Examining the role of dopamine in methylphenidate's effects on resting brain function.

The amplitude of low-frequency fluctuations (ALFF) and global functional connectivity density (gFCD) are fMRI (Functional MRI) metrics widely used to assess resting brain function. However, their differential sensitivity to stimulant-induced dopamine (DA) increases, including the rate of DA rise and the relationship between them, have not been investigated. Here we used, simultaneous PET-fMRI to examine the association between dynamic changes in striatal DA and brain activity as assessed by ALFF and gFCD, following placebo, intravenous (IV), or oral methylphenidate (MP) administration, using a within-subject double-blind placebo-controlled design. In putamen, MP significantly reduced D2/3 receptor availability and strongly reduced ALFF and increased gFCD in the brain for IV-MP (Cohen's d > 1.6) but less so for oral-MP (Cohen's d < 0.6). Enhanced gFCD was associated with both the level and the rate of striatal DA increases, whereas decreased ALFF was only associated with the level of DA increases. These findings suggest distinct representations of neurovascular activation with ALFF and gFCD by stimulant-induced DA increases with differential sensitivity to the rate and the level of DA increases. We also observed an inverse association between gFCD and ALFF that was markedly enhanced during IV-MP, which could reflect an increased contribution from MP's vasoactive properties.

Noradrenergic alterations in Parkinson's disease: a combined 11C-yohimbine PET/neuromelanin MRI study.

Degeneration of the noradrenergic system is now considered a pathological hallmark of Parkinson's disease, but little is known about its consequences in terms of parkinsonian manifestations. Here, we evaluated two aspects of the noradrenergic system using multimodal in vivo imaging in patients with Parkinson's disease and healthy controls: the pigmented cell bodies of the locus coeruleus with neuromelanin sensitive MRI; and the density of α2-adrenergic receptors (ARs) with PET using 11C-yohimbine. Thirty patients with Parkinson's disease and 30 age- and sex-matched healthy control subjects were included. The characteristics of the patients' symptoms were assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Patients showed reduced neuromelanin signal intensity in the locus coeruleus compared with controls and diminished 11C-yohimbine binding in widespread cortical regions, including the motor cortex, as well as in the insula, thalamus and putamen. Clinically, locus coeruleus neuronal loss was correlated with motor (bradykinesia, motor fluctuations, tremor) and non-motor (fatigue, apathy, constipation) symptoms. A reduction of α2-AR availability in the thalamus was associated with tremor, while a reduction in the putamen, the insula and the superior temporal gyrus was associated with anxiety. These results highlight a multifaceted alteration of the noradrenergic system in Parkinson's disease since locus coeruleus and α2-AR degeneration were found to be partly uncoupled. These findings raise important issues about noradrenergic dysfunction that may encourage the search for new drugs targeting this system, including α2-ARs, for the treatment of Parkinson's disease.

Short-term effects of obesity surgery versus low-energy diet on body composition and tissue-specific glucose uptake: a randomised clinical study using whole-body integrated 18F-FDG-PET/MRI.

