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The increasing use of 177Lu-labelled agents for targeted radionuclide cancer therapy highlights the radiation protection challenges in the management of radioactive waste due to 177mLu impurities. To ensure safe disposal, compliance with clearance criteria must be verified using calibrated systems, such as Hyper Pure Germanium (HPGe) spectrometers. This work aims to validate a customized GEANT4 model of our HPGe system in Marinelli beaker geometry to propose an operational approach properly quantifying 177Lu and 177mLu in waste samples. The system's efficiency curve was calculated by modelling gamma sources in the energy range of interest and validated by spectra measurements of 177Lu and 99mTc sources. Correction factors accounting for true coincidence summing (TCS) effect were simulated for 177Lu, 177mLu and 99mTc and they were applied to the spectrum measurement of a waste sample model with known activities of 177Lu and 177mLu. Thus, an operational approach for activities quantification was tested comparing the results with the nominal values. No significant differences were observed between simulated and measured efficiency values. TCS correction factors are significant only for 177mLu (1.6 at 112.95 keV and 204.11 keV). Eventually, the proposed framework to quantify 177Lu in a waste sample allowed to estimate the 177Lu and 177mLu component activities within a maximum 16% uncertainty. Results show that the HPGe model could be a powerful resource for a wide range of applications in daily clinical routine and it could be used to build a simple quality assurance program to monitor the detector response constancy in time. [ABSTRACT FROM AUTHOR]

In a clinical contest, it is common to use dedicated phantoms to perform quality assurance test to check the performance of a SPECT system. Some of these phantoms are also used to calibrate the system for dosimetric evaluation of patients undergoing radiometabolic cancer therapy. In this work, a 3D-OSEM reconstructed 177Lu SPECT dataset of a homogeneous cylindrical phantom is described. This dataset was acquired to investigate the variation of the SPECT calibration factor, counts convergence, noise and uniformity by varying the number of subsets and iterations. In particular, the dataset is composed of images reconstructed using five different numbers of subsets and sixteen different numbers of iterations, for a total of 80 different configurations. The dataset is suitable for comparison with other reconstruction algorithms (e.g. FBP, MLEM, etc.) and radionuclides (e.g. technetium, yttrium). In regards to the uniformity issue, the same dataset allows the user to perform radiomic investigations on the influence of the border effect on the reconstructed images. Measurement(s) Calibration Factor • coefficient of variation • Texture Descriptor Technology Type(s) Single Photon Emission Computed Tomography • Image Feature [ABSTRACT FROM AUTHOR]

Testicular cancer is a rare tumor with a worldwide incidence that has increased over the last few decades. The majority of these tumors are testicular non-germ (TNGCTs) and germ cell tumors (TGCTs); the latter divided into two broad classes - seminomatous (SGCTs) and non-seminomatous germ cell tumors (NSGCTs). Although ultrasonography (US) maintains a primary role in the diagnostic workup of scrotal pathology, magnetic resonance imaging (MRI) has emerged as the imaging modality recommended for challenging cases, providing additional information to clarify inconclusive/equivocal US. In this work we describe and publicly share a collection of 44 images of annotated T2-weighted MRI lesions from 42 patients. Given that testicular cancer is a rare tumor, we are confident that this collection can be used to validate statistical models and to further investigate TNGCT and TGCT peculiarities using medical imaging features. Measurement(s) sex cord-gonadal stromal tumor Technology Type(s) magnetic resonance imaging Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14511624 [ABSTRACT FROM AUTHOR]

How to differentiate with MRI-based techniques testicular germ (TGCTs) and testicular non-germ cell tumors (TNGCTs) is still under debate and Radiomics may be the turning key. Our purpose is to investigate the performance of MRI-based Radiomics signatures for the preoperative prediction of testicular neoplasm histology. The aim is twofold: (i), differentiating TGCTs and TNGCTs status and (ii) differentiating seminomas (SGCTs) from non-seminomatous (NSGCTs). Forty-two patients with pathology-proven testicular neoplasms and referred for pre-treatment MRI, were retrospectively enrolled. Thirty-two out of 44 lesions were TGCTs. Twelve out of 44 were TNGCTs or other histologies. Two radiologists segmented the volume of interest on T2-weighted images. Approximately 500 imaging features were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was applied as method for variable selection. A linear model and a linear support vector machine (SVM) were trained with selected features to assess discrimination scores for the two endpoints. LASSO identified 3 features that were employed to build fivefold validated linear discriminant and linear SVM classifiers for the TGCT-TNGCT endpoint giving an overall accuracy of 89%. Four features were employed to build another SVM for the SGCT-SNGCT endpoint with an overall accuracy of 86%. The data obtained proved that T2-weighted-based Radiomics is a promising tool in the diagnostic workup of testicular neoplasms by discriminating germ cell from non-gem cell tumors, and seminomas from non-seminomas. [ABSTRACT FROM AUTHOR]