Internal radiation dose assessment of radiopharmaceuticals prepared with cyclotron-produced 99m Tc

Technetium-99m (99m Tc) is the radioisotope most widely used in diagnostic nuclear medicine. It is readily available from 99 Mo/99m Tc generators as the \u3b2- decay product of the 99 Mo (T\ubd =66 h) parent nuclide. This latter is obtained as a fission product in nuclear reactors by neutron-induced reactions on highly enriched uranium. Alternative production routes, such as direct reactions using proton beams on specific target materials [100 Mo(p,2n)99m Tc], have the potential to be both reliable and relatively cost-effective. However, results showed that the 99m Tc extracted from proton-bombarded 100 Mo-enriched targets contains small quantities of several Tc radioisotopes (93m Tc, 93 Tc, 94 Tc, 94m Tc, 95 Tc, 95m Tc 96 Tc and 97m Tc). The aim of this work was to estimate the dose increase (DI) due to the contribution of Tc radioisotopes generated as impurities, after the intravenous injection of four radiopharmaceuticals prepared with cyclotron-produced 99m Tc (CP-99m Tc) using 99.05% 100 Mo-enriched metallic targets

Physical radiotherapy treatment planning based on functional PET/CT data

Positron emission tomography (PET) provides molecular information about the tumor microenvironment in addition to anatomical imaging. Hence, it seems to be highly beneficial to integrate PET data into radiotherapy (RT) treatment planning. Functional PET images can be used in RT planning following different strategies, with different orders of complexity. In a first instance, PET imaging data can be used for better target volume delineation. A second strategy, dose painting by contours (DPBC), consists of creating an additional PET-based target volume which will then be treated with a higher dose level. In contrast, dose painting by numbers (DPBN) aims for a locally varying dose prescription according to the variation of the PET signal. For both dose painting approaches, isotoxicity planning strategies should be applied in order not to compromise organs at risk compared to conventional modern RT treatment. In terms of physical dose painting treatment planning, several factors that may introduce limitations and uncertainties are of major importance. These are the PET voxel size, uncertainties due to image acquisition and reconstruction, a reproducible image registration, inherent biological uncertainties due to biological and chemical tracer characteristics, accurate dose calculation algorithms and radiation delivery techniques able to apply highly modulated dose distributions. Further research is necessary in order to investigate these factors and their influence on dose painting treatment planning and delivery thoroughly. To date, dose painting remains a theoretical concept which needs further validation. Nevertheless, molecular imaging has the potential to significantly improve target volume delineation and might also serve as a basis for treatment alteration in the future

The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation

In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image preprocessing, standardise the formulation and nomenclature of 169 radiomic features and share two benchmark digital phantoms for software calibration. However, to better assess the concordance of radiomic tools, more heterogeneous phantoms are needed. We created two digital phantoms, called ImSURE phantoms, having isotropic and anisotropic voxel size, respectively, and 90 regions of interest (ROIs) each. To use these phantoms, we designed a systematic feature extraction workflow including 919 different feature values (obtained from the 169 IBSI-standardised features considering all possible combinations of feature aggregation and intensity discretisation methods). The ImSURE phantoms will allow to assess the concordance of radiomic software depending on interpolation, discretisation and aggregation methods, as well as on ROI volume and shape. Eventually, we provide the feature values extracted from these phantoms using five open-source IBSI-compliant software

Quantitative analysis of image metrics for reduced and standard dose pediatric 18F-FDG PET/MRI examinations

The study performs a comprehensive analysis of image metrics to objectively support the reduction of injected activity in pediatric oncology 18F-FDG PET/MR (18F-fludeoxyglucose PET/MR) examinations. Contrast-to-Noise Ratio (CNR), Normalized Noise (NN), tumor burden, and standardized uptake value (SUV) parameters stability were investigated to robustly define the acceptable reduced activity level that preserves the clinical utility of images, considering different PET applications

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging

AimTo study the feasibility of radiomic analysis of baseline [F-18]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.Material and methodsSeventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PG(whole): whole PG; PG(41%): prostate having standardized uptake value - SUV > 0.41*SUVmax; PG(2.5): prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.ResultsThe median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG(2.5) and 0.4 discretization, for which the median test AUC was 0.78.ConclusionRadiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR