Whole-Body Positron Emission Tomography with 18F-Fluorodeoxyglucose/Magnetic Resonance Imaging as a Screening Tool for the Detection of Malignant Transformation in Individuals with Neurofibromatosis Type 1.

Malignant peripheral nerve sheath tumors (MPNSTs) are the leading cause of death in patients with neurofibromatosis type 1. They can result from premalignant neurofibromas, including neurofibromas with atypia and atypical neurofibromatous neoplasms of uncertain biologic potential. Some phenotypic characteristics have been described as associated with their development. The aim of this study was to outline our use of whole-body positron emission tomography with 18F-fluorodeoxyglucose/magnetic resonance imaging in adults with neurofibromatosis type 1, especially in the screening of asymptomatic individuals with a higher risk of developing an MPNST, and to study its impact on neurofibroma classification (malignant vs premalignant) and MPNST staging over time. Individuals with neurofibromatosis type 1 who underwent a positron emission tomography with 18F-fluorodeoxyglucose/magnetic resonance imaging between 2017 and 2021 were included, analyzing separately the screened population. Maximum standard uptake value and diffusion-weighted imaging were assessed. Biopsy/surgery confirmed the diagnosis. In all, 345 positron emission tomography with 18F-fluorodeoxyglucose/magnetic resonance imaging were performed in 241 patients, including 149 asymptomatic (62%) but at-risk patients. Eight MPNSTs in 8 screened individuals (5%), 6 neurofibromas with atypia in 4 individuals (3%), and 29 atypical neurofibromatous neoplasms of uncertain biologic potential in 23 individuals (15%) were diagnosed. Over time, the proportion of grade 3 MPNST and the malignant/premalignant ratio in screened individuals significantly decreased (P = .03 and P < .001, respectively). This study emphasizes the diagnostic and screening performances of whole-body positron emission tomography with 18F-fluorodeoxyglucose/magnetic resonance imaging in adults with neurofibromatosis type 1.

Accuracy and reproducibility of a cone beam CT-based virtual parenchymal perfusion algorithm in the prediction of SPECT/CT anatomical and volumetric results during the planification of radioembolization for HCC.

OBJECTIVES: To evaluate anatomical and volumetric predictability of a cone beam computed tomography (CBCT)-based virtual parenchymal perfusion (VPP) software for the single-photon-emission computed tomography (SPECT)/CT imaging results during the work-up for transarterial radioembolization (TARE) procedure in patients with hepatocellular carcinoma (HCC). METHODS: VPP was evaluated retrospectively on CBCT data of patients treated by TARE for HCC. 99mTc macroaggregated albumin particles (99mTc-MAA) uptake territories on work-up SPECT/CT was used as ground truth for the evaluation. Semi-quantitative evaluation consisted of the ranking of visual consistency of the parenchymal enhancement and portal vein tumoral involvement on VPP and 99mTc-MAA SPECT/CT, using a three-rank scale and two-rank scale, respectively. Inter-reader agreement was evaluated using a kappa coefficient. Quantitative evaluation included absolute volume error calculation and Pearson correlation between volumes enhanced territories on VPP and 99mTc-MAA SPECT/CT. RESULTS: Fifty-two CBCTs were performed in 33 included patients. Semi-quantitative evaluation showed a good concordance between actual 99mTc-MAA uptake and the virtual enhanced territories in 73% and 75% of cases; a mild concordance in 12% and 10% and a poor concordance in 15%, for the two readers. Kappa coefficient was 0.86. Portal vein involvement evaluation showed a good concordance in 58.3% and 66.7% for the two readers, respectively, with a kappa coefficient of 0.82. Quantitative evaluation showed a volume error of 0.46 ± 0.78 mL [0.01-3.55], and Pearson R2 factor at 0.75 with a p value < 0.01. CONCLUSION: CBCT-based VPP software is accurate and reliable to predict 99mTc-MAA SPECT/CT anatomical and volumetric results in HCC patients during TARE. KEY POINTS: • Virtual parenchymal perfusion (VPP) software is accurate and reliable in the prediction of 99mTc-MAA SPECT volumetric and targeting results in HCC patients during transarterial radioembolization (TARE). • VPP software may be used per-operatively to optimize the microcatheter position for 90Y infusion allowing precise tumor targeting while preserving non-tumoral parenchyma. • Post-operatively, VPP software may allow an accurate estimation of the perfused volume by each arterial branch and, thus, a precise 90Y dosimetry for TARE procedures.

Clinical impact of dual-tracer FDOPA and FDG PET/CT for the evaluation of patients with parkinsonian syndromes.

Parkinsonian syndromes include typical cases of idiopathic Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) associated with cognitive and vegetative disorders, which are more challenging to diagnose. The aim of this study was to assess -the value of dual-tracer imaging 6-fluoro-(18F)-L-DOPA (FDOPA) and fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT), performed in routine patients demonstrating extrapyramidal signs and cognitive complains, for the diagnosis and management of parkinsonian syndromes.We retrospectively included 143 consecutive patients who underwent both FDOPA PET/CT (for the evaluation of parkinsonism) and FDG PET/CT (for the evaluation of cognitive complaints) in the same institution. The suspected clinical diagnosis before imaging and the final post-imaging diagnosis were collected by a dedicated questionnaire.FDOPA was pathological in 90.2% of cases, including 74.1% of PD, 3.5% of parkinsonian dementia and 7% of APS. FDG was normal or near normal in 58.7% of patients. A pattern of diffuse cortical hypometabolism was observed in the remaining patients, more frequently in APS than in PD patients (P = .001). Importantly, in 7.7% of cases dual-tracer PET/CT allowed to decide between several diagnostic hypotheses and led to a new diagnosis in 14.0%. Therefore, the management of these patients was modified, with clinical re-evaluation in a specialized unit and a control of neuropsychological tests and imaging.Dual-tracer PET/CT imaging may be a precious help in the diagnosis and management of parkinsonian syndromes.

Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach.

INTRODUCTION: Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles. METHODS: 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. RESULTS: The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. CONCLUSION: We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.