A prospective clinical study of the influence of oral protein intake on [18F]FET-PET uptake and test-retest repeatability in glioma.

BACKGROUND: O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET PET) scanning is used in routine clinical management and evaluation of gliomas with a recommended 4 h prior fasting. Knowledge of test-retest variation of [18F]FET PET imaging uptake metrics and the impact of accidental protein intake can be critical for interpretation. The aim of this study was to investigate the repeatability of [18F]FET-PET metrics and to assess the impact of protein-intake prior to [18F]FET PET scanning of gliomas. RESULTS: Test-retest variability in the non-protein group was good with absolute (and relative) upper and lower limits of agreement of + 0.15 and - 0.13 (+ 9.7% and - 9.0%) for mean tumour-to-background ratio (TBRmean), + 0.43 and - 0.28 (+ 19.6% and - 11.8%) for maximal tumour-to-background ratio (TBRmax), and + 2.14 cm3 and - 1.53 ml (+ 219.8% and - 57.3%) for biological tumour volume (BTV). Variation was lower for uptake ratios than for BTV. Protein intake was associated with a 27% increase in the total sum of plasma concentration of the L-type amino acid transporter 1 (LAT1) relevant amino acids and with decreased standardized uptake value (SUV) in both healthy appearing background brain tissue (mean SUV - 25%) and in tumour (maximal SUV - 14%). Oral intake of 24 g of protein 1 h prior to injection of tracer tended to increase variability, but the effects on derived tumour metrics TBRmean and TBRmax were only borderline significant, and changes generally within the variability observed in the group with no protein intake. CONCLUSION: The test-retest repeatability was found to be good, and better for TBRmax and TBRmean than BTV, with the methodological limitation that tumour growth may have influenced results. Oral intake of 24 g of protein one hour before a [18F]FET PET scan decreases uptake of [18F]FET in both tumour and in healthy appearing brain, with no clinically significant difference on the most commonly used tumour metrics.

The cingulate island sign in a mixed memory clinical cohort: Prevalence and diagnostic accuracy.

INTRODUCTION: Visual rating of the cingulate island sign (CIS) on [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) has a high specificity for dementia with Lewy bodies (DLB) in selected cohorts such as DLB versus Alzheimer's disease (AD). In a mixed memory clinical population this study aimed to uncover the prevalence of CIS, the diagnostic accuracy for DLB, and the relationship between CIS and disease severity. METHODS: CIS on [18F]FDG-PET was retrospectively assessed with the visual CIS rating scale (CISRs) in 1000 patients with a syndrome diagnosis of mild cognitive impairment (MCI) or dementia with no restrictions in etiological diagnosis. RESULTS: In this cohort 24.3 % had a CISRs score ≥1 and 3.5 % had a CISRs score = 4. The prevalence of a CISRs score ≥1 was highest in DLB (74.0 %, n = 57). A CISRs score ≥1 was present in at least 9 % in other diagnostic groups. The prevalence of CIS across disease severities showed no statistically significant difference (p = 0.23). To differentiate DLB from non-DLB the optimal cut-off was a CISRs score ≥1 (balanced accuracy = 77.1 %) in MCI/mild dementia and a CISRs score ≥2 (balanced accuracy = 80.6 %) in moderate/severe dementia. The positive predictive value of a CISRs score = 4 for DLB was 57.7 % in MCI/mild dementia and 33.3 % in moderate/severe dementia. CONCLUSION: The CISRs is useful in differentiating DLB from other etiologies in a mixed memory clinical population. Balanced accuracy and positive predictive value may vary across disease severities in the population studied.

Expanding the Spectrum of Stress-Induced Childhood-Onset Neurodegeneration with Variable Ataxia and Seizures (CONDSIAS).

Stress-induced childhood-onset neurodegeneration with variable ataxia and seizures (CONDSIAS) is an extremely rare, autosomal recessive neurodegenerative disorder. It is caused by biallelic pathogenic variants in the ADPRS gene, which encodes an enzyme involved in DNA repair, and is characterized by exacerbations in relation to physical or emotional stress, and febrile illness. We report a 24-year-old female, who was compound heterozygous for two novel pathogenic variants revealed by whole exome sequencing. Additionally, we summarize the published cases of CONDSIAS. In our patient, onset of symptoms occurred at 5 years of age and consisted of episodes of truncal dystonic posturing, followed half a year later by sudden diplopia, dizziness, ataxia, and gait instability. Progressive hearing loss, urinary urgency, and thoracic kyphoscoliosis ensued. Present neurological examination revealed dysarthria, facial mini-myoclonus, muscle weakness and atrophy of hands and feet, leg spasticity with clonus, truncal and appendicular ataxia, and spastic-ataxic gait. Hybrid [18F]-fluorodeoxyglucose (FDG) positron emission tomography/magnetic resonance imaging (PET/MRI) of the brain revealed cerebellar atrophy, particularly of the vermis, with corresponding hypometabolism. MRI of the spinal cord showed mild atrophy. After informed consent from the patient, we initiated experimental, off-label treatment with minocycline, a poly-ADP-polymerase (PARP) inhibitor, which has shown beneficial effects in a Drosophila fly model. The present case report expands the list of known pathogenic variants in CONDIAS and presents details of the clinical phenotype. Future studies will reveal whether PARP inhibition is an effective treatment strategy for CONDIAS.

