Bone marrow biopsy (BMB) and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) provide complementary information in staging diffuse large B-cell lymphoma (DLBCL). We evaluated the diagnostic performance and inter-reader reproducibility of a liver-referenced visual classification integrating bone marrow (BM) and splenic uptake. Survival associations were secondary exploratory objectives. This retrospective single-centre study included a primary diagnostic cohort of 107 patients with newly diagnosed DLBCL and adequate pretreatment BMB, and a separate prognostic cohort of 141 patients. Two nuclear medicine physicians independently classified BM and splenic uptake as High or Low relative to liver, incorporating focal uptake. Any High indicated that one or both organs were High. Diagnostic performance was evaluated in the full cohort and in a sensitivity cohort excluding focal BM uptake (n = 94, 12 BMB-positive). Progression-free survival (PFS) and overall survival (OS) were analysed using Cox regression. BMB was positive in 18 of 107 patients. Any High had 83.3

PURPOSE:This study aimed to evaluate the diagnostic accuracy of 18F-Florastamin prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET/CT) in detecting intermediate-risk prostate cancer and compare its performance with multiparametric magnetic resonance imaging (mpMRI). PATIENTS AND METHODS:Fifty-nine patients with suspected intermediate-risk prostate cancer (prostate-specific antigen: 3-20 ng/mL) were prospectively enrolled. All participants underwent examination with 18F-Florastamin PET/CT and mpMRI, with imaging results assessed using the PSMA-Reporting and Data System and Prostate Imaging-Reporting and Data System Version 2.1 scoring systems, respectively. Prostate biopsy served as the reference standard. Diagnostic metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC), were calculated to compare the 2 modalities. RESULTS:PSMA PET/CT demonstrated a sensitivity of 72.4%, specificity of 83.3%, and overall accuracy of 78%, with an AUC of 0.78 (95% CI: 0.67-0.89). Contrastingly, mpMRI showed a higher sensitivity (89.7%) but lower specificity (66.7%), achieving the same overall accuracy of 78% and an AUC of 0.78 (95% CI: 0.68-0.88). No statistically significant difference in AUC was observed between the 2 modalities (P=0.967). While PSMA PET/CT offered higher specificity and PPV, reducing false-positive results, mpMRI excelled in sensitivity and NPV, minimizing false negatives. CONCLUSIONS:18F-Florastamin PSMA PET/CT demonstrated comparable diagnostic accuracy to mpMRI, with the added benefit of higher specificity. These findings suggest that PSMA PET/CT could complement mpMRI in the diagnostic evaluation of intermediate-risk prostate cancer, enhancing patient stratification and aiding clinical decision-making.

Ischemia-reperfusion (I/R) injury, resulting from transient or permanent cerebral vessel occlusion, triggers oxidative stress, neuroinflammation, and blood-brain barrier (BBB) disruption, leading to progressive neuronal damage and cognitive decline. The hippocampus, due to its high metabolic demand and susceptibility to oxidative stress, is particularly vulnerable to I/R-induced injury. This study evaluated the neuroprotective effects of α-lipoic acid (α-LA), a potent antioxidant, using bilateral common carotid arteries occlusion/reperfusion (BCCAO/R) mouse model and an oxygen-glucose deprivation/reoxygenation in vitro model. In BCCAO/R mice, α-LA improved spatial memory without affecting motor activity and restored hippocampal tight junction proteins (Claudin-5 and Occludin) and antioxidant enzyme expression, indicating BBB stabilization and oxidative stress reduction. Although synaptic proteins (BDNF and PSD-95) were not restored, cognitive improvements suggest alternative protective mechanisms. In HT22 cells, α-LA decreased intracellular reactive oxygen species levels, enhanced viability, and inhibited apoptosis via decreased PARP cleavage and caspase-3 activation. These protective effects were linked to the activation of the Nrf2/ARE signaling pathway and the upregulation of its downstream antioxidant targets. Overall, α-LA demonstrated marked neuroprotective effects in ischemic models by reducing oxidative stress, preserving BBB integrity, and restoring hippocampal function, positioning it as a promising therapeutic candidate for ischemic brain injury.

