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Objective: Positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT) or magnetic resonance imaging (18F-FDG PET/MRI) has emerged as a promising tool for managing various types of cancer. This review study was conducted to investigate the role of 18F- FDG PET/CT and FDG PET/MRI in the management of gynecological malignancies. Search strategy: We searched for relevant articles in the three databases PubMed/MEDLINE, Scopus, and Web of Science. Selection criteria: All studies reporting data on the FDG PET/CT and FDG PET MRI in the management of gynecological cancer, performed anywhere in the world and published exclusively in the English language, were included in the present study. Data collection and analysis: We used the EndNote software (EndNote X8.1, Thomson Reuters) to list the studies and screen them on the basis of the inclusion criteria. Data, including first author, publication year, sample size, clinical application, imaging type, and main result, were extracted and tabulated in Excel. The sensitivity, specificity, and diagnostic accuracy of the modalities were extracted and summarized. Main results: After screening 988 records, 166 studies published between 2004 and 2022 were included, covering various methodologies. Studies were divided into the following five categories: the role of FDG PET/CT and FDG-PET/MRI in the management of: (a) endometrial cancer (n = 30); (b) ovarian cancer (n = 60); (c) cervical cancer (n = 50); (d) vulvar and vagina cancers (n = 12); and (e) gynecological cancers (n = 14). Conclusions: FDG PET/CT and FDG PET/MRI have demonstrated potential as non-invasive imaging tools for enhancing the management of gynecological malignancies. Nevertheless, certain associated challenges warrant attention.

We provide updated guidance and standards for the indication, acquisition, and interpretation of [ 18 F]FDG PET/CT for plasma cell disorders. Procedures and characteristics are reported and different scenarios for the clinical use of [ 18 F]FDG PET/CT are discussed. This document provides clinicians and technicians with the best available evidence to support the implementation of [ 18 F]FDG PET/CT imaging in routine practice and future research.

Purpose Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [ 18 F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)–based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. Materials and methods Forty-four, previously untreated MM patients underwent whole-body [ 18 F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients’ progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. Results Median follow-up [95% CI] of the patient cohort was 110 months [105–123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [ 18 F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [ 18 F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. Conclusions The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.

•Brain SUVs were precisely fitted to pre-scan BGL by a 2nd-degree polynomial model.•SUV variability due to BGL changes can be greatly reduced by using our method.•BGL-corrected SUVs calculated in a different dataset highly correlated with CMRglc.•Regional [18F]FDG differences were found at extreme BGL.•Our method can be a standard protocol in preclinical brain [18F]FDG PET research. Introduction SUV measurements from static brain [18F]FDG PET acquisitions are a commonly used tool in preclinical research, providing a simple alternative for kinetic modelling, which requires complex and time-consuming dynamic acquisitions. However, SUV can be severely affected by the animal handling and preconditioning protocols, primarily by those that may induce changes in blood glucose levels (BGL). Here, we aimed at developing and investigating the feasibility of SUV-based approaches for a wide range of BGL far beyond normal values, and consequently, to develop and validate a new model to generate standardized and reproducible SUV measurements for any BGL. Material and methods We performed dynamic and static brain [18F]FDG PET acquisitions in 52 male Sprague-Dawley rats sorted into control (n = 10), non-fasting (n = 14), insulin-induced hypoglycemia (n = 12) and glucagon-induced hyperglycemia (n = 16) groups. Brain [18F]FDG PET images were cropped, aligned and co-registered to a standard template to calculate whole-brain and regional SUV. Cerebral Metabolic Rate of Glucose (CMRglc) was also estimated from 2-Tissue Compartment Model (2TCM) and Patlak plot for validation purposes. Results Our results showed that BGL=100±6 mg/dL can be considered a reproducible reference value for normoglycemia. Furthermore, we successfully established a 2nd-degree polynomial model (C1=0.66E-4, C2=-0.0408 and C3=7.298) relying exclusively on BGL measures at pre-[18F]FDG injection time, that characterizes more precisely the relationship between SUV and BGL for a wide range of BGL values (from 10 to 338 mg/dL). We confirmed the ability of this model to generate corrected SUV estimations that are highly correlated to CMRglc estimations (R2= 0.54 2TCM CMRgluc and R2= 0.49 Patlak CMRgluc). Besides, slight regional differences in SUV were found in animals from extreme BGL groups, showing that [18F]FDG uptake is mostly directed toward central regions of the brain when BGLs are significantly decreased. Conclusion Our study successfully established a non-linear model that relies exclusively on pre-scan BGL measurements to characterize the relationship between [18F]FDG SUV and BGL. The extensive validation confirmed its ability to generate SUV-based surrogates of CMRglu along a wide range of BGL and it holds the potential to be adopted as a standard protocol by the preclinical neuroimaging community using brain [18F]FDG PET imaging.

