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Purpose: [123I]FP-CIT (DaTSCAN®) single-photon emission computed tomography (SPECT) imaging is widely used to study neurodegenerative parkinsonism, by measuring presynaptic dopamine transporter (DAT) in striatal regions. Beyond DAT, [123I]FP-CIT may be considered for other monoaminergic systems, in particular the serotonin transporter (SERT). Independent component analysis (ICA) implemented in source-based morphometry (SBM) could represent an alternative method to explore monoaminergic pathways, studying the relationship among voxels and grouping them into "neurotransmission" networks.Procedures: One hundred forty-three subjects [84 with Parkinson's disease (PD) and 59 control individuals (CG)] underwent DATSCAN® imaging. The [123I]FP-CIT binding was evaluated by multivariate SBM approach, as well as by a whole-brain voxel-wise univariate (statistical parametric mapping, SPM) approach.Results: As compared to the univariate whole-brain approach (SPM) (only demonstrating striatal [123I]FP-CIT binding reduction in PD group), SBM identified six sources of non-artefactual origin, including basal ganglia and cortical regions as well as brainstem. Among them, three sources (basal ganglia and cortical regions) presented loading scores (as index of [123I]FP-CIT binding) significantly different between PD and CG. Notably, even if not significantly different between PD and CG, the remaining three non-artefactual sources were characterized by a predominant frontal, brainstem, and occipito-temporal involvement.Conclusion: The concept of source blind separation by the application of ICA (as implemented in SBM) represents a feasible approach to be considered in [123I]FP-CIT (DaTSCAN®) SPECT imaging. Taking advantage of this multivariate analysis, specific patterns of variance can be identified (involving either striatal than extrastriatal regions) that could be useful in differentiating neurodegenerative parkinsonisms. [ABSTRACT FROM AUTHOR]

Background: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing 90 Y selective internal radiation therapy (SIRT)., Materials/methods: This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with 90 Y SIRT. The patients underwent personalized treatment planning and voxel-wise dosimetry. After the procedure, the OS and PFS were evaluated. Three structures were delineated including tumoral liver (TL), normal perfused liver (NPL), and whole normal liver (WNL). 289 dose-volume constraints (DVCs) were extracted from dose-volume histograms of physical and biological effective dose (BED) maps calculated on 99m Tc-MAA and 90 Y SPECT/CT images. Subsequently, the DVCs and 16 clinical biomarkers were used as features for univariate and multivariate analysis. Cox proportional hazard ratio (HR) was employed for univariate analysis. HR and the concordance index (C-Index) were calculated for each feature. Using eight different strategies, a cross-combination of various models and feature selection (FS) methods was applied for multivariate analysis. The performance of each model was assessed using an averaged C-Index on a three-fold nested cross-validation framework. The Kaplan-Meier (KM) curve was employed for univariate and machine learning (ML) model performance assessment., Results: The median OS was 11 months [95% CI: 8.5, 13.09], whereas the PFS was seven months [95% CI: 5.6, 10.98]. Univariate analysis demonstrated the presence of Ascites (HR: 9.2[1.8,47]) and the aim of SIRT (segmentectomy, lobectomy, palliative) (HR: 0.066 [0.0057, 0.78]), Aspartate aminotransferase (AST) level (HR:0.1 [0.012-0.86]), and MAA-Dose-V 205 (%)-TL (HR:8.5[1,72]) as predictors for OS. 90 Y-derived parameters were associated with PFS but not with OS. MAA-Dose-V 205 (%)-WNL, MAA-BED-V 400 (%)-WNL with (HR:13 [1.5-120]) and 90 Y-Dose-mean-TL, 90 Y-D 50 -TL-Gy, 90 Y-Dose-V 205 (%)-TL, 90 Y-Dose- D 50 -TL-Gy, and 90 Y-BED-V 400 (%)-TL (HR:15 [1.8-120]) were highly associated with PFS among dosimetry parameters. The highest C-index observed in multivariate analysis using ML was 0.94 ± 0.13 obtained from Variable Hunting-variable-importance (VH.VIMP) FS and Cox Proportional Hazard model predicting OS, using clinical features. However, the combination of VH. VIMP FS method with a Generalized Linear Model Network model predicting OS using Therapy strategy features outperformed the other models in terms of both C-index and stratification of KM curves (C-Index: 0.93 ± 0.14 and log-rank p-value of 0.023 for KM curve stratification)., Conclusion: This preliminary study confirmed the role played by baseline clinical biomarkers and dosimetry parameters in predicting the treatment outcome, paving the way for the establishment of a dose-effect relationship. In addition, the feasibility of using ML along with these features was demonstrated as a helpful tool in the clinical management of patients, both prior to and following 90 Y-SIRT., (© 2024. The Author(s).)