Apples and Korean green chili peppers are rich in phytochemicals and recognized for their diverse bioactive properties. Given the potential to enhance these beneficial compounds, this study investigated the effects of mineral supplementation during cultivation on the antioxidant and antiproliferative activities of extracts from both crops. Mineral-enriched cultivation significantly increased the total phenolic and flavonoid contents in both crops, which was accompanied by enhanced DPPH and ABTS radical scavenging activities. Moreover, the mineral-supplemented extracts of Korean green chili pepper activated the Nrf2 signaling pathway and upregulated downstream antioxidant enzymes, including heme oxygenase-1 (HO-1), γ-glutamylcysteine ligase (GCL), and glutathione peroxidase (GPx). Notably, the mineral-supplemented Korean green chili pepper extract significantly suppressed the proliferation of human colorectal cancer cells. These findings suggest that mineral supplementation during cultivation may improve the functional quality of apples and Korean green chili peppers, supporting their potential application in cancer prevention and complementary therapeutic strategies.

Background:Low-dose chest computed tomography (LDCT) screening improves early detection of lung cancer but poses challenges such as false positives and overdiagnosis, especially for nodules smaller than 8 mm where follow-up guidelines are unclear. Traditional risk prediction models have limitations, and deep learning (DL) algorithms offer potential improvements but often require large datasets. This study aimed to develop a DL-based, label-free lung cancer risk prediction model using alternative LDCT images and validate it in individuals without non-calcified solid pulmonary nodules larger than 8 mm. Methods:We utilized LDCT scans from individuals without non-calcified solid nodules larger than 8 mm to develop a DL-based lung cancer risk prediction model. An alternative training dataset included 1,064 LDCT scans: 380 from patients with pathologically confirmed lung cancer and 684 from control individuals without lung cancer development over 5 years. For the lung cancer group, only the contralateral lung (without the tumor) was analyzed to represent high-risk individuals without large nodules. The LDCT scans were randomly divided into training and validation sets in a 3:1 ratio. Four three-dimensional (3D) convolutional neural networks (CNNs; 3D-CNN, MobileNet v2, SEResNet18, EfficientNet-B0) were trained using densely connected U-Net (DenseUNet)-segmented lung parenchyma images. The models were validated on a real-world test dataset comprising 1,306 LDCT scans (1,254 low-risk and 52 high-risk individuals) and evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Brier scores, and calibration measures. Results:In the validation dataset, the AUC values were 0.801 for 3D-CNN, 0.802 for MobileNet v2, 0.755 for EfficientNet-B0, and 0.833 for SEResNet18. Corresponding Brier scores were 0.169, 0.175, 0.217, and 0.156, respectively, indicating good calibration, especially for SEResNet18. In the test dataset, the AUC values were 0.769 for 3D-CNN, 0.753 for MobileNet v2, 0.681 for EfficientNet-B0, and 0.820 for SEResNet18, with Brier scores of 0.169, 0.180, 0.202, and 0.138, respectively. The SEResNet18 model demonstrated the best performance, achieving the highest AUC and lowest Brier score in both validation and test datasets. Conclusions:Our study demonstrated that DL-based, label-free lung cancer risk prediction models using alternative LDCT images can effectively predict lung cancer development in individuals without non-calcified solid pulmonary nodules larger than 8 mm. By analyzing lung parenchyma on LDCT images without relying on nodule detection, these models may enhance the efficiency of LDCT screening programs. Further prospective studies are needed to determine their clinical utility and impact on screening protocols, and validation in larger, diverse populations is necessary to ensure generalizability.

Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk factors. IVH often occurs within a few hours after birth, yet its onset cannot be reliably predicted using clinical symptoms or vital signs such as blood pressure or heart rate. Accurate early prediction of IVH is critical for timely intervention but remains challenging due to the limited feasibility of current prediction methods in resource-constrained settings. Traditional prediction methods often require advanced equipment and specialized expertise. Therefore, developing a predictive model based on a limited set of available factors is crucial for accurate and reliable early IVH prediction in VLBWIs. We propose a deep neural network-based model with an attention mechanism (DNN-A) that utilizes a limited set of readily available clinical factors. The model was trained and tested on data from 387 infants, incorporating eight variables, including maternal age, delivery mode, endotracheal intubation, birth weight, gestational age at delivery, APGAR scores at 1 and 5 min, and sex. Our DNN-A model achieved 90% and 87% accuracies in the training and testing sets, respectively, with an area under the curve of 87, and outperformed various state-of-the-art machine-learning-based models for IVH prediction. These results underscore the effectiveness of DNN-A for predicting the risk of IVH in VLBWIs in low-resource settings.