This meta-analysis aims to compare the diagnostic efficacy of [ F]FDG PET/CT and [ F]FDG PET/MRI in patients with non-small cell lung cancer (NSCLC). An extensive literature search was conducted throughout the PubMed, Embase, and Web of Science databases for works accessible through September 2024. We included studies assessed the diagnostic efficacy of [ F]FDG PET/CT and [ F]FDG PET/MRI in NSCLC. The meta-analysis includes six studies with a total of 437 patients. The sensitivity and specificity of [ F]FDG PET/CT and [ F]FDG PET/MRI for detecting lymph node metastasis were similar, at 0.82 (0.68-0.94) vs. 0.86 (0.70-0.97) and 0.88 (0.76-0.96) vs. 0.90 (0.85-0.94), respectively, with no significant differences (  = 0.70 for sensitivity,  = 0.75 for specificity). For distant metastasis, the sensitivity of [ F]FDG PET/CT and [ F]FDG PET/MRI was 0.86 (0.60-1.00) and 0.93 (0.63-1.00), and specificity was 0.89 (0.65-1.00) vs. 0.90 (0.64-1.00), respectively, also showing no significant differences (  = 0.66 for sensitivity,  = 0.97 for specificity). Our meta-analysis shows that [ F]FDG PET/MRI has similar sensitivity and specificity to [ F]FDG PET/CT in identifying lymph node and distant metastases in patients with NSCLC. Additional larger sample prospective studies are needed to confirm these findings. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023479817, CRD42023479817.

This meta-analysis evaluates and compares the diagnostic accuracy of [ F]FDG PET/CT and [ F]FDG PET/MRI in detecting breast cancer recurrence. A search was conducted across PubMed, Web of Science, and Embase databases up to June 10, 2025, to identify studies evaluating the diagnostic performance of [ F]FDG PET/CT and/or [ F]FDG PET/MRI in breast cancer recurrence. Sensitivity and specificity were calculated using the DerSimonian and Laird method with Freeman-Tukey double arcsine transformation. The Quality Assessment for Studies of Diagnostic Accuracy-2 (QUADAS-2) guidelines were employed to perform the quality evaluation. Seventeen studies involving 1,450 patients were included. At the lesion level, the sensitivity of [ F]FDG PET/CT was 0.97 (95% CI: 0.91-1.00), with a specificity of 0.79 (95% CI: 0.58-0.94). [ F]FDG PET/MRI showed a sensitivity of 0.95 (95% CI: 0.91-0.99) and specificity of 0.87 (95% CI:0.75-0.95). Both modalities demonstrated similar sensitivity (  = 0.71) and specificity (  = 0.66). At the patient level, the sensitivity of [ F]FDG PET/CT was 0.93 (95% CI: 0.88-0.96), with a specificity of 0.87 (95% CI: 0.80-0.93). [ F]FDG PET/MRI showed a sensitivity of 0.99 (95% CI: 0.94-1.00) and specificity of 0.98 (95% CI, 0.90-1.00). Both modalities demonstrated similar sensitivity (  = 0.07) and specificity (  = 0.06). [ F]FDG PET/CT and [ F]FDG PET/MRI exhibit comparable sensitivity and specificity in detecting breast cancer recurrence at both the lesion and patient levels. However, high heterogeneity warrants further head-to-head studies to strengthen the evidence and provide more comprehensive insights.

