Detection of Splenic Tissue Using Tc-99m-Labelled Denatured Red Blood Cells Scintigraphy-A Quantitative Single Center Analysis

Background: Red blood cells (RBC) scintigraphy can be used not only for detection of bleeding sites, but also of spleen tissue. However, there is no established quantitative readout. Therefore, we investigated uptake in suspected splenic lesions in direct quantitative correlation to sites of physiologic uptake in order to objectify the readout. Methods: 20 patients with Tc-99m-labelled RBC scintigraphy and SPECT/low-dose CT for assessment of suspected splenic tissue were included. Lesions were rated as vital splenic or non-splenic tissue, and uptake and physiologic uptake of bone marrow, pancreas, and spleen were then quantified using a volume-of-interest based approach. Hepatic uptake served as a reference. Results: The median uptake ratio was significantly higher in splenic (2.82 (range, 0.58-24.10), n = 47) compared to other lesions (0.49 (0.01-0.83), n = 7), p < 0.001, and 5 lesions were newly discovered. The median pancreatic uptake was 0.09 (range 0.03-0.67), bone marrow 0.17 (0.03-0.45), and orthotopic spleen 14.45 (3.04-29.82). Compared to orthotopic spleens, the pancreas showed lowest uptake (0.09 vs. 14.45, p = 0.004). Based on pancreatic uptake we defined a cutoff (0.75) to distinguish splenic from other tissues. Conclusion: As the uptake in extra-splenic regions is invariably low compared to splenules, it can be used as comparator for evaluating suspected splenic tissues

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic 18FFET PET radiomics

PURPOSE To evaluate radiomic features extracted from standard static images (20-40~min p.i.), early summation images (5-15~min p.i.), and dynamic 18FFET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. METHODS A total of 159 patients (median age 60.2~years, range 19-82~years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic 18FFET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20-40~min summation images; TBR20-40), early static (5-15~min summation images; TBR5-15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. RESULTS The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95{\%} confidence interval 0.71-0.92) and sensitivity of 92.1{\%} in the independent testing cohort. Weak predictive capability was obtained in the TBR5-15 model, with an AUC of 0.61 (95{\%} CI 0.42-0.80) in the testing cohort, while no predictive power was observed in the TBR20-40 model. CONCLUSIONS Radiomics based on TTP images extracted from dynamic 18FFET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively

Can Radiomics Provide Additional Information in [F-18]FET-Negative Gliomas?

Simple Summary Amino acid positron emission tomography (PET) complements standard magnetic resonance imaging (MRI) since it directly visualizes the increased amino acid transport into tumor cells. Amino acid PET using O-(2-[F-18]fluoroethyl)-L-tyrosine ([F-18]FET) has proven to be relevant, for example, for glioma classification, identification of tumor progression or recurrence, or for the delineation of tumor extent. Nevertheless, a relevant proportion of low-grade gliomas (30%) and few high-grade gliomas (5%) were found to show no or even decreased amino acid uptake by conventional visual analysis of PET images. Advanced image analysis with the extraction of radiomic features is known to provide more detailed information on tumor characteristics than conventional analyses. Hence, this study aimed to investigate whether radiomic features derived from dynamic [F-18]FET PET data differ between [F-18]FET-negative glioma and healthy background and thus provide information that cannot be extracted by visual read. The purpose of this study was to evaluate the possibility of extracting relevant information from radiomic features even in apparently [F-18]FET-negative gliomas. A total of 46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [F-18]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [F-18]FET PET data, i.e., early and late tumor-to-background (TBR5-15, TBR20-40) and time-to-peak (TTP) images. Radiomic features of healthy background were calculated from the tumor volume of interest mirrored in the contralateral hemisphere. The ability to distinguish tumors from healthy tissue was assessed using the Wilcoxon test and logistic regression. A total of 5, 15, and 69% of features derived from TBR20-40, TBR5-15, and TTP images, respectively, were significantly different. A high number of significantly different TTP features was even found in isometabolic gliomas (after exclusion of photopenic gliomas) with visually normal [F-18]FET uptake in static images. However, the differences did not reach satisfactory predictability for machine-learning-based identification of tumor tissue. In conclusion, radiomic features derived from dynamic [F-18]FET PET data may extract additional information even in [F-18]FET-negative gliomas, which should be investigated in larger cohorts and correlated with histological and outcome features in future studies

Monitoring of Tumor Growth with [F-18]-FET PET in a Mouse Model of Glioblastoma: SUV Measurements and Volumetric Approaches