AIMS/HYPOTHESIS: Obesity surgery (OS) and diet-induced weight loss rapidly improve insulin resistance. We aim to investigate the impact of either Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) surgery compared with a diet low in energy (low-calorie diet; LCD) on body composition, glucose control and insulin sensitivity, assessed both at the global and tissue-specific level in individuals with obesity but not diabetes. METHODS: In this parallel group randomised controlled trial, patients on a waiting list for OS were randomised (no blinding, sealed envelopes) to either undergo surgery directly or undergo an LCD before surgery. At baseline and 4 weeks after surgery (n=15, 11 RYGB and 4 SG) or 4 weeks after the start of LCD (n=9), investigations were carried out, including an OGTT and hyperinsulinaemic-euglycaemic clamps during which concomitant simultaneous whole-body [18F]fluorodeoxyglucose-positron emission tomography (PET)/MRI was performed. The primary outcome was HOMA-IR change. RESULTS: One month after bariatric surgery and initiation of LCD, both treatments induced similar reductions in body weight (mean ± SD: -7.7±1.4 kg and -7.4±2.2 kg, respectively), adipose tissue volume (7%) and liver fat content (2% units). HOMA-IR, a main endpoint, was significantly reduced following OS (-26.3% [95% CI -49.5, -3.0], p=0.009) and non-significantly following LCD (-20.9% [95% CI -58.2, 16.5). For both groups, there were similar reductions in triglycerides and LDL-cholesterol. Fasting plasma glucose and insulin were also significantly reduced only following OS. There was an increase in glucose AUC in response to an OGTT in the OS group (by 20%) but not in the LCD group. During hyperinsulinaemia, only the OS group showed a significantly increased PET-derived glucose uptake rate in skeletal muscle but a reduced uptake in the heart and abdominal adipose tissue. Both liver and brain glucose uptake rates were unchanged after surgery or LCD. Whole-body glucose disposal and endogenous glucose production were not significantly affected. CONCLUSIONS/INTERPRETATION: The short-term metabolic effects seen 4 weeks after OS are not explained by loss of body fat alone. Thus OS, but not LCD, led to reductions in fasting plasma glucose and insulin resistance as well as to distinct changes in insulin-stimulated glucose fluxes to different tissues. Such effects may contribute to the prevention or reversal of type 2 diabetes following OS. Moreover, the full effects on whole-body insulin resistance and plasma glucose require a longer time than 4 weeks. TRIAL REGISTRATION: ClinicalTrials.gov NCT02988011 FUNDING: This work was supported by AstraZeneca R&D, the Swedish Diabetes Foundation, the European Union's Horizon Europe Research project PAS GRAS, the European Commission via the Marie Sklodowska Curie Innovative Training Network TREATMENT, EXODIAB, the Family Ernfors Foundation, the P.O. Zetterling Foundation, Novo Nordisk Foundation, the Agnes and Mac Rudberg Foundation and the Uppsala University Hospital ALF grants.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain.

PURPOSE: The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS: 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS: Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS: Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Increased [18F]FDG uptake in the infarcted myocardial area displayed by combined PET/CMR correlates with snRNA-seq-detected inflammatory cell invasion.

Combined [18F]FDG PET-cardiac MRI imaging (PET/CMR) is a useful tool to assess myocardial viability and cardiac function in patients with acute myocardial infarction (AMI). Here, we evaluated the prognostic value of PET/CMR in a porcine closed-chest reperfused AMI (rAMI) model. Late gadolinium enhancement by PET/CMR imaging displayed tracer uptake defect at the infarction site by 3 days after the rAMI in the majority of the animals (group Match, n = 28). Increased [18F]FDG uptake at the infarcted area (metabolism/contractility mismatch) with reduced tracer uptake in the remote viable myocardium (group Mismatch, n = 12) 3 days after rAMI was observed in the animals with larger infarct size and worse left ventricular ejection fraction (LVEF) (34 ± 8.7 vs 42.0 ± 5.2%), with lower LVEF also at the 1-month follow-up (35.8 ± 9.5 vs 43.0 ± 6.3%). Transcriptome analyses by bulk and single-nuclei RNA sequencing of the infarcted myocardium and border zones (n = 3 of each group, and 3 sham-operated controls) revealed a strong inflammatory response with infiltration of monocytes and macrophages in the infarcted and border areas in Mismatch animals. Our data indicate a high prognostic relevance of combined PET/MRI in the subacute phase of rAMI for subsequent impairment of heart function and underline the adverse effects of an excessive activation of the innate immune system in the initial phase after rAMI.

Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [18F]FDG-PET/MRI.

PURPOSE: To evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [18F]FDG-PET/MRI staging can predict pathologic complete response (pCR) in patients with newly diagnosed breast cancer prior to neoadjuvant system therapy (NAST). METHODS: Altogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [18F]FDG-PET/MRI, a histopathological workup of their breast cancer lesions and evaluation of clinical data. Fifty-six features derived from positron emission tomography (PET), magnetic resonance imaging (MRI), sociodemographic / anthropometric, histopathologic as well as clinical data were generated and used as input for an extreme Gradient Boosting model (XGBoost) to predict pCR. The model was evaluated in a five-fold nested-cross-validation incorporating independent hyper-parameter tuning within the inner loops to reduce the risk of overoptimistic estimations. Diagnostic model-performance was assessed by determining the area under the curve of the receiver operating characteristics curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, feature importances of the XGBoost model were evaluated to assess which features contributed most to distinguish between pCR and non-pCR. RESULTS: Nested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features. CONCLUSION: The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process.

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response.