Introduction: Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve patient outcomes., Patients and Methods: The research cohort consisted of 120 patients spanning from cognitively unimpaired individuals to those with mild cognitive impairment, dementia, and other mental disorders. Patients underwent various imaging assessments, including 3D T1-weighted MRI, amyloid PET scans using either 18F-florbetapir (FBP) or 18F-flutemetamol (FMM), and 18F-FDG PET. Summed images of FMM/FBP and FDG were used as proxy for simultaneous scanning of 2 different tracers. A SwinUNETR model, a convolution-free transformer architecture, was trained for image translation. The model was trained using mean square error loss function and 5-fold cross-validation. Visual evaluation involved assessing image similarity and amyloid status, comparing synthesized images with actual ones. Statistical analysis was conducted to determine the significance of differences., Results: Visual inspection of synthesized images revealed remarkable similarity to reference images across various clinical statuses. The mean centiloid bias for dementia, mild cognitive impairment, and healthy control subjects and for FBP tracers is 15.70 ± 29.78, 0.35 ± 33.68, and 6.52 ± 25.19, respectively, whereas for FMM, it is -6.85 ± 25.02, 4.23 ± 23.78, and 5.71 ± 21.72, respectively. Clinical evaluation by 2 readers further confirmed the model's efficiency, with 97 FBP/FMM and 63 FDG synthesized images (from 120 subjects) found similar to ground truth diagnoses (rank 3), whereas 3 FBP/FMM and 15 FDG synthesized images were considered nonsimilar (rank 1). Promising sensitivity, specificity, and accuracy were achieved in amyloid status assessment based on synthesized images, with an average sensitivity of 95 ± 2.5, specificity of 72.5 ± 12.5, and accuracy of 87.5 ± 2.5. Error distribution analyses provided valuable insights into error levels across brain regions, with most falling between -0.1 and +0.2 SUV ratio. Correlation analyses demonstrated strong associations between actual and synthesized images, particularly for FMM images (FBP: Y = 0.72X + 20.95, R2 = 0.54; FMM: Y = 0.65X + 22.77, R2 = 0.77)., Conclusions: This study demonstrated the potential of a novel convolution-free transformer architecture, SwinUNETR, for synthesizing realistic FDG and FBP/FMM images from summation scans mimicking simultaneous dual-tracer imaging., Competing Interests: Conflicts of interest and sources of funding: The authors have no relevant financial or nonfinancial interests to disclose, and the authors have no competing interests to declare that are relevant to the content of this article. V.G. received research support and speaker fees through her institution from GE Healthcare, Siemens Healthineers, Janssen, and Novo Nordisk. H.Z. received research support through his institution from GE Healthcare., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Introduction: Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([ 18 F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [ 18 F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients., Methods: A total of 166 subjects including cognitively unimpaired individuals (N = 72), subjects with mild cognitive impairment (N = 73) and dementia (N = 21) were included in this study. All underwent T1-weighted MRI, dual-phase amyloid PET scans using either Fluorine-18 Florbetapir ([ 18 F]FBP) or Fluorine-18 Flutemetamol ([ 18 F]FMM), and an [ 18 F]FDG PET scan. Two transformer-based DL models called SwinUNETR were trained separately to synthesize the [ 18 F]FDG from early phase [ 18 F]FBP and [ 18 F]FMM (eFBP/eFMM). A clinical similarity score (1: no similarity to 3: similar) was assessed to compare the imaging information obtained by synthesized [ 18 F]FDG as well as eFBP/eFMM to actual [ 18 F]FDG. Quantitative evaluations include region wise correlation and single-subject voxel-wise analyses in comparison with a reference [ 18 F]FDG PET healthy control database. Dice coefficients were calculated to quantify the whole-brain spatial overlap between hypometabolic ([ 18 F]FDG PET) and hypoperfused (eFBP/eFMM) binary maps at the single-subject level as well as between [ 18 F]FDG PET and synthetic [ 18 F]FDG PET hypometabolic binary maps., Results: The clinical evaluation showed that, in comparison to eFBP/eFMM (average of clinical similarity score (CSS) = 1.53), the synthetic [ 18 F]FDG images are quite similar to the actual [ 18 F]FDG images (average of CSS = 2.7) in terms of preserving clinically relevant uptake patterns. The single-subject voxel-wise analyses showed that at the group level, the Dice scores improved by around 13% and 5% when using the DL approach for eFBP and eFMM, respectively. The correlation analysis results indicated a relatively strong correlation between eFBP/eFMM and [ 18 F]FDG (eFBP: slope = 0.77, R 2  = 0.61, P-value < 0.0001); eFMM: slope = 0.77, R 2  = 0.61, P-value < 0.0001). This correlation improved for synthetic [ 18 F]FDG (synthetic [ 18 F]FDG generated from eFBP (slope = 1.00, R 2  = 0.68, P-value < 0.0001), eFMM (slope = 0.93, R 2  = 0.72, P-value < 0.0001))., Conclusion: We proposed a DL model for generating the [ 18 F]FDG from eFBP/eFMM PET images. This method may be used as an alternative for multiple radiotracer scanning in research and clinical settings allowing to adopt the currently validated [ 18 F]FDG PET normal reference databases for data analysis., (© 2024. The Author(s).)

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management., (© 2024. The Author(s).)

In up to two thirds of prostate-specific membrane antigen (PSMA) PET scans, unspecific bone uptake has been described. The aim of this study was to estimate the diagnostic accuracy of [ 68 Ga]Ga-PSMA-11 PET/CT for bone metastases and the occurrence of equivocal lesions. Methods: We analyzed retrospectively 118 patients who underwent a [ 68 Ga]Ga-PSMA-11 PET/CT for initial staging or recurrence evaluation. Lesions were interpreted according to the PSMA reporting and data system (PSMA-RADS) and the prostate cancer molecular imaging standardized evaluation (PROMISE) criteria. The SUV max and the localization of each lesion were recorded. A combination of prior or follow-up examinations was used as a reference standard to categorize benign and malignant lesions. Correlation between the final diagnosis and imaging or clinicobiochemical parameters was tested. The diagnostic accuracy was calculated for different cutoffs of PSMA-RADS criteria, for PROMISE criteria, and the sequential combination of both. Results: In total, 265 bone abnormalities were identified in 70 of 118 patients. Among these, 148 (55.8%) lesions in 50 (42.4%) patients were classified as PSMA-RADS-3B. There were no PSMA-RADS-3D lesions in our cohort. Equivocal lesions were more frequent on the ribs (30.6%) followed by the pelvis (26.5%), but in the ribs, such an uptake was malignant in 33.3% of cases versus 66.7% in the pelvis. A significant association was found between the final diagnosis and the SUV max , prostate-specific antigen (PSA), PSA doubling time, International Society of Urological Pathology score, and the number of foci. The sensitivity and specificity were 100% and 63.6% for the PSMA-RADS-3B cutoff, respectively; 40.5% and 100% for the PSMA-RADS-4 cutoff, respectively; and 89.3% and 96.6% for both the PROMISE criteria and the sequential PSMA-RADS/PROMISE strategy, respectively. In the sequential method, the number of equivocal lesions was reduced from 147 to 2. We found that 53% of PSMA-RADS-3B lesions were malignant; 95.5% of lesions classified positive by the sequential method were true positives, whereas 32.6% were false negatives. Conclusion: [ 68 Ga]Ga-PSMA-11 PET/CT has high accuracy for the diagnosis of bone metastases. Equivocal lesions constitute nearly half of the lesions seen on PSMA PET. The sequential combination of PSMA-RADS and PROMISE criteria reduces the number of lesions classified as equivocal. PSMA-RADS-3B lesions which are positive according to the PROMISE criteria should be considered highly suggestive of malignancy., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.

Importance: An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression., Objective: To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia., Design, Setting, and Participants: This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study., Exposures: Tau PET, Aβ PET, and MRI., Main Outcomes and Measures: Positive results on tau PET (temporal meta-region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics., Results: In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia., Conclusions and Relevance: In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.