Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.

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: TBRGE-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 TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). CONCLUSION: The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Combination of pre-treatment dynamic [18F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma.

PURPOSE: The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [18F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS: A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [18F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. RESULTS: A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort. CONCLUSIONS: This study successfully built and evaluated prediction models using [18F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [18F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [18F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.

TSPO PET signal using [18F]GE180 is associated with survival in recurrent gliomas.

PURPOSE: Glioma patients, especially recurrent glioma, suffer from a poor prognosis. While advances to classify glioma on a molecular level improved prognostication at initial diagnosis, markers to prognosticate survival in the recurrent situation are still needed. As 18 kDa translocator protein (TSPO) was previously reported to be associated with aggressive histopathological glioma features, we correlated the TSPO positron emission tomography (PET) signal using [18F]GE180 in a large cohort of recurrent glioma patients with their clinical outcome. METHODS: In patients with [18F]GE180 PET at glioma recurrence, [18F]GE180 PET parameters (e.g., SUVmax) as well as other imaging features (e.g., MRI volume, [18F]FET PET parameters when available) were evaluated together with patient characteristics (age, sex, Karnofsky-Performance score) and neuropathological features (e.g. WHO 2021 grade, IDH-mutation status). Uni- and multivariate Cox regression and Kaplan-Meier survival analyses were performed to identify prognostic factors for post-recurrence survival (PRS) and time to treatment failure (TTF). RESULTS: Eighty-eight consecutive patients were evaluated. TSPO tracer uptake correlated with tumor grade at recurrence (p < 0.05), with no significant differences in IDH-wild-type versus IDH-mutant tumors. Within the subgroup of IDH-mutant glioma (n = 46), patients with low SUVmax (median split, ≤ 1.60) had a significantly longer PRS (median 41.6 vs. 25.3 months, p = 0.031) and TTF (32.2 vs 8.7 months, p = 0.001). Also among IDH-wild-type glioblastoma (n = 42), patients with low SUVmax (≤ 1.89) had a significantly longer PRS (median not reached vs 8.2 months, p = 0.002). SUVmax remained an independent prognostic factor for PRS in the multivariate analysis including CNS WHO 2021 grade, IDH status, and age. Tumor volume defined by [18F]FET PET or contrast-enhanced MRI correlated weakly with TSPO tracer uptake. Treatment regimen did not differ among the median split subgroups. CONCLUSION: Our data suggest that TSPO PET using [18F]GE180 can help to prognosticate recurrent glioma patients even among homogeneous molecular subgroups and may therefore serve as valuable non-invasive biomarker for individualized patient management.

TSPO PET imaging of natalizumab-associated progressive multifocal leukoencephalopathy.

Progressive multifocal leukoencephalopathy (PML) is a severe infection of the CNS caused by the polyomavirus JC that can occur in multiple sclerosis patients treated with natalizumab. Clinical management of patients with natalizumab-associated PML is challenging not least because current imaging tools for the early detection, longitudinal monitoring and differential diagnosis of PML lesions are limited. Here we evaluate whether translocator protein (TSPO) PET imaging can be applied to monitor the inflammatory activity of PML lesions over time and differentiate them from multiple sclerosis lesions. For this monocentre pilot study we followed eight patients with natalizumab-associated PML with PET imaging using the TSPO radioligand 18F-GE-180 combined with frequent 3 T MRI. In addition we compared TSPO PET signals in PML lesions with the signal pattern of multiple sclerosis lesions from 17 independent multiple sclerosis patients. We evaluated the standardized uptake value ratio as well as the morphometry of the TSPO uptake for putative PML and multiple sclerosis lesions areas compared to a radiologically unaffected pseudo-reference region in the cerebrum. Furthermore, TSPO expression in situ was immunohistochemically verified by determining the density and cellular identity of TSPO-expressing cells in brain sections from four patients with early natalizumab-associated PML as well as five patients with other forms of PML and six patients with inflammatory demyelinating CNS lesions (clinically isolated syndrome/multiple sclerosis). Histological analysis revealed a reticular accumulation of TSPO expressing phagocytes in PML lesions, while such phagocytes showed a more homogeneous distribution in putative multiple sclerosis lesions. TSPO PET imaging showed an enhanced tracer uptake in natalizumab-associated PML lesions that was present from the early to the chronic stages (up to 52 months after PML diagnosis). While gadolinium enhancement on MRI rapidly declined to baseline levels, TSPO tracer uptake followed a slow one phase decay curve. A TSPO-based 3D diagnostic matrix taking into account the uptake levels as well as the shape and texture of the TSPO signal differentiated >96% of PML and multiple sclerosis lesions. Indeed, treatment with rituximab after natalizumab-associated PML in three patients did not affect tracer uptake in the assigned PML lesions but reverted tracer uptake to baseline in the assigned active multiple sclerosis lesions. Taken together our study suggests that TSPO PET imaging can reveal CNS inflammation in natalizumab-associated PML. TSPO PET may facilitate longitudinal monitoring of disease activity and help to distinguish recurrent multiple sclerosis activity from PML progression.

Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat.

Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET 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 [18F]FET 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 [18F]FET 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 [18F]FET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively.

Feasibility of short imaging protocols for [18F]PI-2620 tau-PET in progressive supranuclear palsy.

PURPOSE: Dynamic 60-min positron emission tomography (PET) imaging with the novel tau radiotracer [18F]PI-2620 facilitated accurate discrimination between patients with progressive supranuclear palsy (PSP) and healthy controls (HCs). This study investigated if truncated acquisition and static time windows can be used for [18F]PI-2620 tau-PET imaging of PSP. METHODS: Thirty-seven patients with PSP Richardson syndrome (PSP-RS) were evaluated together with ten HCs. [18F]PI-2620 PET was performed by a dynamic 60-min scan. Distribution volume ratios (DVRs) were calculated using full and truncated scan durations (0-60, 0-50, 0-40, 0-30, and 0-20 min p.i.). Standardized uptake value ratios (SUVrs) were obtained 20-40, 30-50, and 40-60 min p.i.. All DVR and SUVr data were compared with regard to their potential to discriminate patients with PSP-RS from HCs in predefined subcortical and cortical target regions (effect size, area under the curve (AUC), multi-region classifier). RESULTS: 0-50 and 0-40 DVR showed equivalent effect sizes as 0-60 DVR (averaged Cohen's d: 1.22 and 1.16 vs. 1.26), whereas the performance dropped for 0-30 or 0-20 DVR. The 20-40 SUVr indicated the best performance of all static acquisition windows (averaged Cohen's d: 0.99). The globus pallidus internus discriminated patients with PSP-RS and HCs at a similarly high level for 0-60 DVR (AUC: 0.96), 0-40 DVR (AUC: 0.96), and 20-40 SUVr (AUC: 0.94). The multi-region classifier sensitivity of these time windows was consistently 86%. CONCLUSION: Truncated and static imaging windows can be used for [18F]PI-2620 PET imaging of PSP. 0-40 min dynamic scanning offers the best balance between accuracy and economic scanning.

The Traumatic Inoculation Process Affects TSPO Radioligand Uptake in Experimental Orthotopic Glioblastoma.

BACKGROUND: The translocator protein (TSPO) has been proven to have great potential as a target for the positron emission tomography (PET) imaging of glioblastoma. However, there is an ongoing debate about the potential various sources of the TSPO PET signal. This work investigates the impact of the inoculation-driven immune response on the PET signal in experimental orthotopic glioblastoma. METHODS: Serial [18F]GE-180 and O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) PET scans were performed at day 7/8 and day 14/15 after the inoculation of GL261 mouse glioblastoma cells (n = 24) or saline (sham, n = 6) into the right striatum of immunocompetent C57BL/6 mice. An additional n = 25 sham mice underwent [18F]GE-180 PET and/or autoradiography (ARG) at days 7, 14, 21, 28, 35, 50 and 90 in order to monitor potential reactive processes that were solely related to the inoculation procedure. In vivo imaging results were directly compared to tissue-based analyses including ARG and immunohistochemistry. RESULTS: We found that the inoculation process represents an immunogenic event, which significantly contributes to TSPO radioligand uptake. [18F]GE-180 uptake in GL261-bearing mice surpassed [18F]FET uptake both in the extent and the intensity, e.g., mean target-to-background ratio (TBRmean) in PET at day 7/8: 1.22 for [18F]GE-180 vs. 1.04 for [18F]FET, p < 0.001. Sham mice showed increased [18F]GE-180 uptake at the inoculation channel, which, however, continuously decreased over time (e.g., TBRmean in PET: 1.20 at day 7 vs. 1.09 at day 35, p = 0.04). At the inoculation channel, the percentage of TSPO/IBA1 co-staining decreased, whereas TSPO/GFAP (glial fibrillary acidic protein) co-staining increased over time (p < 0.001). CONCLUSION: We identify the inoculation-driven immune response to be a relevant contributor to the PET signal and add a new aspect to consider for planning PET imaging studies in orthotopic glioblastoma models.