PURPOSE: Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. METHODS: Thirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. RESULTS: Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. CONCLUSIONS: In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

Diagnostic accuracy of fully hybrid [68Ga]Ga-PSMA-11 PET/MRI and [68Ga]Ga-RM2 PET/MRI in patients with biochemically recurrent prostate cancer: a prospective single-center phase II clinical trial.

PURPOSE: To compare the diagnostic accuracy and detection rates of PET/MRI with [68Ga]Ga-PSMA-11 and [68Ga]Ga-M2 in patients with biochemical recurrence of prostate cancer (PCa). METHODS: Sixty patients were enrolled in this prospective single-center phase II clinical trial from June 2020 to October 2022. Forty-four/60 completed all study examinations and were available at follow-up (median: 22.8 months, range: 6-31.5 months). Two nuclear medicine physicians analyzed PET images and two radiologists interpreted MRI; images were then re-examined to produce an integrated PET/MRI report for both [68Ga]Ga-PSMA-11 and [68Ga]Ga-RM2 examinations. A composite reference standard including histological specimens, response to treatment, and conventional imaging gathered during follow-up was used to validate imaging findings. Detection rates, accuracy, sensitivity, specificity, positive, and negative predictive value were assessed. McNemar's test was used to compare sensitivity and specificity on a per-patient base and detection rate on a per-region base. Prostate bed, locoregional lymph nodes, non-skeletal distant metastases, and bone metastases were considered. p-value significance was defined below the 0.05 level after correction for multiple testing. RESULTS: Patients' median age was 69.8 years (interquartile range (IQR): 61.8-75.1) and median PSA level at time of imaging was 0.53 ng/mL (IQR: 0.33-2.04). During follow-up, evidence of recurrence was observed in 31/44 patients. Combining MRI with [68Ga]Ga-PSMA-11 PET and [68Ga]Ga-RM2 PET resulted in sensitivity = 100% and 93.5% and specificity of 69.2% and 69.2%, respectively. When considering the individual imaging modalities, [68Ga]Ga-RM2 PET showed lower sensitivity compared to [68Ga]Ga-PSMA-11 PET and MRI (61.3% vs 83.9% and 87.1%, p = 0.046 and 0.043, respectively), while specificity was comparable among the imaging modalities (100% vs 84.6% and 69.2%, p = 0.479 and 0.134, respectively). CONCLUSION: This study brings further evidence on the utility of fully hybrid PET/MRI for disease characterization in patients with biochemically recurrent PCa. Imaging with [68Ga]Ga-PSMA-11 PET showed high sensitivity, while the utility of [68Ga]Ga-RM2 PET in absence of a simultaneous whole-body/multiparametric MRI remains to be determined.

Added Value of [18F]PSMA-1007 PET/CT and PET/MRI in Patients With Biochemically Recurrent Prostate Cancer: Impact on Detection Rates and Clinical Management.

BACKGROUND: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) can change management in a large fraction of patients with biochemically recurrent prostate cancer (BCR). PURPOSE: To investigate the added value of PET to MRI and CT for this patient group, and to explore whether the choice of the PET paired modality (PET/MRI vs. PET/CT) impacts detection rates and clinical management. STUDY TYPE: Retrospective. SUBJECTS: 41 patients with BCR (median age [range]: 68 [55-78]). FIELD STRENGTH/SEQUENCE: 3T, including T1-weighted gradient echo (GRE), T2-weighted turbo spin echo (TSE) and dynamic contrast-enhanced GRE sequences, diffusion-weighted echo-planar imaging, and a T1-weighted TSE spine sequence. In addition to MRI, [18F]PSMA-1007 PET and low-dose CT were acquired on the same day. ASSESSMENT: Images were reported using a five-point Likert scale by two teams each consisting of a radiologist and a nuclear medicine physician. The radiologist performed a reading using CT and MRI data and a joint reading between radiologist and nuclear medicine physician was performed using MRI, CT, and PET from either PET/MRI or PET/CT. Findings were presented to an oncologist to create intended treatment plans. Intrareader and interreader agreement analysis was performed. STATISTICAL TESTS: McNemar test, Cohen's κ, and intraclass correlation coefficients. A P-value <0.05 was considered significant. RESULTS: 7 patients had positive findings on MRI and CT, 22 patients on joint reading with PET/CT, and 18 patients joint reading with PET/MRI. For overall positivity, interreader agreement was poor for MR and CT (κ = 0.36) and almost perfect with addition of PET (PET/CT κ = 0.85, PET/MRI κ = 0.85). The addition of PET from PET/CT and PET/MRI changed intended treatment in 20 and 18 patients, respectively. Between joint readings, intended treatment was different for eight patients. DATA CONCLUSION: The addition of [18F]PSMA-1007 PET/MRI or PET/CT to MRI and CT may increase detection rates, could reduce interreader variability, and may change intended treatment in half of patients with BCR. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Disassociation between fluctuation shift over frequency and glucose metabolism in motor cortex of Parkinson's disease: a hybrid PET/fMRI study.

