Comparison of 99mTc-Labeled Colloid SPECT/CT and Planar Lymphoscintigraphy in Sentinel Lymph Node Detection in Patients with Melanoma: A Meta-Analysis

We compared the detection rate (DR) for sentinel lymph nodes (SLNS), the number of SLNs and the subjects with additional SLNs of single-photon emission computed tomography (SPECT/CT) and planar lymphoscintigraphy (PL) in patients with melanoma. Furthermore, we evaluated the impact of SPECT/CT on surgical plans. Articles containing head-to-head comparisons between SPECT/CT and PL were searched in Pubmed/MEDLINE and Scopus. The literature search was updated until December 31st, 2019. DR was calculated on a per-patient-based analysis; the studies were pooled by their odds ratios (ORs) with a random effects model to assess the significance of difference (p< 0.05). The number of additional SLNs (calculated as the relative risk) and pooled proportion of patients with additional SLNs were investigated. The pooled ratio of surgical procedures influenced by the SPECT/CT findings was calculated. Seventeen studies with 1438 patients were eligible for the calculation of DR of SPECT/CT and PL. The average DR was 98.28% (95% confidence interval (95% CI): 97.94-99.19%) for the SPECT/CT and 95.53% (95% CI: 92.55-97.77%) for the PL; OR of 2.31 (95% CI: 1.66-4.18,p< 0.001) in favor of the SPECT/CT. There was a relative risk of a higher number of SLNs (1.13) for the SPECT/CT and 17.87% of patients with additional SLNs were detected by SPECT/CT. The average impact of SPECT/CT on surgery resulted in 37.43% of cases. This meta-analysis favored SPECT/CT over PL for the identification of SLNs in patients with melanoma due to a higher DR, reproducibility, number of SLNs depicted, proportion of patients with additional SLNs and the impact on the surgical plan. However, PL remains a good option due to the high values of the DR for SLNs

Comparison of 99mTc-Labeled Colloid SPECT/CT and Planar Lymphoscintigraphy in Sentinel Lymph Node Detection in Patients with Melanoma: A Meta-Analysis

We compared the detection rate (DR) for sentinel lymph nodes (SLNS), the number of SLNs and the subjects with additional SLNs of single-photon emission computed tomography (SPECT/CT) and planar lymphoscintigraphy (PL) in patients with melanoma. Furthermore, we evaluated the impact of SPECT/CT on surgical plans. Articles containing head-to-head comparisons between SPECT/CT and PL were searched in Pubmed/MEDLINE and Scopus. The literature search was updated until December 31st, 2019. DR was calculated on a per-patient-based analysis; the studies were pooled by their odds ratios (ORs) with a random effects model to assess the significance of difference (p< 0.05). The number of additional SLNs (calculated as the relative risk) and pooled proportion of patients with additional SLNs were investigated. The pooled ratio of surgical procedures influenced by the SPECT/CT findings was calculated. Seventeen studies with 1438 patients were eligible for the calculation of DR of SPECT/CT and PL. The average DR was 98.28% (95% confidence interval (95% CI): 97.94-99.19%) for the SPECT/CT and 95.53% (95% CI: 92.55-97.77%) for the PL; OR of 2.31 (95% CI: 1.66-4.18,p< 0.001) in favor of the SPECT/CT. There was a relative risk of a higher number of SLNs (1.13) for the SPECT/CT and 17.87% of patients with additional SLNs were detected by SPECT/CT. The average impact of SPECT/CT on surgery resulted in 37.43% of cases. This meta-analysis favored SPECT/CT over PL for the identification of SLNs in patients with melanoma due to a higher DR, reproducibility, number of SLNs depicted, proportion of patients with additional SLNs and the impact on the surgical plan. However, PL remains a good option due to the high values of the DR for SLNs

A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [18F]flortaucipir PET in a memory clinic cohort

Purpose: [18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer's disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population. Methods: We included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages. Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen's kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches. Results: We found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater's expertise (k&gt;0.68, p&lt;0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p&lt;0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC&gt;0.80) and amyloid-positive from negative subjects (AUC&gt;0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p&lt;0.01), independently from the classification method. Conclusion: Our results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.</p