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

Acetyl-DL-leucine in cerebellar ataxia ([18F]-FDG-PET study): how does a cerebellar disorder influence cortical sensorimotor networks?

OBJECTIVE: The aim of the study was to deepen our insights into central compensatory processes of brain networks in patients with cerebellar ataxia (CA) before and with treatment with acetyl-DL-leucine (AL) by means of resting-state [18F]-FDG-PET brain imaging. METHODS: Retrospective analyses of [18F]-FDG-PET data in 22 patients with CA (with vestibular and ocular motor disturbances) of different etiologies who were scanned before (PET A) and on AL treatment (PET B). Group subtraction analyses, e.g., for responders and non-responders, comparisons with healthy controls and correlation analyses of regional cerebral glucose metabolism (rCGM) with symptom duration, ataxia (SARA) and quality of life (QoL) scores were calculated. RESULTS: Prior to treatment rCGM was consistently downregulated at the cerebellar level and increased in multisensory cortical areas, e.g., somatosensory, primary and secondary visual (including V5, precuneus), secondary vestibular (temporal gyrus, anterior insula), and premotor/supplementary motor areas. With AL (PET B vs. A) cerebellar hypometabolism was deepened and sensorimotor hypermetabolism increased only in responders with clinical benefit, but not for the non-responders and the whole CA group. A positive correlation of ataxia improvement with rCGM was found in visual and vestibular cortices, a negative correlation in cerebellar and brainstem areas. QoL showed a positive correlation with rCGM in the cerebellum and symptom duration in premotor and somatosensory areas. CONCLUSIONS: Central compensatory processes in CA mainly involve multisensory visual, vestibular, and somatosensory networks as well as premotor/primary motor areas at the cortical level. The enhanced divergence of cortical sensorimotor up- and cerebellar downregulation with AL in responders could reflect amplification of inhibitory cerebellar mechanisms.

Toward Single-Time-Point Image-Based Dosimetry of 177Lu-PSMA-617 Therapy.

Radiopharmaceutical therapies (RPTs) with 177Lu-prostate-specific membrane antigen (PSMA) ligands have demonstrated promising results for the treatment of metastatic castration-resistant prostate cancer. The lack of absorbed-dose-effect relationships currently prevents patient-specific activity personalization. To ease the implementation of dosimetry in the routine clinical workflow for RPT, simplified methods such as single-time-point (STP) instead of multiple-time-point (MTP) imaging protocols are required. This work aimed at assessing differences in the time-integrated activity (TIA) of STP versus MTP image-based dosimetry for 177Lu-PSMA-617 therapy. Methods: Twenty metastatic castration-resistant prostate cancer patients with MTP quantitative 177Lu-SPECT imaging data (∼24, 48, and 72 h post injection (p.i.)) available on first and second 177Lu-PSMA-617 therapy cycles were included in this study. Time-activity curves were fitted for kidneys and lesions to derive effective half-lives and yield a reference TIA. STP approaches involved the formula by Hänscheid (STPH) and a prior-information method (STPprior) that uses the effective half-lives from the first therapy cycle. All time points were considered for the STP approaches. Percentage differences (PDs) in TIA between STP and MTP were compared for the second therapy cycle. Results: Using STPH at 48 h p.i. for kidneys showed a -1.3% ± 5.6% PD from MTP, whereas STPprior showed a PD of 4.6% ± 6.2%. The smallest average PDs for the 56 investigated individual lesions were found using STPprior at 48 h p.i., at only 0.4% ± 14.9%, whereas STPH at 72 h p.i. had a smallest PD of -1.9% ± 14.8%. Conclusion: STP dosimetry for 177Lu-PSMA-617 therapy using a single SPECT/CT scan at 48 or 72 h p.i. is feasible, with a PD of less than ±20% compared with MTP. The validity of both STPH and STPprior has been demonstrated. We believe this finding can increase the adoption of dosimetry and facilitate implementation in routine clinical RPT workflows. Doing so will ultimately enable the finding of dose-effect relationships based on fixed therapy activities that may, in future, allow for absorbed-dose-based RPT activity personalization.

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: [18F]FET and [18F]GE-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 inter-rater 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. [18F]GE-180 features were more sensitive to reconstruction settings than [18F]FET 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 harmonization. Thus, for clinical utilization it is recommended to exclude affected features.