Increased glucose metabolism and decreased low-frequency fluctuation have been consistently reported in the motor area of Parkinson's disease (PD). The reason for such seeming paradox is unclear. Here, we enrolled 34 PD patients and 25 healthy controls (HCs) for hybrid PET/fMRI scan (PET/fMRI(discovery) dataset). In addition, 2 replication datasets, namely fMRI(validation-1) and fMRI(validation-2) dataset, were also included. We computed ratio of standard uptake value (SUVr) to measure FDG-uptake. The amplitude of low-frequency fluctuations (ALFF) for the following 4 frequency bands was calculated: slow-5, slow-4, slow-3, and slow-2. We obtained a significant group-by-frequency interaction effect of ALFF in the paracentral lobule/supplementary motor area (PFWE = 0.003) and the right sensorimotor area (PFWE < 0.001) in the PET/fMRI(discovery) dataset, which could be replicated using fMRI(validation-1) and fMRI(validation-2) datasets (PFWE < 0.05). In detail, HCs exhibited power law-like fluctuation pattern, but PD patients did not. Correlation analyses further revealed significant associations between ALFF and FDG-uptake in HCs (P-values < 0.031), but not in PD (P-values > 0.28). Taken together, this study identified a fluctuation shift over frequency effect in PD patients, which further disassociated with glucose metabolism in the motor cortex.

Positron emission tomography in the diagnosis and management of primary pediatric lung tumors.

Primary pediatric lung tumors are uncommon and have many overlapping clinical and imaging features. In contrast to adult lung tumors, these rare pediatric neoplasms have a relatively broad histologic spectrum. Informed by a single-institution 13-year retrospective record review, we present an overview of the most common primary pediatric lung neoplasms, with a focus on the role of positron emission tomography (PET), specifically 18F-fluorodeoxyglucose (FDG) PET and 68Ga-DOTATATE PET, in the management of primary pediatric lung tumors. In addition to characteristic conventional radiographic and cross-sectional imaging findings, knowledge of patient age, underlying cancer predisposition syndromes, and PET imaging features may help narrow the differential. While metastases from other primary malignancies remain the most commonly encountered pediatric lung malignancy, the examples presented in this pictorial essay highlight many of the important conventional radiologic and PET imaging features of primary pediatric lung malignancies.

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone. MATERIAL AND METHODS: All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. RESULTS: One hundred and fifty-four PSMA PET/MRI scans were available (133 [68Ga]Ga-PSMA-11 and 21 [18F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (n = 124) and a test cohort (n = 30). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5 ± 15.6%. CONCLUSION: Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.

Association between gene expression and functional-metabolic architecture in Parkinson's disease.

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18 F-fluorodeoxy glucose positron emission tomography/MR images.

Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using features from 18 F-fluorodeoxy glucose (18 F-FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) who underwent 18 F-FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in 18 F-FDG PET/MR images. Single-modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics-based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three-fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T2 -weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single-ROI radiomics models. The combined model using features from the tumoral region in T2 -weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that 18 F-FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics-based method integrating features from the tumoral and peritumoral regions in 18 F-FDG PET/MR images gave a superior performance for evaluating PMI.

The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer.

PURPOSE: To evaluate the diagnostic accuracy of [18F]-DCFPyL PET/MRI radiomics for the prediction of pathological grade group in prostate cancer (PCa) in therapy-naïve patients. METHODS: Patients with confirmed or suspected PCa, who underwent [18F]-DCFPyL PET/MRI (n = 105), were included in this retrospective analysis of two prospective clinical trials. Radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines. Histopathology obtained from systematic and targeted biopsies of the PET/MRI-detected lesions was the reference standard. Histopathology patterns were dichotomized as ISUP GG 1-2 vs. ISUP GG ≥ 3 categories. Different single-modality models were defined for feature extraction, including PET- and MRI-derived radiomic features. The clinical model included age, PSA, and lesions' PROMISE classification. Single models, as well as different combinations of them, were generated to calculate their performances. A cross-validation approach was used to evaluate the internal validity of the models. RESULTS: All radiomic models outperformed the clinical models. The best model for grade group prediction was the combination of PET + ADC + T2w radiomic features, showing sensitivity, specificity, accuracy, and AUC of 0.85, 0.83, 0.84, and 0.85, respectively. The MRI-derived (ADC + T2w) features showed sensitivity, specificity, accuracy, and AUC of 0.88, 0.78, 0.83, and 0.84, respectively. PET-derived features showed 0.83, 0.68, 0.76, and 0.79, respectively. The baseline clinical model showed 0.73, 0.44, 0.60, and 0.58, respectively. The addition of the clinical model to the best radiomic model did not improve the diagnostic performance. The performances of MRI and PET/MRI radiomic models as per the cross-validation scheme yielded an accuracy of 0.80 (AUC = 0.79), whereas clinical models presented an accuracy of 0.60 (AUC = 0.60). CONCLUSION: The combined [18F]-DCFPyL PET/MRI radiomic model was the best-performing model and outperformed the clinical model for pathological grade group prediction, indicating a complementary value of the hybrid PET/MRI model for non-invasive risk stratification of PCa. Further prospective studies are required to confirm the reproducibility and clinical utility of this approach.

Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.

BACKGROUND: Hybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to investigate the effect of recently proposed fuzzy radiomics and compare its predictive performance to conventional radiomics in cancer imaging cohorts. In addition, lesion vs. lesion+surrounding fuzzy and conventional radiomic analysis was conducted. METHODS: Previously published 11C Methionine (MET) positron emission tomography (PET) glioma, 18F-FDG PET/computed tomography (CT) lung, and 68GA-PSMA-11 PET/magneto-resonance imaging (MRI) prostate cancer retrospective cohorts were included in the analysis to predict their respective clinical endpoints. Four delineation methods including manually defined reference binary (Ref-B), its smoothed, fuzzified version (Ref-F), as well as extended binary (Ext-B) and its fuzzified version (Ext-F) were incorporated to extract imaging biomarker standardization initiative (IBSI)-conform radiomic features from each cohort. Machine learning for the four delineation approaches was performed utilizing a Monte Carlo cross-validation scheme to estimate the predictive performance of the four delineation methods. RESULTS: Reference fuzzy (Ref-F) delineation outperformed its binary delineation (Ref-B) counterpart in all cohorts within a volume range of 938-354987 mm3 with relative cross-validation area under the receiver operator characteristics curve (AUC) of +4.7-10.4. Compared to Ref-B, the highest AUC performance difference was observed by the Ref-F delineation in the glioma cohort (Ref-F: 0.74 vs. Ref-B: 0.70) and in the prostate cohort by Ref-F and Ext-F (Ref-F: 0.84, Ext-F: 0.86 vs. Ref-B: 0.80). In addition, fuzzy radiomics decreased feature redundancy by approx. 20%. CONCLUSIONS: Fuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared to conventional binary radiomics in PET. We hypothesize that this effect is due to the ability of fuzzy radiomics to model partial volume effects and delineation uncertainties at small lesion boundaries. In addition, we consider that the lower redundancy of fuzzy radiomic features supports the identification of imaging biomarkers in future studies. Future studies shall consider systematically analyzing lesions and their surroundings with fuzzy and binary radiomics.

[18F]FDG PET-MRI provides survival biomarkers in primary central nervous system lymphoma in the elderly: an ancillary study from the BLOCAGE trial of the LOC network.