Noninvasive tumor growth monitoring is of particular interest for the evaluation of experimental glioma therapies. This study investigates the potential of positron emission tomography (PET) using O-(2-F-18-fluoroethyl)-L-tyrosine ([F-18]-FET) to determine tumor growth in a murine glioblastoma (GBM) model including estimation of the biological tumor volume (BTV), which has hitherto not been investigated in the pre-clinical context. Fifteen GBM bearing mice (GL261) and six control mice (shams) were investigated during 5 weeks by PET followed by autoradiographic and histological assessments. [F-18]-FET PET was quantitated by calculation of maximum and mean standardized uptake values within a universal volume-of-interest (VOI) corrected for healthy background (SUVmax/BG, SUVmean/BG). A partial volume effect correction (PVEC) was applied in comparison to ex vivo autoradiography. BTVs obtained by predefined thresholds for VOI definition (SUV/BG: >= 1.4;>= 1.6;>= 1.8;>= 2.0) were compared to the histologically assessed tumor volume (n = 8). Finally, individual-optimal" thresholds for BTV definition best reflecting the histology were determined. In GBM mice SUVmax/BG and SUVmean/BG clearly increased with time, however at high inter-animal variability. No relevant [F-18]-FET uptake was observed in shams. PVEC recovered signal loss of SUVmean/BG assessment in relation to autoradiography. BTV as estimated by predefined thresholds strongly differed from the histology volume. Strikingly, the individual "optimal" thresholds for BTV assessment correlated highly with SUVmax/BG (rho = 0.97, p < 0.001), allowing SUVmax/BG-based calculation of individual thresholds. The method was verified by a subsequent validation study (n = 15, p = 0.88, p < 0.01) leading to extensively higher agreement of BTV estimations when compared to histology in contrast to predefined thresholds. [F-18]-FET PET with standard SUV measurements is feasible for glioma imaging in the GBM mouse model. PVEC is beneficial to improve accuracy of [F-18]-FET PET SUV quantification. Although SUVmax/BG and SUVmean/BG increase during the disease course, these parameters do not correlate with the respective tumor size. For the first time, we propose a histology-verified method allowing appropriate individual BTV estimation for volumetric in vivo monitoring of tumor growth with [F-18]-FET PET and show that standardized thresholds from routine clinical practice seem to be inappropriate for BTV estimation in the GBM mouse model

PSMA Expression in Glioblastoma as a Basis for Theranostic Approaches: A Retrospective, Correlational Panel Study Including Immunohistochemistry, Clinical Parameters and PET Imaging

Aim: The aim of the current study was to enlighten the evolution of prostate-specific membrane antigen (PSMA) expression in glioblastoma between initial diagnosis and recurrence in order to provide preliminary insight for further clinical investigations into innovative PSMA-directed treatment concepts in neuro-oncology. Methods: Patients who underwent resection for de-novo glioblastoma (GBM) and had a re-resection in case of a recurrent tumor following radiochemotherapy and subsequent chemotherapy were included (n = 16). Histological and immunohistochemical stainings were performed at initial diagnosis and at recurrence (n = 96 tissue specimens). Levels of PSMA expression both in endothelial and non-endothelial cells as well as vascular density (CD34) were quantified via immunohistochemistry and changes between initial diagnosis and recurrence were determined. Immunohistochemical findings were correlated with survival and established clinical parameters. Results: PSMA expression was found to be present in all GBM tissue samples at initial diagnosis as well as in all but one case of recurrent tumor samples. The level of PSMA expression in glioblastoma varied inter-individually both in endothelial and non-endothelial cells. Likewise, the temporal evolution of PSMA expression highly varied in between patients. The level of vascular PSMA expression at recurrence and its change between initial diagnosis and recurrence was associated with post recurrence survival time: Patients with high vascular PSMA expression at recurrence as well as patients with increasing PSMA expression throughout the disease course survived shorter than patients with low vascular PSMA expression or decreasing vascular PSMA expression. There was no significant correlation of PSMA expression with MGMT promoter methylation status or Ki-67 labelling index. Conclusion: PSMA is expressed in glioblastoma both at initial diagnosis and at recurrence. High vascular PSMA expression at recurrence seems to be a negative prognostic marker. Thus, PSMA expression in GBM might present a promising target for theranostic approaches in recurrent glioblastoma. Especially PSMA PET imaging and PSMA-directed radioligand therapy warrant further studies in brain tumor patients

Feasibility of radiomic feature harmonization for pooling of [18F]FET or [18F]GE-180 PET images of gliomas

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