The bone marrow (BM) is a main risk organ during Lu-177-PSMA ligand therapy of metastasized castration-resistant prostate cancer (mCRPC) patients. So far, BM dosimetry relies on S values, which are pre-computed for reference anatomies, simplified activity distributions, and a physiological BM distribution. However, mCRPC patients may show a considerable bone lesion load, which leads to a heterogeneous and patient-specific activity accumulation close to BM-bearing sites. Furthermore, the patient-specific BM distribution might be significantly altered in the presence of bone lesions. The aim was to perform BM absorbed dose calculations through Monte Carlo (MC) simulations and to investigate the potential value of image-based BM localization. This study is based on 11 Lu-177-PSMA-617 therapy cycles of 10 patients (10 first cycles), who obtained a pre-therapeutic Ga-68-PSMA-11 PET/CT; quantitative Lu-177 SPECT acquisitions of the abdomen 24 (+CT), 48, and 72 h p.i.; and a Lu-177 whole-body planar acquisition at 24 h post-therapy. Patient-specific 3D volumes of interest were segmented from the Ga-68-PSMA-11 PET/CT, filled with activity information from the Lu-177 data, and imported into the FLUKA MC code together with the patient CT. MC simulations of the BM absorbed dose were performed assuming a physiological BM distribution according to the ICRP 110 reference male (MC1) or a displacement of active BM from the direct location of bone lesions (MC2). Results were compared with those from S values (SMIRD). BM absorbed doses were correlated with the decrease of lymphocytes, total white blood cells, hemoglobin level, and platelets. For two patients, an additional pre-therapeutic Tc-99m-anti-granulocyte antibody SPECT/CT was performed for BM localization. Median BM absorbed doses were 130, 37, and 11 mGy/GBq for MC1, MC2, and SMIRD, respectively. Significant strong correlation with the decrease of platelet counts was found, with highest correlation for MC2 (MC1: r = − 0.63, p = 0.04; MC2: r = − 0.71, p = 0.01; SMIRD: r = − 0.62, p = 0.04). For both investigated patients, BM localization via Tc-99m-anti-granulocyte antibody SPECT/CT indicated a displacement of active BM from the direct location of lesions similar to model MC2 and led to a reduction in the BM absorbed dose of 40 and 41% compared to MC1. Higher BM absorbed doses were observed for MC-based models; however, for MC2, all absorbed doses were still below 2 Gy. MC1 resulted in critical values for some patients, but is suspected to yield strongly exaggerated absorbed doses by neglecting bone marrow displacement. Image-based BM localization might be beneficial, and future studies are recommended to support an improvement for the prediction of hematoxicities.

Augmenting the brain clearance of toxic oligomers with small molecule modulators constitutes a promising therapeutic concept against tau deposition. However, there has been no test of this concept in animal models of Alzheimer’s disease (AD) with initiation at a late disease stage. Thus, we aimed to investigate the effects of interventional late-stage Anle138b treatment, which previously indicated great potential to inhibit oligomer accumulation by binding of pathological aggregates, on the metabolic decline in transgenic mice with established tauopathy in a longitudinal 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) study. Twelve transgenic mice expressing all six human tau isoforms (hTau) and ten controls were imaged by FDG-PET at baseline (14.5 months), followed by randomization into Anle138b treatment and vehicle groups for 3 months. FDG-PET was repeated after treatment for 3 months, and brains were analyzed by tau immunohistochemistry. Longitudinal changes of glucose metabolism were compared between study groups, and the end point tau load was correlated with individual FDG-PET findings. Tau pathology was significantly ameliorated by late-stage Anle138b treatment when compared to vehicle (frontal cortex − 53%, p < 0.001; hippocampus − 59%, p < 0.005). FDG-PET revealed a reversal of metabolic decline during Anle138b treatment, whereas the vehicle group showed ongoing deterioration. End point glucose metabolism in the brain of hTau mice had a strong correlation with tau deposition measured by immunohistochemistry (R = 0.92, p < 0.001). Late-stage oligomer modulation effectively ameliorated tau pathology in hTau mice and rescued metabolic function. Molecular imaging by FDG-PET can serve for monitoring effects of Anle138b treatment.

Background: Patients with metastatic, castration-resistant prostate cancer (mCRPC) present with an increased tumor burden in the skeleton. For these patients, Lutetium-177 (Lu-177) radioligand therapy targeting the prostate-specific membrane antigen (PSMA) has gained increasing interest with promising outcome data. Patient-individualized dosimetry enables improvement of therapy success with the aim of minimizing absorbed dose to organs at risk while maximizing absorbed dose to tumors. Different dosimetric approaches with varying complexity and accuracy exist for this purpose. The Medical Internal Radiation Dose (MIRD) formalism applied to tumors assumes a homogeneous activity distribution in a sphere with unit density for derivation of tumor S values (TSV). Voxel S value (VSV) approaches can account for heterogeneous activities but are simulated for a specific tissue. Full patient-individual Monte Carlo (MC) absorbed dose simulation addresses both, heterogeneous activity and density distributions. Subsequent CT-based density weighting has the potential to overcome the assumption of homogeneous density in the MIRD formalism with TSV and VSV methods, which could be a major limitation for the application in bone metastases with heterogeneous density. The aim of this investigation is a comparison of these methods for bone lesion dosimetry in mCRPC patients receiving Lu-177-PSMA therapy. Results: In total, 289 bone lesions in 15 mCRPC patients were analyzed. Percentage difference (PD) of average absorbed dose per lesion compared to MC, averaged over all lesions, was + 14 ± 10% (min: − 21%; max: + 56%) for TSVs. With lesion-individual density weighting using Hounsfield Unit (HU)-to-density conversion on the patient’s CT image, PD was reduced to − 8 ± 1% (min: − 10%; max: − 3%). PD on a voxel level for three-dimensional (3D) voxel-wise dosimetry methods, averaged per lesion, revealed large PDs of + 18 ± 11% (min: − 27%; max: + 58%) for a soft tissue VSV approach compared to MC; after voxel-wise density correction, this was reduced to − 5 ± 1% (min: − 12%; max: − 2%). Conclusion: Patient-individual MC absorbed dose simulation is capable to account for heterogeneous densities in bone lesions. Since the computational effort prevents its routine clinical application, TSV or VSV dosimetry approaches are used. This study showed the necessity of lesion-individual density weighting for TSV or VSV in Lu-177-PSMA therapy bone lesion dosimetry.

In this study, dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [¹⁸F]GE180 and the amino acid [¹⁸F]FET were acquired and normalized to healthy background (tumor-to-background ratio, TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

Purpose: According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. Methods: Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [¹⁸F]GE-180, dynamic amino acid PET using [¹⁸F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [¹⁸F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. Results: TBR_GE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [¹⁸F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBR_GE-180 features, parameters from kinetic and early static [¹⁸F]FET PET images, age, and the features from TBR_T2 images such as the kurtosis (0.97). Conclusion: The findings suggest that incorporating TBR_GE-180 features along with kinetic information from dynamic [¹⁸F]FET PET, kurtosis from TBR_T2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Introduction: Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated.Methods: [(18)FJFET and [(18)FJGE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate multicentric data. Statistical robustness measures were calculated before and after ComBat harmonization. Differences between features due to setting variations were assessed using Friedman test, coefficient of variation (CV) and inter-rater reliability measures, including intraclass and Spearman's rank correlation coefficients and Fleiss' Kappa.Results: According to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and interrater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [(18)FJGE-180 features were more sensitive to reconstruction settings than [(18)FJFET features.Conclusions: According to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmo-nization. Thus, for clinical utilization it is recommended to exclude affected features.