Background and Objectives: Therapeutic hypothermia (TH) shows promise as an approach with neuroprotective effects, capable of reducing secondary brain damage and intracranial pressure following successful mechanical thrombectomy in the acute phase. However, its effect on cognitive impairment remains unclear. This study investigated whether TH can improve cognitive impairment in a mouse model of transient middle cerebral artery occlusion followed by reperfusion (tMCAO/R). Materials and Methods: Nine-week-old C57BL/6N mice (male) were randomly assigned to three groups: sham, tMCAO/R, and tMCAO/R with TH. Cognitive function was assessed 1 month after model induction using the Y-maze test, and regional cerebral glucose metabolism was measured through positron emission tomography with fluorine-18 fluorodeoxyglucose. Results: tMCAO/R induced cognitive impairment, which showed improvement with TH. The TH group exhibited a significant recovery in cerebral glucose metabolism in the thalamus compared to the tMCAO/R group. Conclusions: These findings indicate that TH may hold promise as a therapeutic strategy for alleviating ischemia/reperfusion-induced cognitive impairment.

Background: Intrahepatic cholangiocarcinoma (IHCC) is highly aggressive primary hepatic malignancy with an increasing incidence. Objective: This study aimed to develop machine learning-based radiomic clustering using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for predicting recurrence-free survival (RFS) and overall survival (OS) in IHCC. Methods: We retrospectively reviewed pretreatment F-18 FDG PET/CT scans of 60 IHCC patients who underwent surgery without neoadjuvant treatment between January 2008 and July 2020. Radiomic features such as first order, shape, and gray level were extracted from the scans of 52 patients and analyzed using unsupervised hierarchical clustering. Results: Of the 60 patients, 36 experienced recurrence and 31 died during follow-up. Eight patients with a negative FDG uptake were classified as Group 0. The unsupervised hierarchical clustering analysis divided the total cohort into three clusters (Group 1: n = 27; Group 2: n = 23; Group 3: n = 2). The Kaplan–Meier curves showed significant differences in RFS and OS among the clusters (p < 0.0001). Multivariate analyses showed that the PET radiomics grouping was an independent prognostic factor for RFS (hazard ratio (HR) = 3.03, p = 0.001) and OS (HR = 2.39, p = 0.030). Oxidative phosphorylation was significantly activated in Group 1, and the KRAS, P53, and WNT β-catenin pathways were enriched in Group 2. Conclusions: This study demonstrated that machine learning-based PET radiomics clustering can preoperatively predict prognosis and provide valuable information complementing the genomic profiling of IHCC.

Visceral adipose tissue (VAT) dysfunction has been recently recognized as a potential contributor to the development of Alzheimer’s disease (AD). This study aimed to explore the relationship between VAT metabolism and cerebral glucose metabolism in patients with cognitive impairment. This cross-sectional prospective study included 54 patients who underwent 18F-fluorodeoxyglucose (18F-FDG) brain and torso positron emission tomography/computed tomography (PET/CT), and neuropsychological evaluations. VAT metabolism was measured by 18F-FDG torso PET/CT, and cerebral glucose metabolism was measured using 18F-FDG brain PET/CT. A voxel-based analysis revealed that the high-VAT-metabolism group exhibited a significantly lower cerebral glucose metabolism in AD-signature regions such as the parietal and temporal cortices. In the volume-of-interest analysis, multiple linear regression analyses with adjustment for age, sex, and white matter hyperintensity volume revealed that VAT metabolism was negatively associated with cerebral glucose metabolism in AD-signature regions. In addition, higher VAT metabolism was correlated with poorer outcomes on cognitive assessments, including the Korean Boston Naming Test, Rey Complex Figure Test immediate recall, and the Controlled Oral Word Association Test. In conclusion, our study revealed significant relationships among VAT metabolism, cerebral glucose metabolism, and cognitive function. This suggests that VAT dysfunction actively contributes to the neurodegenerative processes characteristic of AD, making VAT dysfunction targeting a novel AD therapy approach.

