From a sample of 15651 RR Lyrae with accurate proper motions in Gaia DR2, we measure the azimuthally averaged kinematics of the inner stellar halo between 1.5 kpc and 20 kpc from the Galactic centre. We find that their kinematics are strongly radially anisotropic, and their velocity ellipsoid nearly spherically aligned over this volume. Only in the inner regions 5 kpc does the anisotropy significantly fall (but still with β > 0.25) and the velocity ellipsoid tilt towards cylindrical alignment. In the inner regions, our sample of halo stars rotates at up to 50 km s −1 , which may reflect the early history of the Milky Way, although there is also significant angular momentum exchange with the Galactic bar at these radii. We subsequently apply the Jeans equations to these kinematic measurements in order to non-parametrically infer the azimuthally averaged gravitational acceleration field over this volume, and by removing the contribution from baryonic matter, measure the contribution from dark matter. We find that the gravitational potential of the dark matter is nearly spherical with average flattening q Φ = 1.01 ± 0.06 between 5 kpc and 20 kpc, and by fitting parametric ellipsoidal density profiles to the acceleration field, we measure the flattening of the dark matter halo over these radii to be q ρ = 1.00 ± 0.09.

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68 Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/ CT images in DICOM format. Its pipeline was written using Python and C11 languages. A bone mask based on CT and a normaluptake mask including organs with physiologic 68 Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUV mean , and PSMA SUV max . Twenty 68 Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra-and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intraand interobserver variability analyses showed high agreement (all ICCs . 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUV mean , and SUV max were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P 5 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r 5 0.72, P , 0.001) and PSMA-TL (r 5 0.66, P 5 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in 68 Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

PET combined with CT and prostate-specific membrane antigen (PSMA) ligands has gained significant interest for staging prostate cancer (PC). In this study, we propose 2 multimodal quantitative indices as imaging biomarkers for the assessment of osseous tumor burden using Ga-PSMA PET/CT and present preliminary clinical data. We defined 2 bone PET indices (BPIs) that incorporate anatomic information from CT and functional information from Ga-PSMA PET: BPI is the percentage of bone volume affected by tumor and BPI additionally considers the level of PSMA expression. We describe a semiautomatic computation method based on segmentation of bones in CT and of lesions in PET. Data from 45 patients with castration-resistant PC and bone metastases during Ra-dichloride were retrospectively analyzed. We evaluated the computational stability and reproducibility of the proposed indices and explored their relation to the prostate-specific antigen blood value, the bone scan index (BSI), and disease classification using PERCIST. On the technical side, BPI and BPI showed an interobserver maximum difference of 3.5%, and their computation took only a few minutes. On the clinical side, BPI and BPI showed significant correlations with BSI ( = 0.76 and 0.74, respectively, < 0.001) and prostate-specific antigen values ( = 0.57 and 0.54, respectively, < 0.01). When the proposed indices were compared against expert rating using PERCIST, BPI and BPI showed better agreement than BSI, indicating their potential for objective response evaluation. We propose the evaluation of BPI and BPI as imaging biomarkers for Ga-PSMA PET/CT in a prospective study exploring their potential for outcome prediction in patients with bone metastases from PC.