranostics of several highly incident tumours, including prostate and breast. This study presents the first human dosimetry of ⁶⁸Ga-NODAGA-MJ9 in the first five consecutive patients with recurrent prostate cancer included in a dual-tracer positron emission tomography (PET) protocol. Five male patients with biochemical relapse of prostate adenocarcinoma underwent four whole-body time-of-flight PET/CT scans within 2 h after tracer injection. To be used as input in OLINDA/EXM 2.0, time-integrated activity coefficients were derived from manually drawn regions of interest over the following body regions: brain, thyroid, lungs, heart, liver, gallbladder, spleen, stomach, kidneys, adrenals, red marrow, pancreas, intestines, urinary bladder and whole body. Organ absorbed doses and effective dose (ED) were calculated with OLINDA/EXM 2.0 using the NURBS voxelized phantoms adjusted to the ICRP-89 organ masses and ICRP103 tissue-weighting factors. Additional absorbed dose estimations were performed with OLINDA/EXM 1.1 to be comparable with similar previous publications. The body regions receiving the highest absorbed doses were the pancreas, the urinary bladder wall, the small intestine and the kidneys (260, 69.8, 38.8 and 34.8 μGy/MBq respectively). The ED considering a 30-min urinary voiding cycle was 17.6 μSv/MBq in male patients. The increment of voiding time interval produced a significant increase of absorbed doses in bladder, prostate and testes, as well as an increase of ED. ED also increased if calculated with OLINDA/EXM 1.1. These results have been discussed in view of similar publications on bombesin analogues or on other commonly used theranostic peptides. The pancreas is the most irradiated organ after the injection of ⁶⁸Ga-NODAGA-MJ9, followed by the urinary bladder wall, the small intestine and the kidneys. ED is in the same range of other common ⁶⁸Ga-labelled peptides. Differences with similarly published studies on bombesin analogues exist, and are mainly dependent on the methodology used for absorbed dose calculations. Clinicaltrial.Gov identifier: NCT02111954 , posted on 11/042014.

Aim was to develop a full automatic clustering approach of the time-activity curves (TAC) from dynamic ¹⁸F-FET PET and evaluate its association with IDH1 mutation status and survival in patients with gliomas. Thirty-seven patients (mean age: 45±13 y) with newly diagnosed gliomas and dynamic ¹⁸F-FET PET before any histopathologic investigation or treatment were retrospectively included. Each dynamic ¹⁸F-FET PET was realigned to the first image and spatially normalized in the Montreal Neurological Institute template. A tumor mask was semi-automatically generated from Z-score maps. Each brain tumor voxel was clustered in one of the 3 following centroids using dynamic time warping and k-means clustering (centroid #1: slowly increasing slope; centroid #2: rapidly increasing followed by slowly decreasing slope; and centroid #3: rapidly increasing followed by rapidly decreasing slope). The percentage of each dynamic ¹⁸F-FET TAC within tumors and other conventional ¹⁸F-FET PET parameters (maximum and mean tumor-to-brain ratios [TBR_max and TBR_mean], time-to-peak [TTP] and slope) was compared between wild-type and IDH1 mutant tumors. Their prognostic value was assessed in terms of progression free-survival (PFS) and overall survival (OS) by Kaplan-Meier estimates. Twenty patients were IDH1 wild-type and 17 IDH1 mutant. Higher percentage of centroid #1 and centroid #3 within tumors were positively (P = 0.016) and negatively (P = 0.01) correlated with IDH1 mutated status. Also, TBR_max, TBR_mean, TTP, and slope discriminated significantly between tumors with and without IDH1 mutation (P range 0.01 to 0.04). Progression occurred in 22 patients (59%) at a median of 13.1 months (7.6–37.6 months) and 13 patients (35%) died from tumor progression. Patients with a percentage of centroid #1 > 90% had a longer survival compared with those with a percentage of centroid #1 < 90% (P = 0.003 for PFS and P = 0.028 for OS). This remained significant after stratification on IDH1 mutation status (P = 0.029 for PFS and P = 0.034 for OS). Compared to other conventional ¹⁸F-FET PET parameters, TTP and slope were associated with PFS and OS (P range 0.009 to 0.04). Based on dynamic ¹⁸F-FET PET acquisition, we developed a full automatic clustering approach of TAC which appears to be a valuable noninvasive diagnostic and prognostic marker in patients with gliomas.

Amino-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). All 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. Thirty-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%]. With 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.

Signature of survival: a 18 F-FDG PET based whole-liver radiomic analysis predicts survival after 90 Y-TARE for hepatocellular carcinoma Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma

Background and purpose: Studies of the effects of nicotine on motor symptoms in Parkinson's disease (PD) brought out discordant results. The aim of the present study was to evaluate the efficacy and safety of high doses of transdermal nicotine on motor symptoms in PD.Methods: Forty PD patients were randomly assigned to a treated and untreated arm in an open‐label study. Treated patients received increasing doses of nicotine to reach 90 mg/day by 11 weeks. This dosage was maintained for 28 weeks (W39) and then reduced over 6 weeks. Final evaluation was performed 6 weeks after washout. The main outcome measure was the OFF‐DOPA Unified Parkinson's Disease Rating Scale (UPDRS) motor score measured on video recordings by raters blinded to the medication status of the patients.Results: There was no significant difference in OFF‐DOPA UPDRS motor scores between the nicotine‐treated and non‐treated groups, neither at W39 (19.4 ± 9.3 vs. 21.5 ± 14.2) nor considering W39 differences from baseline (−1.5 ± 12.1 vs. +0.9 ± 12.1). The 39‐item Parkinson's disease questionnaire scores decreased in nicotine‐treated patients and increased in non‐treated patients, but the difference was not significant. Overall tolerability was acceptable, and 12/20 treated patients reached the maximal dosage.Conclusions: High doses of transdermal nicotine were tolerated, but our study failed to demonstrate significant improvement in UPDRS motor scores. Improvement in unblinded secondary outcomes (UPDRS‐II, UPDRS‐IV, doses of l‐DOPA equivalents) suggest a possible benefit for patients treated with nicotine, which should be confirmed in larger double blind, placebo‐controlled studies.

Although several functional studies have demonstrated that positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET/CT) appears to be efficient to identify a cerebral substrate in patients with known macrophagic myofasciitis (MMF), the predictive value of this imaging technique for MMF remains unclear. We presented data and images of a 46-year-old woman. The patient was referred to our center for suspected MMF due to diffuse arthromyalgias and cognitive disorder (involving an impairment of visual selective attention and a weakness in executive functions revealed by neuropsychological assessment) which occurred few years after last vaccine injections. After a first negative deltoid muscle biopsy, a brain 18F-FDG PET/CT was performed and revealed the known spatial pattern of a cerebral glucose hypometabolism involving occipital cortex, medial temporal areas, and cerebellum. Given the clinical suspicion of MMF and brain 18F-FDG PET/CT findings, a 2nd deltoid muscle biopsy was performed and confirmed the diagnosis of MMF with typical histopathological features. This case highlights the predictive value of brain 18F-FDG PET/CT as a noninvasive imaging tool for MMF diagnosis, even when muscle biopsy result comes back negative.

Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF ¹⁸F-FDG brain profiles. ¹⁸F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using ¹⁸F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.