Long-term prognostic value of [15O]H2O PET imaging in patients suspected for cerebral hemodynamic insufficiency.

OBJECTIVES: Quantitative regional cerebral perfusion (rCBF) measurements using [15O]H2O PET with arterial cannulation and acetazolamide (ACZ) challenge have been reserved to identify high-risk patients that are candidates for by-pass operation. We aimed to assess the prognostic value of various parameters in quantitative [15O]H2O PET measurements in patients not subsequently undergoing surgery. METHODS: We identified 32 eligible patients who underwent [15O]H2O brain PET imaging for suspicion of hemodynamic insufficiency between 2009 and 2020. Cerebrovascular events were defined as new ischemic lesions on MRI, stroke, transient ischemic attack, vascular dementia. Follow-up period was 91 months (range: 26-146). rCBF before (rCBFbase) and after (rCBFacz) ACZ challenge and the relative increase (CVR), were examined in the anterior (ACA), middle (MCA), and posterior (PCA) cerebral artery territories of the affected hemisphere, and the most recent MRI scans were scored for infarcts and white matter lesions. RESULTS: Receiver operating characteristic (ROC) curve analysis showed higher prognostic accuracy for rCBFacz(AUC:0.82) compared to CVR (AUC:0.72) and rCBFbase (AUC:0.77). ROC AUC, optimal thresholds (and corresponding sensitivity/specificity/accuracy) for rCBFacz after ACZ in individual territories were 0.79 and 37.8 mL 100g-1 min-1 (0.81/0.63/0.72) for the ACA, 0.84 and 32 mL 100g-1 min-1 (0.81/0.75/0.78) for the MCA, and 0.70 and 43.9 ml/(mL 100g-1 min-1 (0.81/0.43/0,62) for the PCA. Kaplan Meier survival curve showed longer event-free survival in patients with rCBFacz below cut-off (p=0.007). In multivariate analysis rCBFacz remained a significant predictor when correcting for age. CONCLUSION: Quantitative rCBF measurements after ACZ challenge with [15O]H2O PET provided high prognostic value for future cerebrovascular events.

Estimation of brain amyloid accumulation using deep learning in clinical [11C]PiB PET imaging.

INTRODUCTION: Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. METHODS: A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. RESULTS: The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. CONCLUSION: The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.

Clinical validation of the cingulate island sign visual rating scale in dementia with Lewy bodies.

INTRODUCTION: The cingulate island sign (CIS) is a metabolic pattern on [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) associated with dementia with Lewy bodies (DLB). The aim of this study was to validate the visual CIS rating scale (CISRs) for the diagnosis of DLB and to explore the clinical correlates. METHODS: This single-center study included 166 DLB patients and 161 patients with Alzheimer's disease (AD). The CIS on [18F]FDG-PET scans was rated using the CISRs independently by three blinded raters. RESULTS: The optimal cut-off to differentiate DLB from AD was a CISRs score ≥ 1 (sensitivity = 66%, specificity = 84%) whereas a CISRs score ≥ 2 (sensitivity = 58%, specificity = 92%) was optimal to differentiate amyloid positive DLB (n = 43 (82.7%)) and AD. To identify DLB with abnormal (n = 53 (72.6%)) versus normal (n = 20 (27.4%)) dopamine transporter imaging, a CISRs cut-off of 4 had a specificity of 95%. DLB with a CISRs score of 4 performed significantly better in tests on free verbal recall and picture based cued recall, but worse on processing speed compared to DLB with a CISRs score of 0. CONCLUSION: This study confirms the CISRs as a valid marker for the diagnosis of DLB with a high specificity and a lower, but acceptable, sensitivity. Concomitant AD pathology does not influence diagnostic accuracy of the CISRs. In DLB patients, presence of CIS is associated with relative preserved memory function and impaired processing speed.

DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants.

Purpose: Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants. Methods: The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC. Results: Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method. Conclusion: We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer's disease.