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.

INTRODUCTION: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, the gold standard TSPO PET quantification method includes a 90 min scan and continuous arterial blood sampling, which is challenging to perform on a routine basis. In this work, we determine what information is required for a simplified quantification approach using a machine learning algorithm. MATERIALS AND METHODS: We analyzed data from 18 patients with ischemic stroke who received 0-90 min [18F]GE-180 PET as well as T1-weigted (T1w), FLAIR, and arterial spin labeling (ASL) MRI scans. During PET scans, five manual venous blood samples at 5, 15, 30, 60, and 85 min post injection (p.i.) were drawn, and plasma activity concentration was measured. Total distribution volume (VT) was calculated using Logan plot with the full dynamic PET and an image-derived input function (IDIF) from the carotid arteries. IDIF was scaled by a calibration factor derived from all the measured plasma activity concentrations. The calculated VT values were used for training a random forest regressor. As input features for the model, we used three late PET frames (60-70, 70-80, and 80-90 min p.i.), the ASL image reflecting perfusion, the voxel coordinates, the lesion mask, and the five plasma activity concentrations. The algorithm was validated with the leave-one-out approach. To estimate the impact of the individual features on the algorithm's performance, we used Shapley Additive Explanations (SHAP). Having determined that the three late PET frames and the plasma activity concentrations were the most important features, we tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots. RESULTS: When using all the input features, the algorithm predicted VT values with high accuracy (87.8 ±â€¯8.3%) for both lesion and non-lesion voxels. The SHAP values demonstrated high impact of the late PET frames (60-70, 70-80, and 80-90 min p.i.) and plasma activity concentrations on the VT prediction, while the influence of the ASL-derived perfusion, voxel coordinates, and the lesion mask was low. Among all the combinations of the late PET frames and plasma activity concentrations, the 70-80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest VT estimate in the ischemic lesion. CONCLUSION: Reliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.

Prognostic Value of TSPO PET Before Radiotherapy in Newly Diagnosed IDH-Wild-Type Glioblastoma.

The 18-kDa translocator protein (TSPO) is gaining recognition as a relevant target in glioblastoma imaging. However, data on the potential prognostic value of TSPO PET imaging in glioblastoma are lacking. Therefore, we investigated the association of TSPO PET imaging results with survival outcome in a homogeneous cohort of glioblastoma patients. Methods: Patients were included who had newly diagnosed, histologically confirmed isocitrate dehydrogenase (IDH)-wild-type glioblastoma with available TSPO PET before either normofractionated radiotherapy combined with temozolomide or hypofractionated radiotherapy. SUVmax on TSPO PET, TSPO binding affinity status, tumor volumes on MRI, and further clinical data, such as O 6-alkylguanine DNA methyltransferase (MGMT) and telomerase reverse transcriptase (TERT) gene promoter mutation status, were correlated with patient survival. Results: Forty-five patients (median age, 63.3 y) were included. Median SUVmax was 2.2 (range, 1.0-4.7). A TSPO PET signal was associated with survival: High uptake intensity (SUVmax > 2.2) was related to significantly shorter overall survival (OS; 8.3 vs. 17.8 mo, P = 0.037). Besides SUVmax, prognostic factors for OS were age (P = 0.046), MGMT promoter methylation status (P = 0.032), and T2-weighted MRI volume (P = 0.031). In the multivariate survival analysis, SUVmax in TSPO PET remained an independent prognostic factor for OS (P = 0.023), with a hazard ratio of 2.212 (95% CI, 1.115-4.386) for death in cases with a high TSPO PET signal (SUVmax > 2.2). Conclusion: A high TSPO PET signal before radiotherapy is associated with significantly shorter survival in patients with newly diagnosed IDH-wild-type glioblastoma. TSPO PET seems to add prognostic insights beyond established clinical parameters and might serve as an informative tool as clinicians make survival predictions for patients with glioblastoma.

Reduced Acquisition Time [18F]GE-180 PET Scanning Protocol Replaces Gold-Standard Dynamic Acquisition in a Mouse Ischemic Stroke Model.

