Predictors for acute exacerbation of interstitial pneumonia following lung cancer surgery: a multicenter study.

BACKGROUND: Acute exacerbation (AE) of interstitial lung disease (ILD) is one of the most serious complications during perioperative period of lung cancer resection. This study aimed to investigate the correlation between preoperative 2- deoxy-2-[18F]fluoro-D-glucose (18F-FDG) PET/CT findings and AE in lung cancer patients with ILD. METHODS: We retrospectively reviewed the data of 210 patients who underwent lung resection for non-small cell lung cancer. Relationships between clinical data and PET images and AE were evaluated. The patients were divided into an AE(+) and an AE(-) group for multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was conducted and the area under curve (AUC) was used to assess the predictive values. RESULTS: Among 210 patients, 48 (22.8%) were diagnosed with ILD based on chest CT. Among them, 9 patients (18.75%) developed AE after lung resection and were defined as AE(+) group. The course of ILD was longer in AE(+) group compared to AE(-) group. More patients in AE(+) group had a history of AE and chronic obstructive pulmonary disease (COPD) than in AE(-) group. The maximum standardized uptake value (SUVmax) of the noncancerous interstitial pneumonia (IP) area and cancers in AE(+) group was significantly higher compared to AE(-) group. Univariate logistic regression analysis showed that AE, COPD, SUVmax of the noncancerous IP area, SUVmax of cancer, surgical method were significantly correlated with AE. The course of ILD[OR(95%CI) 2.919; P = 0.032], SUVmax of the noncancerous IP area[OR(95%CI) 7.630;P = 0.012] and D-Dimer level[OR(95%CI) 38.39;P = 0.041] were identified as independent predictors for AE in patients with ILD after lung cancer surgery. When the three indicators were combined, we found significantly better predictive performance for postoperative AE than that of SUVmax of the noncancerous IP area alone [0.963 (95% CI 0.914-1.00); sensitivity, 100%, specificity 87.2%, P < 0.001 vs. 0.875 (95% CI 0.789 ~ 0.960); sensitivity, 88.9%, specificity, 76.9%, P = 0.001; difference in AUC = 0.088, Z = 1.987, P = 0.04]. CONCLUSION: The combination of the course of ILD, SUVmax of the noncancerous IP area and D-Dimer levels has high predictive value for the occurrence of AE in patients with concomitant interstitial lesions.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Nuclear medicine imaging modalities to detect incidentalomas and their impact on patient management: a systematic review.

PURPOSE: This systematic review aims to investigate the role of nuclear imaging techniques in detecting incidentalomas and their impact on patient management. METHODS: Following PRISMA guidelines, a comprehensive literature search was conducted from February to May 2022. Studies in English involving patients undergoing nuclear medicine studies with incidental tumor findings were included. Data on imaging modalities, incidentaloma characteristics, management changes, and follow-up were extracted and analyzed. RESULTS: Ninety-two studies involving 64.884 patients were included. Incidentalomas were detected in 611 cases (0.9%), with thyroid being the most common site. PET/CT with FDG and choline tracers showed the highest incidentaloma detection rates. Detection of incidentalomas led to a change in therapeutic strategy in 59% of cases. Various radiotracers demonstrated high sensitivity for incidentaloma detection, particularly in neuroendocrine tumors and prostate cancer. CONCLUSION: Nuclear imaging techniques play a crucial role in detecting incidentalomas, leading to significant changes in patient management. The high sensitivity of these modalities highlights their potential in routine oncology follow-up protocols. Future directions may include enhancing spatial resolution and promoting theranostic approaches for improved patient care.

Non-invasive mapping of brown adipose tissue activity with magnetic resonance imaging.

Thermogenic brown adipose tissue (BAT) has a positive impact on whole-body metabolism. However, in vivo mapping of BAT activity typically relies on techniques involving ionizing radiation, such as [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) and computed tomography (CT). Here we report a noninvasive metabolic magnetic resonance imaging (MRI) approach based on creatine chemical exchange saturation transfer (Cr-CEST) contrast to assess in vivo BAT activity in rodents and humans. In male rats, a single dose of the ß3-adrenoceptor agonist (CL 316,243) or norepinephrine, as well as cold exposure, triggered a robust elevation of the Cr-CEST MRI signal, which was consistent with the [18F]FDG PET and CT data and 1H nuclear magnetic resonance measurements of creatine concentration in BAT. We further show that Cr-CEST MRI detects cold-stimulated BAT activation in humans (both males and females) using a 3T clinical scanner, with data-matching results from [18F]FDG PET and CT measurements. This study establishes Cr-CEST MRI as a promising noninvasive and radiation-free approach for in vivo mapping of BAT activity.

Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era.

BACKGROUND: The metabolic tumour area (MTA) was found to be a promising predictor of prostate cancer. However, the role of MTA based on 18F-FDG PET/CT in diffuse large B-cell lymphoma (DLBCL) prognosis remains unclear. This study aimed to elucidate the prognostic significance of MTA and evaluate its incremental value to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) for DLBCL patients treated with first-line R-CHOP regimens. METHODS: A total of 280 consecutive patients with newly diagnosed DLBCL and baseline 18F-FDG PET/CT data were retrospectively evaluated. Lesions were delineated via a semiautomated segmentation method based on a 41% SUVmax threshold to estimate semiquantitative metabolic parameters such as total metabolic tumour volume (TMTV) and MTA. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values. Progression-free survival (PFS) and overall survival (OS) were the endpoints that were used to evaluate the prognosis. PFS and OS were estimated via Kaplan‒Meier curves and compared via the log-rank test. RESULTS: Univariate analysis revealed that patients with high MTA, high TMTV and NCCN-IPI ≥ 4 were associated with inferior PFS and OS (P < 0.0001 for all). Multivariate analysis indicated that MTA remained an independent predictor of PFS and OS [hazard ratio (HR), 2.506; 95% confidence interval (CI), 1.337-4.696; P = 0.004; and HR, 1.823; 95% CI, 1.005-3.310; P = 0.048], whereas TMTV was not. Further analysis using the NCCN-IPI model as a covariate revealed that MTA and NCCN-IPI were still independent predictors of PFS (HR, 2.617; 95% CI, 1.494-4.586; P = 0.001; and HR, 2.633; 95% CI, 1.650-4.203; P < 0.0001) and OS (HR, 2.021; 95% CI, 1.201-3.401; P = 0.008; and HR, 3.869; 95% CI, 1.959-7.640; P < 0.0001; respectively). Furthermore, MTA was used to separate patients with high NCCN-IPI risk scores into two groups with significantly different outcomes. CONCLUSIONS: Pre-treatment MTA based on 18F-FDG PET/CT and NCCN-IPI were independent predictor of PFS and OS in DLBCL patients treated with R-CHOP. MTA has additional predictive value for the prognosis of patients with DLBCL, especially in high-risk patients with NCCN-IPI ≥ 4. In addition, the combination of MTA and NCCN-IPI may be helpful in further improving risk stratification and guiding individualised treatment options. TRIAL REGISTRATION: This research was retrospectively registered with the Ethics Committee of the Third Affiliated Hospital of Soochow University, and the registration number was approval No. 155 (approved date: 31 May 2022).

Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.

Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

Metabolic Tumor Volume on 18-Fluorodeoxyglucose Positron Emission Tomography as a Prognostic Marker of Survival in Patients With Locally Advanced or Metastatic Neuroendocrine Neoplasms Treated With 177Lutetium-DOTA-Octreotate Peptide Receptor Radionuclide Therapy.

OBJECTIVE: We investigated metabolic tumor volume (MTV) and total lesion glycolysis (TLG) on pre-treatment FDG-PET as prognostic markers for survival in patients with metastatic neuroendocrine neoplasms (NENs) receiving peptide receptor radionuclide therapy (PRRT). METHODS: A retrospective review of patients with metastatic NENs receiving PRRT was undertaken. Pre-treatment FDG-PET images were analyzed and variables collected included MTV and TLG (dichotomized by median into high vs low). Main Outcomes were overall survival (OS) and progression-free survival (PFS) by MTV and TLG (high vs low). RESULTS: One hundred five patients were included. Median age was 64 years (50% male). Main primary NEN sites were small bowel (43.8%) and pancreas (40.0%). Median MTV was 3.8 mL and median TLG was 19.9. Dichotomization formed identical cohorts regardless of whether MTV or TLG were used. Median OS was 72 months; OS did not differ based on MTV/TLG high versus low (47.4 months vs not reached; hazard ratio, 0.43; 95% confidence interval [CI], 0.18-1.04; P = 0.0594). Median PFS was 30.4 months; PFS differed based on MTV/TLG high versus low (21.6 months vs 45.7 months; hazard ratio, 0.35; 95% CI, 0.19-0.64; P = 0.007). CONCLUSIONS: Low MTV/TLG on pre-treatment FDG-PET was associated with longer PFS in metastatic NEN patients receiving PRRT.

Diagnostic Performances of PET/CT Using Fibroblast Activation Protein Inhibitors in Patients with Primary and Metastatic Liver Tumors: A Comprehensive Literature Review.

PET/CT using radiolabeled fibroblast activation protein inhibitors (FAPIs) is a promising diagnostic tool in oncology, especially when non-increased and/or physiologically high [18F]FDG uptake (as in liver parenchyma) is observed. We aimed to review the role of PET/CT using radiolabeled FAPIs in primary and/or metastatic liver lesions, and to compare their performances with more "conventional" radiopharmaceuticals. A search algorithm based on the terms "FAPI" AND ("hepatic" OR "liver") was applied, with the last update on 1st January 2024. Out of 177 articles retrieved, 76 studies reporting on the diagnostic application of radiolabeled FAPI PET/CT in at least one patient harboring primary or metastatic liver lesion(s) were fully analyzed. Although there was some heterogeneity in clinical conditions and/or study methodology, PET/CT with radiolabeled FAPIs showed an excellent performance in common primary liver malignancies (hepatocarcinoma, intrahepatic cholangiocarcinoma) and liver metastases (mostly from the gastrointestinal tract and lungs). A higher tumor-to-background ratio for FAPIs than for [18F]FDG was found in primary and metastatic liver lesions, due to lower background activity. Despite limited clinical evidence, radiolabeled FAPIs may be used to assess the suitability and effectiveness of FAPI-derived therapeutic agents such as [177Lu]Lu-FAPI. However, future prospective research on a wider population is needed to confirm the excellent performance.

Association between Visceral Adipose Tissue Metabolism and Cerebral Glucose Metabolism in Patients with Cognitive Impairment.

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.

DCE-MRI to distinguish all monoclonal plasma cell disease stages and correlation with diffusion-weighted MRI/PET-based biomarkers in a hybrid simultaneous whole body-2-[18F]FDG-PET/MRI imaging approach.

BACKGROUND: Dynamic contrast-enhanced-MRI (DCE-MRI) is able to study bone marrow angiogenesis in patients with multiple myeloma (MM) and asymptomatic precursor diseases but its role in the management of MM has not yet been established. The aims of this prospective study was to compare DCE-MRI-based parameters between all monoclonal plasma cell disease stages in order to find out discriminatory parameters and to seek correlations with other diffusion-weighted MRI and positron emission tomography (PET)-based biomarkers in a hybrid simultaneous whole-body-2-[18F]fluorodeoxyglucose (FDG)-PET/MRI (WB-2-[18F]FDG-PET/MRI) imaging approach. METHODS: Patients with newly diagnosed Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM) or symptomatic MM according to international myeloma working group and underwent WB-2-[18F]FDG-PET/MRI imaging including bone marrow DCE sequences at the Nantes University Hospital were prospectively enrolled in this study before receiving treatment. RESULTS: One hundred and sixty-seven patients (N = 167, mean age: 64 years ± 11 [Standard deviation], 66 males) were considered for the analysis. DCE-MRI-based Peak Enhancement Intensity (PEI), Time to PEI (TPEI) and their maximum intensity time ratio (MITR: PEI/TPEI) values were significantly different between the different monoclonal plasma cell disease stages, PEI values increasing and TPEI values decreasing progressively along the spectrum of plasma cell disorders, from MGUS stage to symptomatic multiple myeloma. PEI values were significantly higher in patients with diffuse bone marrow involvement (either in PET or in MRI images) than in those without diffuse bone marrow involvement, unlike TPEI values. PEI and TPEI values were not significantly different between patients with or without focal bone lesions. CONCLUSION: Different DCE-MRI-based parameters (PEI, TPEI, MITR) could significantly differentiate all monoclonal plasma cell disease stages and complemented conventional MRI and PET-based biomarkers.

The prognostic value of pretreatment 18F-FDG PET-CT parameters with peripheral blood markers in patients with de novo metastatic nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To develop and validate a prognostic nomogram based on pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET-CT)radiomics parameters and peripheral blood markers for risk stratification in patients with de novo metastatic nasopharyngeal carcinoma (dmNPC). MATERIALS AND METHODS: A total of 558 patients with dmNPC were retrospectively enrolled between 2011 and 2019. Eligible patients were randomly divided into training and validation cohorts (7:3 ratio). A Cox regression model was used to identify prognostic factors for overall survival (OS). The predictive accuracy and discriminative ability of the prognostic nomogram were determined using the concordance index (C-index) and calibration curve. RESULTS: Independent factors derived from multivariable analysis of the training cohort to predict death were lactate dehydrogenase levels, pretreatment Epstein-Barr virus DNA, total lesion glycolysis of locoregional lesions, number of metastatic lesions, and age, all of which were assembled into a nomogram with (nomogram B) or without PET-CT parameters (nomogram A). The C-index of nomogram B for predicting death was 0.70, which was significantly higher than the C-index values for nomogram A. Patients were then stratified into low- and high-risk groups based on the scores calculated using nomogram B for OS. The median OS was significantly higher in the low-risk group than in the high-risk group (69.60 months [95 % CI: 58.50-108.66] vs. 21.40 months [95 % CI: 19.20-23.90]; p＜0.01). All the results were confirmed in the validation cohort. CONCLUSION: The proposed nomogram including PET-CT parameters yielded accurate prognostic predictions for patients with dmNPC, enabling effective risk stratification for these patients.

