Search Results (1,068)

Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [¹⁸F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [¹⁸F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes. Full article

Lung cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. Despite advancements, the overall survival rate for lung cancer remains between 10% and 20% in most countries. However, recent progress in diagnostic tools and therapeutic strategies has led to meaningful improvements in survival outcomes, highlighting the growing importance of personalized management based on accurate disease assessment. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become essential in the management of lung cancer, serving as a key imaging modality for initial diagnosis, staging, treatment response assessment, and follow-up evaluation. Recent developments in radiomics and artificial intelligence (AI), including machine learning and deep learning, have revolutionized the analysis of complex imaging data, enhancing the diagnostic and predictive capabilities of FDG PET/CT in lung cancer. However, the limitations of FDG, including its low specificity for malignancy, have driven the development of novel oncologic radiotracers. One such target is fibroblast activation protein (FAP), a type II transmembrane glycoprotein that is overexpressed in activated cancer-associated fibroblasts within the tumor microenvironment of various epithelial cancers. As a result, FAP-targeted radiopharmaceuticals represent a novel theranostic approach, offering the potential to integrate PET imaging with radioligand therapy (RLT). In this review, we provide a comprehensive overview of FDG PET/CT in lung cancer, along with recent advances in AI. Additionally, we discuss FAP-targeted radiopharmaceuticals for PET imaging and their potential application in RLT for the personalized management of lung cancer. Full article

Hydrogen-rich water (HRW) has shown neuroprotective effects in acute brain injury, but its role in chronic radiation-induced brain injury (RIBI) remains unclear. This study investigated the long-term efficacy of HRW in mitigating cognitive impairment and neuronal damage caused by chronic RIBI. Fifty male Sprague Dawley rats were randomly divided into five groups: control, irradiation (IR), IR with memantine, IR with HRW, and IR with combined treatment. All but the control group received 20 Gy whole-brain X-ray irradiation, followed by daily interventions for 60 days. Behavioral assessments, histopathological analyses, oxidative stress measurements, ¹⁸F-FDG PET/CT imaging, transcriptomic sequencing, RT-qPCR, Western blot, and serum ELISA were performed. HRW significantly improved anxiety-like behavior, memory, and learning performance compared to the IR group. Histological results revealed that HRW reduced neuronal swelling, degeneration, and loss and enhanced dendritic spine density and neurogenesis. PET/CT imaging showed increased hippocampal glucose uptake in the IR group, which was alleviated by HRW treatment. Transcriptomic and molecular analyses indicated that HRW modulated key genes and proteins, including CD44, CD74, SPP1, and Wnt1, potentially through the MIF, Wnt, and SPP1 signaling pathways. Serum CD44 levels were also lower in treated rats, suggesting its potential as a biomarker for chronic RIBI. These findings demonstrate that HRW can alleviate chronic RIBI by preserving neuronal structure, reducing inflammation, and enhancing neuroplasticity, supporting its potential as a therapeutic strategy for radiation-induced cognitive impairment. Full article

Background and Purpose: Stratification based on specific image biomarkers applicable in clinical settings could help optimize treatment outcomes for recurrent non-small cell lung cancer patients. For this purpose, we aimed to determine the clinical impact of positive delta changes (any difference above zero > 0) between baseline [¹⁸F]FDG PET/CT metrics before the first treatment course and reirradiation. Material/Methods: Forty-seven patients who underwent thoracic reirradiation with curative intent at our institute between 2013 and 2021 met the inclusion criteria. All patients had histologically verified NSCLC, ECOG (Eastern Cooperative Oncology Group) ≤ 2, and underwent [¹⁸F]FDG PET/CT for initial staging and re-staging before primary radiotherapy and reirradiation, respectively. The time interval between radiation treatments was at least nine months. Quantitative metabolic volume and intensity parameters were measured before first irradiation and before reirradiation, and the difference above zero (>0; delta change) between them was statistically correlated to locoregional control (LRC), progression-free survival (PFS), and overall survival (OS). Results: Patients were followed for a median time of 33 months after reirradiation. The median OS was 21.8 months (95%-CI: 16.3–27.3), the median PFS was 12 months (95%-CI: 6.7–17.3), and the median LRC was 13 months (95%-CI: 9.0–17.0). Multivariate analysis revealed that the delta changes in SULpeak, SUVmax, and SULmax of the lymph nodes significantly impacted OS (SULpeak p = 0.017; SUVmax p = 0.006; SULmax p = 0.006), PFS (SULpeak p = 0.010; SUVmax p = 0.009; SULmax p = 0.009), and LRC (SULpeak p < 0.001; SUVmax p = 0.003; SULmax p = 0.003). Conclusions: Delta changes in SULpeak, SUVmax, and SULmax of the metastatic lymph nodes significantly impacted all clinical endpoints (OS, PFS and LRC) in recurrent NSCLC patients treated with reirradiation. Hence, these imaging biomarkers could be helpful with regard to patient selection in this challenging clinical situation. Full article