Purpose: Brain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer's disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient's own MRI, and showcase its applicability in detecting AD pathology. Methods: We included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities. Results: The model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects. Conclusion: This work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.

Deep learning based low-activity PET reconstruction of [11C]PiB and [18F]FE-PE2I in neurodegenerative disorders.

PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [18F]FE-PE2I and [11C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising. METHODS: A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [11C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [18F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort. RESULTS: Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated < 2% compared to ground-truth values, which were not significantly changed when extracting the metrics from the denoised images. CONCLUSION: The presented models were based on the same network architecture and proved to be a robust tool for denoising brain PET images with two widely different tracer distributions (delocalized, ([11C]PiB, and highly localized, [18F]FE-PE2I). This broad and robust application makes the presented network a good choice for improving the quality of brain images to the level of the standard-activity images without degrading clinical metric extraction. This will allow for reduced dose or scan time in PET/CT to be implemented clinically.

Widening the spectrum of spinocerebellar ataxia autosomal recessive type 10 (SCAR10).

Biallelic pathogenic variants in the ANO10 gene cause spinocerebellar ataxia recessive type 10. We report two patients, both compound heterozygous for ANO10 variants, including two novel variants. Both patients had onset of cerebellar ataxia in adulthood with slow progression and presented corticospinal tract signs, eye movement abnormalities and cognitive executive impairment. One of them had temporal lobe epilepsy and she also carried a heterozygous variant in CACNB4, a potential risk gene for epilepsy. Both patients had pronounced cerebellar atrophy on cerebral magnetic resonance imaging (MRI) and reduced metabolic activity in cerebellum as well as in the frontal lobes on 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography ((18F)FDG PET) scans. We provide comprehensive clinical, radiological and genetic data on two patients carrying likely pathogenic ANO10 gene variants. Furthermore, we provide evidence for a cerebellar as well as a frontal involvement on brain (18F)FDG PET scans which has not previously been reported.

Automatic detection and delineation of pediatric gliomas on combined [18F]FET PET and MRI.

Introduction: Brain and central nervous system (CNS) tumors are the second most common cancer type in children and adolescents. Positron emission tomography (PET) imaging with radiolabeled amino acids visualizes the amino acid uptake in brain tumor cells compared with the healthy brain tissue, which provides additional information over magnetic resonance imaging (MRI) for differential diagnosis, treatment planning, and the differentiation of tumor relapse from treatment-related changes. However, tumor delineation is a time-consuming task subject to inter-rater variability. We propose a deep learning method for the automatic delineation of O-(2-[18F]fluoroethyl)-l-tyrosine ([18F]FET PET) pediatric CNS tumors. Methods: A total of 109 [18F]FET PET and MRI scans from 66 pediatric patients with manually delineated reference were included. We trained an artificial neural network (ANN) for automatic delineation and compared its performance against the manual reference on delineation accuracy and subsequent clinical metric accuracy. For clinical metrics, we extracted the biological tumor volume (BTV) and tumor-to-background mean and max (TBRmean and TBRmax). Results: The ANN produced high tumor overlap (median dice-similarity coefficient [DSC] of 0.93). The clinical metrics extracted with the manual reference and the ANN were highly correlated (r ≥ 0.99). The spatial location of TBRmax was identical in almost all cases (96%). The ANN and the manual reference produced similar changes in the clinical metrics between baseline and follow-up scans. Conclusion: The proposed ANN achieved high concordance with the manual reference and may be an important tool for decision aid, limiting inter-reader variance and improving longitudinal evaluation in clinical routine, and for future multicenter studies of pediatric CNS tumors.

Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer's disease.

BACKGROUND: The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer's disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting. OBJECTIVE: The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer's disease. METHODS: Eighty-one patients clinically suspected of Alzheimer's disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0-100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course. RESULTS: The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer's disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET. CONCLUSION: The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer's disease compared to 2-[18F]FDG-PET.

C9ORF72 hexanucleotide repeat expansion with Alzheimer's disease-like clinical phenotype: A case report with results from neuropsychology, CSF, FDG-PET, and PiB-PET.

A thorough family history and relevant investigation program are essential to settle accurate diagnosis when clinical presentation is atypical or with a mixed picture.

AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size.

INTRODUCTION: Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derived. Deep learning-based image-to-image translation networks present itself as an optimal solution for MRI-derived AC (MR-AC). High robustness and generalizability of these networks are expected to be achieved through large training cohorts. In this study, we implemented an MR-AC method based on deep learning, and investigated how training cohort size, transfer learning, and MR input affected robustness, and subsequently evaluated the method in a clinical setup, with the overall aim to explore if this method could be implemented in clinical routine for PET/MRI examinations. METHODS: A total cohort of 1037 adult subjects from the Siemens Biograph mMR with two different software versions (VB20P and VE11P) was used. The software upgrade included updates to all MRI sequences. The impact of training group size was investigated by training a convolutional neural network (CNN) on an increasing training group size from 10 to 403. The ability to adapt to changes in the input images between software versions were evaluated using transfer learning from a large cohort to a smaller cohort, by varying training group size from 5 to 91 subjects. The impact of MRI sequence was evaluated by training three networks based on the Dixon VIBE sequence (DeepDixon), T1-weighted MPRAGE (DeepT1), and ultra-short echo time (UTE) sequence (DeepUTE). Blinded clinical evaluation relative to the reference low-dose CT (CT-AC) was performed for DeepDixon in 104 independent 2-[18F]fluoro-2-deoxy-d-glucose ([18F]FDG) PET patient studies performed for suspected neurodegenerative disorder using statistical surface projections. RESULTS: Robustness increased with group size in the training data set: 100 subjects were required to reduce the number of outliers compared to a state-of-the-art segmentation-based method, and a cohort >400 subjects further increased robustness in terms of reduced variation and number of outliers. When using transfer learning to adapt to changes in the MRI input, as few as five subjects were sufficient to minimize outliers. Full robustness was achieved at 20 subjects. Comparable robust and accurate results were obtained using all three types of MRI input with a bias below 1% relative to CT-AC in any brain region. The clinical PET evaluation using DeepDixon showed no clinically relevant differences compared to CT-AC. CONCLUSION: Deep learning based AC requires a large training cohort to achieve accurate and robust performance. Using transfer learning, only five subjects were needed to fine-tune the method to large changes to the input images. No clinically relevant differences were found compared to CT-AC, indicating that clinical implementation of our deep learning-based MR-AC method will be feasible across MRI system types using transfer learning and a limited number of subjects.

Evaluating 2-[18F]FDG-PET in differential diagnosis of dementia using a data-driven decision model.

2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[18F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[18F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[18F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[18F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.

A visual rating scale for cingulate island sign on 18F-FDG-PET to differentiate dementia with Lewy bodies and Alzheimer's disease.

Valid diagnosis of dementia with Lewy bodies (DLB) is essential to establish appropriate treatment and care. However, the diagnostic accuracy is complicated by clinical and pathological overlap with Alzheimer's disease (AD). Cingulate island sign (CIS), defined as sparing of posterior cingulate cortex (PCC) relative to precuneus and cuneus on 18F-fluoro-deoxy-glucose positron emission tomography (18F-FDG-PET), is included in the revised diagnostic DLB criteria. There are no guidelines for the visual grading of CIS, although visual rating is a fast-applicable method in a clinical setting. The objective was to develop a robust visual CIS scale and evaluate the performance in differentiating DLB with and without amyloid beta pathology (Aß+/-), and AD. 18F-FDG-PET scans from 35 DLB patients, 36 AD patients, and 23 healthy controls were rated according to a visual CIS scale based on specific reading criteria. The visual CIS scale was validated against a quantitative CIS ratio derived from a region of interest analysis of PCC, precuneus, and cuneus. DLB patients had a significantly higher visual CIS score compared to AD patients, and controls. A cut-off visual CIS score of 4 significantly differentiated DLB Aß- patients from DLB Aß+ patients. In conclusion, the visual CIS scale is clinically useful to differentiate DLB from AD. The degree of CIS may be related to Aß pathology in DLB patients.

Hashimoto's encephalopathy: Follow-up data from neuropsychology, lumbar puncture, and FDG-PET.

Hashimoto's encephalopathy is a rare disease with nonspecific symptoms, associated with elevated levels of anti-TPO and/or anti-TG. It can be potentially fatal. However, it is responsive to steroid and treated in due time, it can be fully reversible.

Hybrid FDG PET/MRI vs. FDG PET and CT in patients with suspected dementia - A comparison of diagnostic yield and propagated influence on clinical diagnosis and patient management.