AIM: Understanding neuroinflammation after acute ischemic stroke is a crucial step on the way to an individualized post-stroke treatment. Microglia activation, an essential part of neuroinflammation, can be assessed using [18F]GE-180 18 kDa translocator protein positron emission tomography (TSPO-PET). However, the commonly used 60-90 min post-injection (p.i.) time window was not yet proven to be suitable for post-stroke neuroinflammation assessment. In this study, we compare semi-quantitative estimates derived from late time frames to quantitative estimates calculated using a full 0-90 min dynamic scan in a mouse photothrombotic stroke (PT) model. MATERIALS AND METHODS: Six mice after PT and six sham mice were included in the study. For a half of the mice, we acquired four serial 0-90 min scans per mouse (analysis cohort) and calculated standardized uptake value ratios (SUVRs; cerebellar reference) for the PT volume of interest (VOI) in five late 10 min time frames as well as distribution volume ratios (DVRs) for the same VOI. We compared late static 10 min SUVRs and the 60-90 min time frame of the analysis cohort to the corresponding DVRs by linear fitting. The other half of the animals received a static 60-90 min scan and was used as a validation cohort. We extrapolated DVRs by using the static 60-90 min p.i. time window, which were compared to the DVRs of the analysis cohort. RESULTS: We found high linear correlations between SUVRs and DVRs in the analysis cohort for all studied 10 min time frames, while the fits of the 60-70, 70-80, and 80-90 min p.i. time frames were the ones closest to the line of identity. For the 60-90 min time window, we observed an excellent linear correlation between SUVR and DVR regardless of the phenotype (PT vs. sham). The extrapolated DVRs of the validation cohort were not significantly different from the DVRs of the analysis group. CONCLUSION: Simplified quantification by a reference tissue ratio of the late 60-90 min p.i. [18F]GE-180 PET image can replace full quantification of a dynamic scan for assessment of microglial activation in the mouse PT model.

18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy.

BACKGROUND: Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. METHODS: A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. RESULTS: First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. CONCLUSIONS: 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.

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

The purpose of this study was to evaluate the possibility of extracting relevant information from radiomic features even in apparently [18F]FET-negative gliomas. A total of 46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [18F]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [18F]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 [18F]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 [18F]FET PET data may extract additional information even in [18F]FET-negative gliomas, which should be investigated in larger cohorts and correlated with histological and outcome features in future studies.

Evaluation of the Efficacy of a Combined Treatment Using the mTOR-Inhibitor Everolimus and [177Lu]Lu-DOTA-TATE in Nude CD1 Mice with SSTR-Expressing Pancreatic AR42J Xenograft Tumors.

Therapy options for advanced pancreatic neuroendocrine tumors (pNETs) include the mTOR inhibitor everolimus and peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-DOTA-TATE, however further optimization in the therapeutic landscape is required as response rates are still low. In this study, we investigated the synergistic and potentially enhanced efficacy of a combined treatment with everolimus and [177Lu]Lu-DOTA-TATE in a mouse model. Baseline [68Ga]Ga-DOTA-TATE PET scans were obtained five days after athymic CD1 mice were inoculated with AR42J tumor cells, before separating the animals into four groups. Group 1 received a placebo, group 2 everolimus, group 3 a placebo and PRRT, and group 4 everolimus and PRRT. The treatment response was monitored by manually measuring the tumor volumes (manual tumor volume, MTV) and conducting sequential [68Ga]Ga-DOTA-TATE PET scans at one, two, and four weeks after treatment induction. The biological tumor volume (BTV) was derived from PET scans using threshold-based volume of interest (VOI) measurements. Tracer uptake was measured semi-quantitatively as a tumor to background ratio (TBR). Mice were euthanized due to excessive tumor growth according to the ethics protocol; blood samples were drawn for the preparation of full blood counts and kidneys were obtained for histological analysis. For the histological assessment, a standardized score (renal damage score, RDS) was used. Full blood counts showed significantly increased numbers of neutrophils and lymphocytes in the groups receiving PRRT. All other parameters did not differ relevantly. In the histological analysis, groups receiving PRRT had a significantly higher RDS, whereas everolimus only tended to cause an increase in the RDS. Mice in groups 1 and 2 had to be euthanized due to excessive tumor growth two weeks after the start of the therapy, whereas follow-up in groups 3 and 4 comprised four weeks. PRRT significantly inhibited tumor growth; the administration of everolimus did not induce an additional effect. A good correlation existed between MTV and BTV. PRRT significantly reduced the TBR. [68Ga]Ga-DOTA-TATE PET is suitable for monitoring tumor growth in the applied model. The high efficacy of [177Lu]Lu-DOTA-TATE is not enhanced by the combination with everolimus.

Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy.

This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell's concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.