Background: Hypometabolism of the thalamus has been reported in epilepsy patients. This study aimed to investigate the prognostic value of thalamic metabolic activity in children with epilepsy. Methods: A total of 200 children with epilepsy and 237 children without epilepsy (sex- and age-matched control group) underwent 18F-FDG PET/CT in this study. Localization of the interictal hypometabolic epileptic focus was performed visually. Bilateral thalamic metabolic activity was evaluated qualitatively (thalamic FDG uptake in relation to the cerebral cortex) and semi-quantitatively (SUV max, normalized SUV (ratio to ipsilateral cerebellum), and absolute asymmetric index (AAI). Results: A total of 133 patients (66.5%) with epilepsy showed cerebral cortical hypometabolism in the interictal 18F-FDG PET study; there were 76 patients on the right side, 55 patients on the left side, and two patients on both sides. Of these 133 patients, 45 also had visually observed asymmetric hypometabolism in the thalamus. Semi-quantitatively, asymmetry was more prominent in epileptic patients. AAI was a more sensitive variable than other variables. Average AAIs were 3.89% and 7.36% in the control and epilepsy patients, respectively. Metabolic activity in the thalami was significantly reduced in epileptic patients compared to the control group. Associated hypometabolism of the ipsilateral thalamus was observed in 66.5% of epileptic patients with a focal cortical defect semi-quantitatively. Overall, 61 out of 200 patients showed thalamus hypometabolism. Some 51 out of 61 patients (83.6%) with thalamus hypometabolism showed refractory disease; however, the refractory disease was noted in 90 out of 139 (64.7%) patients without thalamus hypometabolism. Brain surgery was performed in 86 epileptic patients (43%). Some 35 out of 86 patients had thalamus hypometabolism. Recurrence of epilepsy was observed more in patients with thalamus hypometabolism (48% vs. 25%), with p ≤ 0.01. Conclusion: This study suggests that patients with thalamus metabolic abnormalities may be more medically resistant to therapy and less responsive to surgical treatments. Therefore, the thalamus metabolic abnormality could be used as a prognostic sign in pediatric epilepsy. Recent studies have also suggested that incorporating thalamic metabolic data into clinical workflows may improve the stratification of treatment-resistant epilepsy in children. Full article

Background/Objectives: The SENTIREC-endo study identified a safe sentinel lymph node mapping algorithm combined with PET-positive node dissection, matching radical pelvic and paraaortic lymphadenectomy in high-risk endometrial cancer. The present study evaluated the diagnostic accuracy of FDG-PET/CT for lymph node metastases in the same population based on location, size, and Standardised Uptake Value (SUV), in addition to assessing interrater agreement across three Danish centres. Methods: This prospective multicentre study included women with high-risk endometrial cancer from the Danish SENTIREC study database (2017–2023). All patients underwent preoperative FDG-PET/CT. Diagnostic accuracy was evaluated against a pathology-confirmed reference standard. Interrater agreement was evaluated between trained specialists in Nuclear Medicine. Results: Among 227 patients, 52 patients (23%) had lymph node metastases. FDG-PET/CT identified lymph node metastases with 56% sensitivity (95% CI: 42–68) and 91% specificity (95% CI: 86–94). Positive and negative predictive values were 64% and 87%, respectively. Specificity for paraaortic nodes was high (97%), though sensitivity remained limited (56%). Lymph node size and SUVmax had moderate diagnostic value (AUC-ROC ~0.7). Interrater proportion of agreement was 95% and Cohen’s Kappa κ = 0.84 (95% CI: 0.73–0.94), the latter of which was ‘almost perfect’. Conclusions: FDG-PET/CT had limited sensitivity in lymph node staging in high-risk EC, and the diagnostic accuracy of FDG-PET/CT remains complementary to the sentinel node procedure. Due to its high specificity and strong interrater reliability, FDG-PET/CT is recommended for clinical implementation in combination with the sensitive sentinel node biopsy for the targeted dissection of PET-positive lymph nodes, particularly in paraaortic regions. Full article

Background: Assessment of the peritoneal cancer burden is crucial for determining the optimal treatment in advanced ovarian cancer (AOC). Effective non-invasive methods to predict tumour load remain limited. This study aimed to assess the applicability of 2-[¹⁸F]FDG PET/CT volumetric parameters, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) for predicting the surgical peritoneal cancer index (PCI) in AOC before primary treatment. Methods: Patients with high-grade serous or undifferentiated AOC who underwent surgical PCI evaluation and 2-[¹⁸F]FDG PET/CT between 01/2013 and 12/2018 were included. MTV and TLG were calculated using thresholds of 40% and 50% (MTV40, MTV50, TLG40, and TLG50). Correlations between the peritoneal carcinomatosis MTV (car_MTV) and TLG (car_TLG) were analysed. The capacity of volumetric parameters to estimate PCIs above or below 14 and 20 was assessed for the whole abdominal cavity and in per-quadrant analysis, specifically for upper-abdomen areas 1, 2, and 3 (MTV40_1, 2, 3 and TLG40_1, 2, 3). Results: MTV40, MTV50, TLG40, and TLG50 significantly correlated with the PCI in the final study population (n = 45). MTV40 showed a Pearson coefficient of 0.41 (p = 0.003). MTV3_40 (AUC 0.79) and TLG3_40 (AUC 0.81) presented the highest AUCs for predicting a PCI above or below 14. The volumetric parameters allowed the prediction of a PCI greater or less than 20, with an AUC of 0.77 for MTV40_1 and 0.78 for TLG40_1. Conclusions: 2-[¹⁸F]FDG PET/CT MTV and TLG correlate significantly with the surgical PCI when assessing peritoneal carcinomatosis or quadrant-specific disease. This approach offers a reliable non-invasive method for evaluating tumour burden in AOC. Full article

Background: The accurate prediction of pathological complete response (pCR) following total neoadjuvant therapy (TNT) is crucial for optimising treatment protocols in locally advanced rectal cancer (LARC). Although conventional imaging techniques such as MRI show limitations in assessing treatment response, metabolic imaging utilising 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) provides distinctive information by quantifying tumour glycolytic activity. This study investigates the predictive value of sequential 18F-FDG PET-CT parameters, focusing on Total Lesion Glycolysis (TLG), in predicting pCR after TNT. Methods: We conducted a retrospective analysis of 33 LARC patients (T3–4/N0–1) treated with TNT (neoadjuvant-chemoradiation followed by consolidation FOLFOX chemotherapy). Sequential PET-CT scans were performed at baseline, interim (after 4 cycles of FOLFOX), and post-TNT. Metabolic parameters, including maximum standardised uptake value (SUVmax) and TLG, were measured. Receiver operating characteristic (ROC) analysis assessed the predictive performance of these parameters for pCR. Results: The pCR rate was 21.2% (7/33). Post-TNT TLG ≤ 10 demonstrated excellent predictive accuracy for pCR (AUC 0.887, 92.3% sensitivity, 85.7% specificity, and 96.0% PPV), outperforming SUVmax (AUC 0.843). Interim TLG ≤ 10 also showed a strong predictive value (AUC 0.824, 100% sensitivity, and 71.4% specificity). Conclusions: TLG may serve as a reliable metabolic biomarker for predicting pathologic complete response (pCR) after total neoadjuvant therapy (TNT) in locally advanced rectal cancer (LARC). Its inclusion in clinical decision-making could improve patient selection for organ preservation strategies, thereby reducing the need for unnecessary surgeries in the future. However, given that the study is based on a small retrospective design, the findings should be interpreted with caution and used alongside other decision-making tools until more comprehensive data are collected from larger studies. Full article

This study assesses the clinical deployment of SubtlePET™, a commercial AI-based denoising algorithm, across three radiotracers—¹⁸F-FDG, ⁶⁸Ga-PSMA-11, and ¹⁸F-FDOPA—with the goal of improving image quality while reducing injected activity, technologist radiation exposure, and scan time. A retrospective analysis on a digital PET/CT system showed that SubtlePET™ enabled dose reductions exceeding 33% and time savings of over 25%. AI-enhanced images were rated interpretable in 100% of cases versus 65% for standard low-dose reconstructions. Notably, 85% of AI-enhanced scans received the maximum Likert quality score (5/5), indicating excellent diagnostic confidence and noise suppression, compared to only 50% with conventional reconstruction. The quantitative image quality improved significantly across all tracers, with SNR and CNR gains of 50–70%. Radiotracer dose reductions were particularly substantial in low-BMI patients (up to 41% for FDG), and the technologist exposure decreased for high-exposure roles. The daily patient throughput increased by an average of 4.84 cases. These findings support the robust integration of SubtlePET™ into routine clinical PET practice, offering improved efficiency, safety, and image quality without compromising lesion detectability. Full article

Objective: Traditional imaging modalities for the planning of Gamma Knife radiosurgery (GKRS) are non-specific and do not accurately delineate intracranial neoplasms. This study aimed to evaluate the utility of positron emission tomography (PET) for the planning of GKRS for intracranial neoplasms (ICNs) and the post-GKRS applications of PET for patient care. Methods: PubMed, Scopus, and ScienceDirect were searched in order to assemble relevant studies regarding the uses of PET in conjunction with GKRS for ICN treatment. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to identify relevant studies on the use of PET in conjunction with GKRS. Particular emphasis was placed on review articles and medical research investigating tumor delineation and post-operative care. Relevant studies were selected and assessed based on quality measures, including study design, sample size, and significance. Inclusion and exclusion criteria were used to examine the yield of the initial search (n = 105). After a secondary review, the included results were identified (n = 50). Results: This study revealed that PET imaging is highly accurate for the planning of GKRS. In fact, many cases indicate that it is more specific than traditional imaging modalities. PET is also capable of complementing traditional imaging techniques through combination imaging. This showed significant efficacy for the planning of GKRS for ICNs. Conclusions: While PET shows a multitude of applications for the treatment of ICNs with GKRS, further research is necessary to assemble a complete set of clinical guidelines for treatment specifications. Importantly, future studies need a greater standardization of methods and expanded trials with a multitude of radiotracers. Full article

Background/Objectives: Diagnosis of Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) currently lacks objective biomarkers. Therefore, this retrospective study aimed to investigate differences in glucose metabolism in the axial musculoskeletal system in PSPS-T2 patients by means of [¹⁸F]FDG PET-CT imaging. Methods: Nine PSPS-T2 patients (five females, four males; mean age of 53 ± 4.82 years) and nine age- and gender-matched healthy controls (five females, four males; mean age of 53 ± 3.91 years) were included. For each participant, 24 regions of interest (ROIs) were manually drawn, including areas of the vertebral endplates, the intervertebral discs, and the psoas muscles. For each ROI, the mean standardized uptake values (SUVs) were assessed. Group differences were evaluated using repeated measures ANOVA with Bonferroni-adjusted post-hoc pairwise comparisons. Additionally, Pearson correlation analyses examined associations between SUV_mean values and the Numerical Rating Scale (NRS) pain scores. Results: Results demonstrated significantly higher SUV_mean values in healthy controls compared to PSPS-T2 patients, particularly at the superior endplates of L4 and S1, the intervertebral discs at L4-L5 and L5-S1, and the posterior endplates of L4 and L5. Although PSPS-T2 patients exhibited higher SUV_mean values than controls in the psoas muscle, these differences were not statistically significant. Additionally, no significant correlations were found between SUV_mean values and NRS pain scores, suggesting that metabolic activity alone does not directly reflect pain severity. Conclusions: Despite the limited sample size of this pilot study, the metabolic fingerprint of the axial musculoskeletal system was shown to be distinctly different in PSPS-T2 patients compared to healthy controls. This could lead to an improved understanding of PSPS-T2 pathophysiology and might open new doors for better diagnosis and treatment strategies. Full article

Background: The recent introduction of whole-body positron emission tomography/ computed tomography (PET/CT) scanners and multi-bed, multi-time point acquisition technique enable calculating fluorodeoxyglucose (FDG) kinetics in the whole body. However, validating parametric, Patlak-derived data is difficult on phantoms. Methods: This prospective study investigated the effect of quantification methods mean, max, and peak on the metabolic rate (MR-FDG) and distribution volume (DV-FDG) quantification, as well as the diagnostic accuracy of parametric Patlak FDG-PET scans in diagnosing lung lesions and lymph node metastases, using histopathology and follow-up as reference standards. Dynamic whole-body FDG PET was acquired for 80 minutes in 34 patients with indeterminate lung lesions and kinetic parameters extracted from lung lesions and representative mediastinal and hilar lymph nodes. Results: All quantification methods—mean, max, and peak—demonstrated high diagnostic accuracy (AUC: MR-FDG: 0.987–0.991 and 0.893–0.905; DV-FDG: 0.948–0.975 and 0.812–0.825) for differentiating benign from malignant lymph nodes and lung lesions. Differences in the magnitude of MR-FDG (−4.76–14.09) and DV-FDG (−10.64–46.10%) were substantial across methods. Variability was more pronounced in lymph nodes (MR-FDG: 1.37–3.48) than in lung lesions (MR-FDG: 3.31–5.04). The variability was lowest between mean and max quantification, with percentage differences of 40.87 ± 5.69% for MR-FDG and 39.26 ± 7.68% for DV-FDG. Conclusions: The choice of method to measure MR-FDG and DV-FDG greatly influences the results, especially in smaller lesions with large and systematic differences. For lung lesions, a conversion factor between mean and max methods of 40% provides acceptable agreement, facilitating retrospective comparisons of measurements, e.g., in meta-analyses. Full article

Background/Objectives: This study aimed to develop machine learning (ML) models to predict recurrence in thymoma patients using conventional and radiomic signatures extracted from preoperative [¹⁸F]FDG PET/CT. Methods: A total of 50 patients (25 males, 25 females; mean age 63.3 ± 14.2 years) who underwent thymectomy and preoperative [¹⁸F]FDG PET/CT between 2012 and 2022 were retrospectively analyzed. Radiomic analysis was performed using free-from-recurrence (FFR) status as a reference. Clinico-metabolic PET parameters were collected, and thymoma lesions were manually segmented on [¹⁸F]FDG PET/CT. A total of 856 radiomic features (RFts) were extracted from PET and CT datasets following IBSI guidelines, and robust RFts were selected. The dataset was split into training (70%) and validation (30%) sets. Two ML models (PET- and CT-based, respectively), each with three classifiers—Random Forest (RF), Support-Vector-Machine, and Tree—were trained and internally validated using RFts and clinico-metabolic signatures. Results: A total of 50 ROIs were selected and segmented. FFR was observed in 84% of our cohort. Forty-three robust RFts were selected from the CT dataset and 16 from the PET dataset, predominantly wavelet-based RFts. Additionally, three metabolic PET parameters were selected and included in the PET Model. Both the CT and PET models successfully discriminated against FFR after surgery, with the CT Model slightly outperforming the PET Model across different classifiers. The performance metrics of the RF classifier for the CT and PET models were AUC = 0.970/0.949, CA = 0.880/0.840, Precision = 0.884/0.842, Recall = 0.880/0.846, Specificity = 0.887/0.839, Sensitivity = 0.920/0.844, TP = 81.8%/83.3%, and TN = 92.9%/84.6%, respectively. Conclusions: ML-models trained on PET/CT radiomic features show promising results for predicting recurrence in patients with thymomas, which could be potentially applied in clinical practice for a better personalized treatment strategy. Full article

We present a case with unusual findings on nuclear imaging after mild SARS-CoV-2 infection. During evaluation for an incidentaloma, 18F-Fluorodeoxyglucose Positron Emission Tomography–Computed Tomography imaging showed activity in the thyroid gland, in the lower thoracic spinal column, in portal lymph nodes, and in the terminal ileum and surrounding lymph nodes, all suspicious for metastatic cancer. The patient underwent extensive invasive and non-invasive diagnostic procedures, including biopsies of all the suspicious foci, only showing a small low-grade thyroid cancer that would often be followed and not immediately operated on. Three months later, the findings had either disappeared or were considered reactive. The patient later recalled having had mild COVID-19 seven days prior to the PET/CT. Full article