Jahr, Jonathan S. MD, PhD, DABA, FASA; Kim, Hae Won PhD; Rojhani, Allen MD; Iskandar, Sandra MD Author Information

Very-low-birth-weight (VLBW) infants are more likely to have poor neurodevelopmental outcomes, even if structural abnormalities are not observed during brain magnetic resonance imaging (MRI). The purpose of the present study was to determine whether cerebral glucose metabolism is correlated with neurodevelopmental outcomes in VLBW infants without structural abnormalities. Twenty-seven VLBW infants (birth weight < 1,500 g) without structural abnormalities were prospectively enrolled. All infants underwent F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) examinations at term-equivalent ages, and the regional glucose metabolic ratios were calculated. Neurodevelopmental outcomes were assessed using the Mental Development Index (MDI) and the Psychomotor Development Index (PDI) of the Bayley Scales of Infant Development-II at a corrected age of 18–24 months. Poor neurodevelopmental outcomes were defined as an MDI or PDI score < 85. The glucose metabolic ratio in the right central region of the brain was significantly correlated with the MDI score (r = 0.505, p = 0.007). The glucose metabolic ratios in the right central region and right insula in the poor-neurodevelopmental-outcome group were significantly lower than those in the good-neurodevelopmental-outcome group (1.03 ± 0.02 vs. 1.08 ± 0.04, p = 0.004, and 1.08 ± 0.05 vs. 1.13 ± 0.05, p = 0.018, respectively). Furthermore, the right central region and insula exhibited large extent of metabolic connectivity in infants with good neurodevelopmental outcome than that in infants with poor neurodevelopmental outcome. Cerebral glucose metabolism was correlated with the neurodevelopmental outcomes of VLBW infants at a corrected age of 18–24 months.

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET). We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set. The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively]. The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.

OBJECTIVE:Impaired lung function is associated with an increased risk for cognitive decline. F-18 fluorodeoxyglucose (FDG) PET is a well-known neurodegenerative biomarker for dementia. We investigated the association between lung and brain function using FDG PET in patients with lung cancer.METHODS:A random sub-sample of 102 patients with lung cancer and without a self-reported history of neuropsychiatric disorders were recruited and underwent both lung function tests and FDG PET scans before treatment. Lung function was analyzed as the percentage predicted value (% pred) of forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1). FDG uptake was measured as standardized uptake values (SUVs) in the frontal, parietal, temporal, and occipital cortices and cognition-related regions. Regional SUV ratios (SUVRs) were calculated by dividing the SUV in each region by the whole-brain SUV and were then evaluated against lung function indices and clinical variables.RESULTS:After excluding five patients with brain metastases, 97 patients were included in the final analysis (mean age, 67.7 ± 10.3 years). Mean FVC and mean FEV1 were 80.0% ± 15.4% and 77.6% ± 17.8%, respectively. Both FVC and FEV1 were positively correlated with SUVRs in all brain regions after adjusting the data for clinical variables. The degree of decrease in SUVRs related to lung function was not significantly different between cognition-related regions and other regions.CONCLUSION:Impaired lung function was associated with decreased glucose metabolism in all regions of the brain, indicating that cognitive decline related to decreased glucose metabolism may be due to reduced perfusion.

We developed and validated a new staging system that includes metabolic information from pretreatment [ 18 F]Fluorodeoxyglucose ([ 18 F]FDG) positron emission tomography/computed tomography (PET/CT) for predicting disease-specific survival (DSS) in gastric cancer (GC) patients. Overall, 731 GC patients undergoing preoperative [ 18 F]FDG PET/CT were enrolled and divided into the training (n = 543) and validation (n = 188) cohorts. A metabolic score (MS) was developed by combining the maximum standardized uptake value (SUVmax) of the primary tumor (T_SUVmax) and metastatic lymph node (N_SUVmax). A new staging system incorporating the MS and tumor-node-metastasis (TNM) stage was developed using conditional inference tree analysis. The MS was stratified as follows: score 1 (T_SUVmax ≤ 4.5 and N_SUVmax ≤ 1.9), score 2 (T_SUVmax > 4.5 and N_SUVmax ≤ 1.9), score 3 (T_SUVmax ≤ 4.5 and N_SUVmax > 1.9), and score 4 (T_SUVmax > 4.5 and N_SUVmax > 1.9) in the training cohort. The new staging system yielded five risk categories: category I (TNM I, II and MS 1), category II (TNM I, II and MS 2), category III (TNM I, II and MS ≥ 3), category IV (TNM III, IV and MS ≤ 3), and category V (TNM III, IV and MS 4) in the training cohort. DSS differed significantly between both staging systems; the new staging system showed better prognostic performance in both training and validation cohorts. The MS was an independent prognostic factor for DSS, and discriminatory power of the new staging system for DSS was better than that of the conventional TNM staging system alone.