Background and purpose This study was undertaken to compare the performance of plasma p‐tau181 with that of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in the identification of early biological Alzheimer disease (AD). Methods We included 533 cognitively impaired participants from the Alzheimer's Disease Neuroimaging Initiative. Participants underwent PET scans, biofluid collection, and cognitive tests. Receiver operating characteristic analyses were used to determine the diagnostic accuracy of plasma p‐tau181 and [18F]FDG‐PET using clinical diagnosis and core AD biomarkers ([18F]florbetapir‐PET and cerebrospinal fluid [CSF] p‐tau181) as reference standards. Differences in the diagnostic accuracy between plasma p‐tau181 and [18F]FDG‐PET were determined by bootstrap‐based tests. Correlations of [18F]FDG‐PET and plasma p‐tau181 with CSF p‐tau181, amyloid β (Aβ) PET, and cognitive performance were evaluated to compare associations between measurements. Results We observed that both plasma p‐tau181 and [18F]FDG‐PET identified individuals with positive AD biomarkers in CSF or on Aβ‐PET. In the MCI group, plasma p‐tau181 outperformed [18F]FDG‐PET in identifying AD measured by CSF (p = 0.0007) and by Aβ‐PET (p = 0.001). We also observed that both plasma p‐tau181 and [18F]FDG‐PET metabolism were associated with core AD biomarkers. However, [18F]FDG‐PET uptake was more closely associated with cognitive outcomes (Montreal Cognitive Assessment, Mini‐Mental State Examination, Clinical Dementia Rating Sum of Boxes, and logical memory delayed recall, p < 0.001) than plasma p‐tau181. Conclusions Overall, although both plasma p‐tau181 and [18F]FDG‐PET were associated with core AD biomarkers, plasma p‐tau181 outperformed [18F]FDG‐PET in identifying individuals with early AD pathophysiology. Taken together, our study suggests that plasma p‐tau181 may aid in detecting individuals with underlying early AD.

Background Reduced 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) brain metabolism was recognized as a biomarker of neurodegeneration in the recently proposed ATN framework for Alzheimer’s disease (AD) biological definition. However, accumulating evidence suggested it is an independent biomarker, which is denoted as “F” in the very study. Methods A total of 551 A+T+ individuals from the Alzheimer’s Disease Neuroimaging Initiative database were recruited and then further divided into four groups based on the biomarker positivity as 132 A+T+N−F−, 102 A+T+N−F+, 113 A+T+N+F−, and 204 A+T+N+F+. Frequency distributions of the groups were compared, as well as the clinical progression [measured by the longitudinal changes in cognition and brain structure, and mild cognitive impairment (MCI) to AD dementia conversion] between every pair of F+ and F− groups. Results The prevalence of A+T+N+F+ profile was 66.24% in clinically diagnosed AD dementia patients; similarly, the majority of individuals with reduced FDG-PET were AD dementia subjects. Among the 551 individuals that included, 537 had at least one follow-up (varied from 1 to 8 years). Individuals in F+ groups performed worse and dropped faster in Mini-Mental State Examination scale and had faster shrinking middle temporal lobe than those in F− groups (all p  < 0.05). Moreover, in MCI patients, reduced FDG-PET exerted 2.47 to 4.08-fold risk of AD dementia progression compared with those without significantly impaired FDG-PET (both p  < 0.001). Conclusions Based on the analyses, separating FDG-PET from “N” biomarker to build the ATN(F) system is necessary and well-founded. The analysis from this study could be a complement to the original ATN framework for AD’s biological definition.