The impact of tau deposition and hypometabolism on cognitive impairment and longitudinal cognitive decline

Introduction: Tau and neurodegeneration strongly correlate with cognitive impairment, as compared to amyloid. However, their contribution in explaining cognition and predicting cognitive decline in memory clinics remains unclarified. Methods: We included 94 participants with Mini-Mental State Examination (MMSE), tau positron emission tomography (PET), amyloid PET, fluorodeoxyglucose (FDG) PET, and MRI scans from Geneva Memory Center. Linear regression and mediation analyses tested the independent and combined association between biomarkers, cognitive performance, and decline. Linear mixed-effects and Cox proportional hazards models assessed biomarkers' prognostic values. Results: Metabolism had the strongest association with cognition (r = 0.712; p &lt; 0.001), followed by tau (r = -0.682; p &lt; 0.001). Neocortical tau showed the strongest association with cognitive decline (r = -0.677; p &lt; 0.001). Metabolism mediated the association between tau and cognition and marginally mediated the one with decline. Tau positivity represented the strongest risk factor for decline (hazard ratio = 32). Discussion: Tau and neurodegeneration synergistically contribute to global cognitive impairment while tau drives decline. The tau PET superior prognostic value supports its implementation in memory clinics. Highlights: Hypometabolism has the strongest association with concurrent cognitive impairment. Neocortical tau pathology is the main determinant of cognitive decline over time. FDG-PET has a superior value compared to MRI as a measure of neurodegeneration. The prognostic value of tau-PET exceeded all other neuroimaging modalities.</p

Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis

Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artifi- cial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmen- tation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis. Methods: From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clin- ical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD. Results: A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity ofAmyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher- order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%. Conclusions: The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD

The impact of tau deposition and hypometabolism on cognitive impairment and longitudinal cognitive decline

Introduction: Tau and neurodegeneration strongly correlate with cognitive impairment, as compared to amyloid. However, their contribution in explaining cognition and predicting cognitive decline in memory clinics remains unclarified. Methods: We included 94 participants with Mini-Mental State Examination (MMSE), tau positron emission tomography (PET), amyloidPET, fluorodeoxyglucose (FDG)PET, and MRI scans from Geneva Memory Center. Linear regression and mediation analyses tested the independent and combined association between biomarkers, cognitive performance, and decline. Linear mixed-effects and Cox proportional hazards models assessed biomarkers’ prognostic values. Results: Metabolism had the strongest association with cognition (r = 0.712; p < 0.001), followed by tau (r = -0.682; p < 0.001). Neocortical tau showed the strongest association with cognitive decline (r = -0.677; p < 0.001). Metabolism mediated the association between tau and cognition and marginally mediated the one with decline. Tau positivity represented the strongest risk factor for decline (hazard ratio = 32). Discussion: Tau and neurodegeneration synergistically contribute to global cognitive impairment while tau drives decline. The tauPET superior prognostic value supports its implementation in memory clinics.ISSN:1552-5279ISSN:1552-526

Clinical Impact of 18F-FDG PET/CT in the Diagnostic Workup of Pancreatic Ductal Adenocarcinoma: A Systematic Review

In this review, the performance of fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) in the diagnostic workup of pancreatic ductal adenocarcinoma (PDAC) is evaluated. A comprehensive literature search up to September 2020 was performed, selecting studies with the presence of: sample size >= 10 patients and index test (i.e., "FDG" or "18F-FDG" AND "pancreatic adenocarcinoma" or "pancreas cancer" AND "PET" or "positron emission tomography"). The methodological quality was evaluated using the revised quality assessment of diagnostic accuracy studies (QUADAS-2) tool and presented according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Basic data (authors, year of publication, country and study design), patients' characteristics (number of enrolled subjects and age), disease phase, type of treatment and grading were retrieved. Forty-six articles met the adopted research criteria. The articles were divided according to the considered clinical context. Namely, besides conventional anatomical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), molecular imaging with FDG PET/CT is an important tool in PDAC, for all disease stages. Further prospective studies will be necessary to confirm the cost-effectiveness of such imaging techniques by testing its real potential improvement in the clinical management of PDAC

18F-Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification

While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD