Search Results (2,320)

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/Objectives: ¹⁸F-PSMA-1007 is one of the more widely used radioligands in prostate cancer imaging with PET/CT. Its major advantage lies in the low urinary tracer activity due to primarily hepatobiliary clearance, but unexpectedly high tracer accumulation in the bladder can occur, potentially hindering assessment of lesions near the prostate bed. This study assesses the impact of furosemide on ¹⁸F-PSMA-1007 tracer accumulation in the bladder. Methods: In this single-center, retrospective, intra-individual comparative analysis, 18 patients undergoing two consecutive ¹⁸F-PSMA-1007 PET/CT scans for biochemical relapse (BCR) or persistence (BCP)—one with and one without prior furosemide administration—were included. Images were acquired 60 min post-injection of 250 MBq of tracer activity. Standardized Uptake Values (SUVmax, SUVpeak, SUVmean) were measured in the bladder and in tissues with physiological uptake by three readers. Differences were analyzed using Wilcoxon signed-rank tests. The inter-reader agreement was assessed using intraclass correlation coefficient. Results: Furosemide significantly decreased bladder SUVmax, SUVpeak, and SUVmean (all p < 0.001). Mean bladder SUVmax decreased from 13.20 ± 10.40 to 3.92 ± 3.47, SUVpeak from 10.94 ± 8.02 to 3.47 ± 3.13, and SUVmean from 8.74 ± 6.66 to 2.81 ± 2.56, representing a large effect size (r ≈ 0.55). Physiological tracer uptake in most organs was not significantly influenced by furosemide (all p > 0.05). Conclusions: Despite the predominantly hepatobiliary clearance of ¹⁸F-PSMA-1007, furosemide-induced forced diuresis leads to a significant reduction in tracer activity in the bladder, which in clinical practice could help in early detection of tumor recurrence. Full article

Background/Objectives: Gastrin-releasing peptide receptor (GRPR) is overexpressed in breast cancer and might be used as a theranostics target. The expression of GRPR strongly correlates with estrogen receptor (ER) expression. Visualization of GRPR-expressing breast tumors might help to select the optimal treatment. Developing GRPR-specific probes for SPECT would permit imaging-guided therapy in regions with restricted access to PET facilities. In this first-in-human study, we evaluated the safety, biodistribution, and dosimetry of the [^99mTc]Tc-DB8 GRPR-antagonistic peptide. We also addressed the important issue of finding the optimal injected peptide mass. Methods: Fifteen female patients with ER-positive primary breast cancer were enrolled and divided into three cohorts receiving [^99mTc]Tc-DB8 (corresponding to three distinct doses of 40, 80, or 120 µg DB8) comprising five patients each. Additionally, four patients with ER-negative primary tumors were injected with 80 µg [^99mTc]Tc-DB8. The injected activity was 360 ± 70 MBq. Planar scintigraphy was performed after 2, 4, 6, and 24 h, and SPECT/CT scans followed planar imaging 2, 4, and 6 h after injection. Results: No adverse events were associated with [^99mTc]Tc-DB8 injections. The effective dose was 0.009–0.014 mSv/MBq. Primary tumors and all known lymph node metastases were visualized irrespective of injected peptide mass. The highest uptake in the ER-positive tumors was 2 h after injection of [^99mTc]Tc-DB8 at a 80 µg DB8 dose (SUV_max 5.3 ± 1.2). Injection of [^99mTc]Tc-DB8 with 80 µg DB8 provided significantly (p < 0.01) higher uptake in primary ER-positive breast cancer lesions than injection with 40 µg DB8 (SUV_max 2.0 ± 0.3) or 120 µg (SUV_max 3.2 ± 1.4). Tumor-to-contralateral breast ratio after injection of 80 μg was also significantly (p < 0.01, ANOVA test) higher than ratios after injection of other peptide masses. The uptake in ER-negative lesions was significantly lower (SUV_max 2.0 ± 0.3) than in ER-positive tumors. Conclusions: Imaging using [^99mTc]Tc-DB8 is safe, tolerable, and associated with low absorbed doses. The tumor uptake is dependent on the injected peptide mass. The injection of an optimal mass (80 µg) provides the highest uptake in ER-positive tumors. At optimal dosing, the uptake was significantly higher in ER-positive than in ER-negative lesions. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption. Full article

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

Background and Objectives: Metabolic tumor volume (MTV) and inflammation-based indices have recently gained attention as potential prognostic markers of diffuse large B-cell lymphoma (DLBCL). We aimed to evaluate the prognostic significance of metabolic and systemic inflammatory parameters in predicting treatment response, relapse, and overall survival (OS) in patients with DLBCL. Materials and Methods: This retrospective cohort study included 70 patients with DLBCL. Clinical characteristics, laboratory values, and metabolic parameters, including maximum standardized uptake value (SUVmax^liver and SUVmax), heterogeneity indices HI1 and HI2, and MTV were analyzed. Survival outcomes were assessed using Kaplan–Meier and log-rank tests. Receiver operating characteristic analyses helped evaluate the diagnostic performance of the selected biomarkers in predicting relapse and mortality. Univariate and multivariate logistic regression analyses were conducted to identify the independent predictors. Results: The mean OS and mean relapse-free survival (RFS) were 71.6 ± 7.4 and 38.7 ± 2.9 months, respectively. SUVmax^liver ≤ 22 and HI2 > 62.3 were associated with a significantly shorter OS. High lactate dehydrogenase (LDH) levels and HI2 > 87.9 were significantly associated with a reduced RFS. LDH, SUVmax^liver, and HI2 had a significant predictive value for relapse. SUVmax^liver and HI2 levels were also predictive of mortality; SUVmax^liver ≤ 22 and HI2 > 62.3 independently predicted mortality, while HI2 > 87.9 independently predicted relapse. MTV was not significantly associated with survival. Conclusions: Metabolic tumor burden and inflammation-based markers, particularly SUVmax^liver and HI2, are significant prognostic indicators of DLBCL and may enhance risk stratification and aid in identifying patients with an increased risk of relapse or mortality, potentially guiding personalized therapy. Full article

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

This study evaluated the bone regeneration effect and mechanical properties of “Sticky bone”, a mixture of platelet-rich fibrin (PRF) and synthetic bone grafts (SBGs), in the repair of large femoral bone defects in rabbits. Eighteen New Zealand white rabbits were included and randomly divided into a Sticky bone group and an SBG alone group. Bone graft samples were collected and analyzed at 4, 8, and 12 weeks after surgery. Micro- computed tomography (CT) analysis showed that the amount of the Sticky bone group in the grayscale ranges of 255–140 (highly mineralized tissue or unabsorbed bone powder) and 140–90 (representing new cancellous bone) was higher than that of the SBG group at each time point and decreased with the number of weeks. The compression strength test showed that the average compression strength of the Sticky bone group reached 5.17 MPa at the 12th week, which was 1.62 times that of the intact bone (3.19 MPa) and was significantly better than that of the SBG group (about 4.12 MPa). This study also confirmed for the first time that the use of a new polyethylene terephthalate (PET) blood collection tube to prepare PRF can stably release key growth factors such as platelet-derived growth factor-BB (PDGF-BB) and vascular endothelial growth factor (VEGF), which are conducive to early bone vascularization and cell proliferation. In summary, Sticky bone has the potential to promote bone formation, enhance tissue integration and mechanical stability, and can be used as an effective alternative material for repairing large-scale bone defects in clinical practice in the future. Full article

Background: Lysosomal-associated membrane protein 1 (LAMP1), typically localized to the lysosomal membrane, is increasingly implicated as a marker of cancer aggressiveness and metastasis when expressed on the cell surface. This study aimed to develop a LAMP1-targeted antibody-based PET tracer and assess its efficacy in mouse models of human breast and colon adenocarcinoma. Methods: To determine the source of LAMP1 expression, we utilized human single-cell RNA sequencing and spatial transcriptomics, complemented by in-house flow cytometry on xenografted mouse models. Tissue microarrays of multiple epithelial cancers and normal tissue were stained for LAMP-1, and staining was quantified. An anti-LAMP1 monoclonal antibody was conjugated with desferrioxamine (DFO) and labeled with zirconium-89 (⁸⁹Zr). Human triple-negative breast cancer (MDA-MB-231) and colon cancer (Caco-2) cell lines were implanted in nude mice. PET/CT imaging was conducted at 24, 72, and 168 h post-intravenous injection of ⁸⁹Zr-DFO-anti-LAMP1 and ⁸⁹Zr-DFO-IgG (negative control), followed by organ-specific biodistribution analyses at the final imaging time point. Results: Integrated single-cell and spatial RNA sequencing demonstrated that LAMP1 expression was localized to myeloid-derived suppressor cells (MDSCs) and cancer-associated fibroblasts (CAFs) in addition to the cancer cells. Tissue microarray showed significantly higher staining for LAMP-1 in tumor tissue compared to normal tissue (3986 ± 2635 vs. 1299 ± 1291, p < 0.001). Additionally, xenograft models showed a significantly higher contribution of cancer cells than the immune cells to cell surface LAMP1 expression. In vivo, PET imaging with ⁸⁹Zr-DFO-anti-LAMP1 PET/CT revealed detectable tumor uptake as early as 24 h post-injection. The ⁸⁹Zr-DFO-anti-LAMP1 tracer demonstrated significantly higher uptake than the control ⁸⁹Zr-DFO-IgG in both models across all time points (MDA-MB-231 SUV_max at 168 h: 12.9 ± 5.7 vs. 4.4 ± 2.4, p = 0.003; Caco-2 SUV_max at 168 h: 8.53 ± 3.03 vs. 3.38 ± 1.25, p < 0.01). Conclusions: Imaging of cell surface LAMP-1 in breast and colon adenocarcinoma is feasible by immuno-PET. LAMP-1 imaging can be expanded to adenocarcinomas of other origins, such as prostate and pancreas. 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