PURPOSE: Primary central nervous system lymphoma (PCNSL) incidence is rising among elderly patients, presenting challenges due to poor prognosis and treatment-related toxicity risks. This study explores the potential of combining [18F]fluorodeoxyglucose ([18F]FDG) PET scans and multimodal MRI for improving management in elderly patients with de novo PCNSL. METHODS: Immunocompetent patients over 60 years with de novo PCNSL were prospectively enrolled in a multicentric study between January 2016 and April 2021. Patients underwent brain [18F]FDG PET-MRI before receiving high-dose methotrexate-based chemotherapy. Relationships between extracted PET (metabolic tumor volume (MTV), sum of MTV for up to five lesions (sumMTV), metabolic imaging lymphoma aggressiveness score (MILAS)) and MRI parameters (tumor contrast-enhancement size, cerebral blood volume (CBV), cerebral blood flow (CBF), apparent diffusion coefficient (ADC)) and treatment response and outcomes were analyzed. RESULTS: Of 54 newly diagnosed diffuse large B-cell PCNSL patients, 52 had positive PET and MRI with highly [18F]FDG-avid and contrast-enhanced disease (SUVmax: 27.7 [22.8-36]). High [18F]FDG uptake and metabolic volume were significantly associated with low ADCmean values and high CBF at baseline. Among patients, 69% achieved an objective response at the end of induction therapy, while 17 were progressive. Higher cerebellar SUVmean and lower sumMTV at diagnosis were significant predictors of complete response: 6.4 [5.7-7.7] vs 5.4 [4.5-6.6] (p = 0.04) and 5.5 [2.1-13.3] vs 15.9 [4.2-19.5] (p = 0.01), respectively. Two-year overall survival (OS) was 71%, with a median progression-free survival (PFS) of 29.6 months and a median follow-up of 37 months. Larger tumor volumes on PET or enhanced T1-weighted MRI were significant predictors of poorer OS, while a high MILAS score at diagnosis was associated with early death (< 1 year). CONCLUSION: Baseline cerebellar metabolism and sumMTV may predict response to end of chemotherapy in PCNSL. Tumor volume and MILAS at baseline are strong prognostic factors.

Feasibility of [18F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study.

PURPOSE: MRI and PET are used in neuro-oncology for the detection and characterisation of lesions for malignancy to target surgical biopsy and to plan surgical resections or stereotactic radiosurgery. The critical role of short-chain fatty acids (SCFAs) in brain tumour biology has come to the forefront. The non-metabolised SCFA radiotracer, [18F]fluoropivalate (FPIA), shows low background signal in most tissues except eliminating organs and has appropriate human dosimetry. Tumour uptake of the radiotracer is, however, unknown. We investigated the uptake characteristics of FPIA in this pilot PET/MRI study. METHODS: Ten adult glioma subjects were identified based on radiological features using standard-of-care MRI prior to any surgical intervention, with subsequent histopathological confirmation of glioma subtype and grade (lower-grade - LGG - and higher-grade - HGG - patients). FPIA was injected as an intravenous bolus injection (range 342-368 MBq), and dynamic PET and MRI data were acquired simultaneously over 66 min. RESULTS: All patients tolerated the PET/MRI protocol. Three patients were reclassified following resection and histology. Tumour maximum standardised uptake value (SUVmax,60) increased in the order LGG (WHO grade 2) < HGG (WHO grade 3) < HGG (WHO grade 4). The net irreversible solute transfer, Ki, and influx rate constant, K1, were significantly higher in HGG (p < 0.05). Of the MRI variables studied, DCE-MRI-derived extravascular-and-extracellular volume fraction (ve) was high in tumours of WHO grade 4 compared with other grades (p < 0.05). SLC25A20 protein expression was higher in HGG compared with LGG. CONCLUSION: Tumoural FPIA PET uptake is higher in HGG compared to LGG. This study supports further investigation of FPIA PET/MRI for brain tumour imaging in a larger patient population. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov, NCT04097535.

The diagnostic accuracy of radiolabeled PSMA-ligand PET for tumour staging in newly diagnosed prostate cancer patients compared to histopathology: a systematic review and meta-analysis.