Nuclear medicine neuroimaging is able to show functional and molecular biologic abnormalities in various neuropsychiatric diseases. Therefore, it has played important roles in the clinical diagnosis and in research on the normal and pathological states of the brain. More than 400 outstanding studies have been conducted by Korean researchers over the past 60 years. In the 1990s, when multiheaded single-photon emission computed tomography (SPECT) scanners were first introduced in South Korea, stroke research using brain perfusion SPECT was conducted. With the spread of positron emission tomography (PET) scanners in the 2000s, research on the clinical usefulness of PET and the evaluation of pathophysiology in various diseases such as epilepsy, brain tumors, degenerative brain diseases, and other neuropsychiatric diseases were actively conducted using [18F]FDG and various neuroreceptor tracers. In the 2010s, with the clinical application of new radiopharmaceuticals for amyloid and tau imaging, research demonstrating the clinical usefulness of PET imaging and the pathophysiology of dementia has increased rapidly. It is expected that the role of nuclear medicine will expand with the development of new radiopharmaceuticals and analysis technologies, along with the application of artificial intelligence for early and differential diagnosis, and the development of therapeutic agents for degenerative brain diseases.

The visceral adipose tissue (VAT) has been recognized as an endocrine organ, and VAT dysfunction could be a risk factor for Alzheimer’s disease (AD). We aimed to evaluate the association of VAT metabolism with AD pathology. This cross-sectional study included 54 older subjects with cognitive impairment who underwent 2-deoxy-2-[fluorine-18]-fluoro-D-glucose (18F-FDG) torso positron emission tomography (PET) and 18F-florbetaben brain PET. 18F-FDG uptake in VAT on 18F-FDG PET images was used as a marker of VAT metabolism, and subjects were classified into high and low VAT metabolism groups. A voxel-based analysis revealed that the high VAT metabolism group exhibited a significantly higher cerebral amyloid-β (Aβ) burden than the low VAT metabolism group. In the volume-of-interest analysis, multiple linear regression analyses with adjustment for age, sex, and white matter hyperintensity volume revealed that 18F-FDG uptake in VAT was significantly associated with the cerebral Aβ burden (β = 0.359, p = 0.007). In conclusion, VAT metabolism was associated with AD pathology in older subjects. Our findings suggest that VAT dysfunction could contribute to AD development.

BACKGROUND Chronic cerebral hypoperfusion (CCH) is known to induce Alzheimer's disease (AD) pathology, but its mechanism remains unclear. The purpose of this study was to identify the cerebral regions that are affected by CCH, and to evaluate the development of AD pathology in a rat model of CCH. METHODS A rat model of CCH was established by bilaterally ligating the common carotid arteries in adult male rats (CCH group). The identical operations were performed on sham rats without arteries ligation (control group). Regional cerebral glucose metabolism was evaluated at 1 and 3 months after bilateral CCA ligation using positron emission tomography with F-18 fluorodeoxyglucose. The expression levels of amyloid β40 (Aβ40), amyloid β42 (Aβ42), and hyperphosphorylated tau were evaluated using western blots at 3 months after the ligation. Cognitive function was evaluated using the Y-maze test at 3 months after the ligation. RESULTS At 1 month after the ligation, cerebral glucose metabolism in the entorhinal, frontal association, motor, and somatosensory cortices were significantly decreased in the CCH group compared with those in the control group. At 3 months after the ligation, cerebral glucose metabolism was normalized in all regions except for the anterodorsal hippocampus, which was significantly decreased compared with that of the control group. The expression of Aβ42 and the Aβ42/40 ratio were significantly higher in the CCH group than those in the control group. The phosphorylated-tau levels of the hippocampus in the CCH group were significantly lower than those in the control group. Cognitive function was more impaired in the CCH group than that in the control group. CONCLUSION Our findings suggest that CCH causes selective neurodegeneration of the anterodorsal hippocampus, which may be a trigger point for the development of AD pathology.

Pulmonary pleomorphic carcinoma (PPC) is a rare type of non-small cell lung cancer (NSCLC) with a more aggressive clinical course and a worse outcome than other types of NSCLC. Pembrolizumab, a monoclonal antibody targeting programmed cell death-1 (PD-1), has been approved as the first-line treatment for advanced NSCLC with robust PD-L1 expression in at least 50% of tumour cells, without epidermal growth factor receptor gene (EGFR) mutations or anaplastic lymphoma kinase gene (ALK) rearrangement. Here, we report the case of an 81-year-old man with multiple comorbidities who was diagnosed with PPC and showed a robust response to pembrolizumab followed by radiation therapy without adverse effects. In the absence of randomized clinical trials for PPCs, our case report demonstrates the potential application of pembrolizumab and radiation therapy for the treatment of PPCs.