Baseline and early 18F-FDG PET/CT evaluations as predictors of progression-free survival in metastatic breast cancer patients treated with targeted anti-CDK therapy.

BACKGROUND: Exploring the value of baseline and early 18F-FDG PET/CT evaluations in prediction PFS in ER+/HER2- metastatic breast cancer patients treated with a cyclin-dependent kinase inhibitor in combination with an endocrine therapy. METHODS: Sixty-six consecutive breast cancer patients who underwent a pre-therapeutic 18F-FDG PET/CT and a second PET/CT within the first 6 months of treatment were retrospectively included. Metabolic tumour volume (MTV) and total lesion glycolysis (TLG) and Dmax, which represents tumour dissemination and is defined as the distance between the two most distant lesions, were computed. The variation in these parameters between baseline and early evaluation PET as well as therapeutic evaluation using PERCIST were assessed as prognosticators of PFS at 18 months. RESULTS: The median follow-up was equal to 22.5 months. Thirty progressions occurred (45.4%). The average time to event was 17.8 ± 10.4 months. At baseline, Dmax was the only predictive metabolic parameter. Patients with a baseline Dmax ≤ 18.10 cm had a significantly better 18 m-PFS survival than the others: 69.2% (7.7%) versus 36.7% (8.8%), p = 0.017. There was no association between PERCIST evaluation and 18 m-PFS status (p = 0.149) and there was no difference in 18 m-PFS status between patients classified as complete, partial metabolic responders or having stable metabolic disease. CONCLUSION: Disease spread at baseline PET, as assessed by Dmax, is predictive of an event occurring within 18 months. In the absence of early metabolic progression, which occurs in 15% of patients, treatment should be continued regardless of the quality of the initial response to treatment.

Head and neck tumor segmentation from [18F]F-FDG PET/CT images based on 3D diffusion model.

Objective.Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes.Approach.The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of 3D PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input, as well as further extending the diffusion model from 2D to 3D, were investigated based on various quantitative metrics and qualitative results.Main results.Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods (mean Dice of 0.739 compared to less than 0.726 for other methods). Compared to the diffusion model in 2D form, the proposed 3D model yielded superior results (mean Dice of 0.739 compared to 0.669). Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation (with mean Dice less than 0.570).Significance.This work demonstrated the effectiveness of the proposed 3D diffusion model in generating more accurate H&N tumor segmentation masks compared to the other reference methods.

The clinical and predictive value of 18F-FDG PET/CT metabolic patterns in a clinical Chinese cohort with autoimmune encephalitis.

AIMS: To investigate the diagnostic and predictive role of 18F-FDG PET/CT in patients with autoimmune encephalitis (AE) as a whole group. METHODS: Thrty-five patients (20 females and 15 males) with AE were recruited. A voxel-to-voxel semi-quantitative analysis based on SPM12 was used to analyze 18F-FDG PET/CT imaging data compared to healthy controls. Further comparison was made in different prognostic groups categorized by modified Rankin Scale (mRS). RESULTS: In total, 24 patients (68.6%) were tested positive neuronal antibodies in serum and/or CSF. Psychiatric symptoms and seizure attacks were major clinical symptoms. In the acute stage, 13 patients (37.1%) demonstrated abnormal brain MRI results, while 33 (94.3%) presented abnormal metabolism patterns. 18F-FDG PET/CT was more sensitive than MRI (p < 0.05). Patients with AE mainly presented mixed metabolism patterns compared to the matched controls, demonstrating hypermetabolism mainly in the cerebellum, BG, MTL, brainstem, insula, middle frontal gyrus, and relatively hypometabolism in the frontal cortex, occipital cortex, temporal gyrus, right parietal gyrus, left cingulate gyrus (p < 0.05, FWE corrected). After a median follow-up of 26 months, the multivariable analysis identified a decreased level of consciousness as an independent risk factor associated with poor outcome of AE (HR = 3.591, p = 0.016). Meanwhile, decreased metabolism of right superior frontal gyrus along with increased metabolism of the middle and upper brainstem was more evident in patients with poor outcome (p < 0.001, uncorrected). CONCLUSION: 18F-FDG PET/CT was more sensitive than MRI to detect neuroimaging abnormalities of AE. A mixed metabolic pattern, characterized by large areas of cortical hypometabolism with focal hypermetabolism was a general metabolic pattern. Decreased metabolism of right superior frontal gyrus with increased metabolism of the middle and upper brainstem may predict poor long-term prognosis of AE.