BACKGROUND: Both 18F-fluoro-deoxy-glucose (FDG) positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI) are routinely used in the evaluation of memory clinic patients. Hybrid PET/MR systems now allow simultaneous PET and MRI imaging within the duration of the PET emission scan. PURPOSE: To compare the diagnostic yield of PET/MRI using an abbreviated MR protocol with that of separate PET and CT in a mixed memory clinic population, and the propagated influences on clinical diagnosis and patient management. MATERIAL AND METHODS: Consecutive memory clinic patients (n = 78) undergoing both CT and hybrid FDG PET/MRI scans were identified retrospectively. MRI and CT were separately evaluated for vascular and structural pathology. PET scans were classified according to the presence of neurodegenerative or vascular disease using CT or MRI, respectively, for anatomical guiding. A memory clinic expert assessed the clinical impact of the additional findings and/or change of PET classification achieved by MRI anatomical guiding as compared to CT guiding. RESULTS: MRI lead to significantly higher Fazekas scores, higher medial temporal and global cortical atrophy scores, and identified more patients with infarcts (28 vs 8, p<0.001) compared to CT. MRI changed PET classification in 13 (17%) patients. Addition of MRI to CT had minor clinical impact in 4/78 (5%) and major clinical impact in 13/78 (17%) of patients. CONCLUSION: The study demonstrates the capabilities of PET/MRI systems for routine clinical imaging of memory clinic patients, and that even an abbreviated hybrid PET/MRI protocol provides significant additional information influencing clinical diagnosis and patient management in a substantial fraction of patients when compared to separate PET and CT.

Early Postoperative 18F-FET PET/MRI for Pediatric Brain and Spinal Cord Tumors.

Complete resection is the treatment of choice for most pediatric brain tumors, but early postoperative MRI for detection of residual tumor may be misleading because of MRI signal changes caused by the operation. PET imaging with amino acid tracers in adults increases the diagnostic accuracy for brain tumors, but the literature in pediatric neurooncology is limited. A hybrid PET/MRI system is highly beneficial in children, reducing the number of scanning procedures, and this is to our knowledge the first larger study using PET/MRI in pediatric neurooncology. We evaluated if additional postoperative 18F-fluoro-ethyl-tyrosine (18F-FET) PET in children and adolescents would improve diagnostic accuracy for the detection of residual tumor as compared with MRI alone and would assist clinical management. Methods: Twenty-two patients (7 male; mean age, 9.5 y; range, 0-19 y) were included prospectively and consecutively in the study and had 27 early postoperative 18F-FET PET exams performed preferentially in a hybrid PET/MRI system (NCT03402425). Results: Using follow-up (93%) or reoperation (7%) as the reference standard, PET combined with MRI discriminated tumor from treatment effects with a lesion-based sensitivity/specificity/accuracy (95% confidence intervals) of 0.73 (0.50-1.00)/1.00 (0.74-1.00)/0.87 (0.73-1.00) compared with MRI alone: 0.80 (0.57-1.00)/0.75 (0.53-0.94)/0.77 (0.65-0.90); that is, the specificity for PET/MRI was 1.00 as compared with 0.75 for MRI alone (P = 0.13). In 11 of 27 cases (41%), results from the 18F-FET PET scans added relevant clinical information, including one scan that directly influenced clinical management because an additional residual tumor site was identified. 18F-FET uptake in reactive changes was frequent (52%), but correct interpretation was possible in all cases. Conclusion: The high specificity for detecting residual tumor suggests that supplementary 18F-FET PET is relevant in cases where reoperation for residual tumor is considered.

Electromagnetic source imaging in presurgical workup of patients with epilepsy: A prospective study.

OBJECTIVE: To determine the diagnostic accuracy and clinical utility of electromagnetic source imaging (EMSI) in presurgical evaluation of patients with epilepsy. METHODS: We prospectively recorded magnetoencephalography (MEG) simultaneously with EEG and performed EMSI, comprising electric source imaging, magnetic source imaging, and analysis of combined MEG-EEG datasets, using 2 different software packages. As reference standard for irritative zone (IZ) and seizure onset zone (SOZ), we used intracranial recordings and for localization accuracy, outcome 1 year after operation. RESULTS: We included 141 consecutive patients. EMSI showed localized epileptiform discharges in 94 patients (67%). Most of the epileptiform discharge clusters (72%) were identified by both modalities, 15% only by EEG, and 14% only by MEG. Agreement was substantial between inverse solutions and moderate between software packages. EMSI provided new information that changed the management plan in 34% of the patients, and these changes were useful in 80%. Depending on the method, EMSI had a concordance of 53% to 89% with IZ and 35% to 73% with SOZ. Localization accuracy of EMSI was between 44% and 57%, which was not significantly different from MRI (49%-76%) and PET (54%-85%). Combined EMSI achieved significantly higher odds ratio compared to electric source imaging and magnetic source imaging. CONCLUSION: EMSI has accuracy similar to established imaging methods and provides clinically useful, new information in 34% of the patients. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that EMSI had a concordance of 53%-89% and 35%-73% (depending on analysis) for the localization of epileptic focus as compared with intracranial recordings-IZ and SOZ, respectively.