PURPOSE: The current clinical recommendations posit the deployment of specific approved radiolabeled prostate-specific membrane antigen-ligand positron emission tomography (PSMA PET) for detecting metastatic prostate cancer during primary staging. Nevertheless, the precise efficacy of such ligands in localizing intraprostatic tumours (index tumour) and T-staging is not well established. Consequently, the objective of this inquiry is to ascertain the diagnostic accuracy of PSMA-PET in the tumour staging of newly diagnosed prostate cancer by means of a meta-analysis that integrates studies utilizing histological confirmation as the reference standard. METHODS: In this study, we conducted a systematic literature search of the PubMed, Embase, Web of Science, and Cochrane Library databases using a predefined collection of search terms. These terms included 'PSMA PET', 'primary staging', and 'prostate cancer'. Subsequently, two independent reviewers evaluated all the studies based on predetermined inclusion criteria, extracted pertinent data, and assessed the quality of evidence. Any disparities were resolved by a third reviewer. A random effects Sidik-Jonkman model was applied to conduct a meta-analysis and estimate the diagnostic accuracy on a per-patient basis, along with 95% confidence intervals. Moreover, an appraisal regarding the likelihood of publication bias and the impact of small-study effects was performed utilizing both Egger's test and a graphical examination of the funnel plot. RESULTS: The present analysis comprised a total of twenty-three scientific papers encompassing 969 patients and involved their analysis by both qualitative and quantitative approaches. The results of this study demonstrated that the estimated diagnostic accuracy of PSMA PET/CT and PSMA PET/MRI, for the detection of intraprostatic tumours, regardless of the type of PSMA-ligand, was 86% (95% CI: 76-96%) and 97% (95% CI: 94-100%), respectively. Furthermore, the diagnostic accuracy for the detection of extraprostatic extension (EPE) was 73% (95% CI: 64-82%) and 77% (95% CI: 69-85%), while the diagnostic accuracy for the detection of seminal vesicle involvement (SVI) was 87% (95% CI: 80-93) and 90% (95% CI: 82-99%), respectively. CONCLUSION: The present investigation has demonstrated that PSMA PET/MRI surpasses currently recommended multiparametric magnetic resonance imaging (mpMRI) in terms of diagnostic accuracy as inferred from a notable data trajectory, whereas PSMA-PET/CT exhibited comparable diagnostic accuracy for intraprostatic tumour detection and T-staging compared to mpMRI. Nevertheless, the analysis has identified certain potential limitations, such as small-study effects and a potential for publication bias, which may impact the overall conclusions drawn from this study.

Glioblastoma PET/MRI: kinetic investigation of [18F]rhPSMA-7.3, [18F]FET and [18F]fluciclovine in an orthotopic mouse model of cancer.

PURPOSE: Glioblastoma multiforme (GBM) is the most common glioma and standard therapies can only slightly prolong the survival. Neo-vascularization is a potential target to image tumor microenvironment, as it defines its brain invasion. We investigate [18F]rhPSMA-7.3 with PET/MRI for quantitative imaging of neo-vascularization in GBM bearing mice and human tumor tissue and compare it to [18F]FET and [18F]fluciclovine using PET pharmacokinetic modeling (PKM). METHODS: [18F]rhPSMA-7.3, [18F]FET, and [18F]fluciclovine were i.v. injected with 10.5 ± 3.1 MBq, 8.0 ± 2.2 MBq, 11.5 ± 1.9 MBq (n = 28, GL261-luc2) and up to 90 min PET/MR imaged 21/28 days after surgery. Regions of interest were delineated on T2-weighted MRI for (i) tumor, (ii) brain, and (iii) the inferior vena cava. Time-activity curves were expressed as SUV mean, SUVR and PKM performed using 1-/2-tissue-compartment models (1TCM, 2TCM), Patlak and Logan analysis (LA). Immunofluorescent staining (IFS), western blotting, and autoradiography of tumor tissue were performed for result validation. RESULTS: [18F]rhPSMA-7.3 showed a tumor uptake with a tumor-to-background-ratio (TBR) = 2.1-2.5, in 15-60 min. PKM (2TCM) confirmed higher K1 (0.34/0.08, p = 0.0012) and volume of distribution VT (0.24/0.1, p = 0.0017) in the tumor region compared to the brain. Linearity in LA and similar k3 = 0.6 and k4 = 0.47 (2TCM, tumor, p = ns) indicated reversible binding. K1, an indicator for vascularization, increased (0.1/0.34, 21 to 28 days, p < 0.005). IFS confirmed co-expression of PSMA and tumor vascularization. [18F]fluciclovine showed higher TBR (2.5/1.8, p < 0.001, 60 min) and VS (1.3/0.7, p < 0.05, tumor) compared to [18F]FET and LA indicated reversible binding. VT increased (p < 0.001, tumor, 21 to 28 days) for [18F]FET (0.5-1.4) and [18F]fluciclovine (0.84-1.5). CONCLUSION: [18F]rhPSMA-7.3 showed to be a potential candidate to investigate the tumor microenvironment of GBM. Following PKM, this uptake was associated with tumor vascularization. In contrast to what is known from PSMA-PET in prostate cancer, reversible binding was found for [18F]rhPSMA-7.3 in GBM, contradicting cellular trapping. Finally, [18F]fluciclovine was superior to [18F]FET rendering it more suitable for PET imaging of GBM.