Verification of the effect of data-driven brain motion correction on PET imaging.

INTRODUCTION: Brain positron emission tomography/computed tomography (PET/CT) scans are useful for identifying the cause of dementia by evaluating glucose metabolism in the brain with F-18-fluorodeoxyglucose or Aß deposition with F-18-florbetaben. However, since imaging time ranges from 10 to 30 minutes, movements during the examination might result in image artifacts, which interfere with diagnosis. To solve this problem, data-driven brain motion correction (DDBMC) techniques are capable of performing motion corrected reconstruction using highly accurate motion estimates with high temporal resolution. In this study, we investigated the effectiveness of DDBMC techniques on PET/CT images using a Hoffman phantom, involving continuous rotational and tilting motion, each expanded up to approximately 20 degrees. MATERIALS AND METHODS: Listmode imaging was performed using a Hoffman phantom that reproduced rotational and tilting motions of the head. Brain motion correction processing was performed on the obtained data. Reconstructed images with and without brain motion correction processing were compared. Visual evaluations by a nuclear medicine specialist and quantitative parameters of images with correction and reference still images were compared. RESULTS: Normalized Mean Squared Error (NMSE) results demonstrated the effectiveness of DDBMC in compensating for rotational and tilting motions during PET imaging. In Cases 1 and 2 involving rotational motion, NMSE decreased from 0.15-0.2 to approximately 0.01 with DDBMC, indicating a substantial reduction in differences from the reference image across various brain regions. In the Structural Similarity Index (SSIM), DDBMC improved it to above 0.96 Contrast assessment revealed notable improvements with DDBMC. In continuous rotational motion, % contrast increased from 42.4% to 73.5%, In tilting motion, % contrast increased from 52.3% to 64.5%, eliminating significant differences from the static reference image. These findings underscore the efficacy of DDBMC in enhancing image contrast and minimizing motion induced variations across different motion scenarios. CONCLUSIONS: DDBMC processing can effectively compensate for continuous rotational and tilting motion of the head during PET, with motion angles of approximately 20 degrees. However, a significant limitation of this study is the exclusive validation of the proposed method using a Hoffman phantom; its applicability to the human brain has not been investigated. Further research involving human subjects is necessary to assess the generalizability and reliability of the presented motion correction technique in real clinical scenarios.

Comparative analysis of F-18 FDG PET/CT images between scrub typhus and systemic lupus erythematosus.

This study evaluated the use of F-18 fluorodeoxyglucose (FDG) PET/CT imaging to differentiate between scrub typhus and systemic lupus erythematosus (SLE) in patients presenting with lymphadenopathy. We carried out a retrospective analysis of 18 scrub typhus patients and seven SLE patients, using various imaging parameters, including lymph node size, spleen and liver lengths, the distance between the two farthest lesions (Dmax), and assessments of glucose metabolism. On FDG PET images, we measured the maximum standardized uptake value (SUVmax) of the lymph nodes, spleen, and liver and the mean standardized uptake value (SUVmean) of the liver and spleen. The Dmax values of scrub typhus patients were significantly longer than those of SLE patients, indicating that lymphadenopathy is more generalized in the patients with scrub typhus. The SUVmax values for the lymph node, spleen, and liver were also higher in patients with scrub typhus, while the SUVmean of the liver and spleen did not differ between the two groups. This study is the first to compare FDG PET/CT images between these two conditions, suggesting the potential of this imaging modality to provide critical diagnostic distinctions.

Diffuse lymphoma involvement of the spinal cord showed on [18F]FDG PET/MRI.

A 61-year-old woman with diffuse large B-cell lymphoma received a fluorine-18-deoxyglucose positron emission tomography/computed tomography ([¹8F]FDG PET/CT) for staging. Because of the obvious uptake of [¹8F]FDG in the spinal cord and brain, a positron emission tomography/magnetic resonance imaging (PET/MRI) was performed after the positron emission tomography/computed tomography (PET/CT). The images showed diffuse [¹8F]FDG uptake of the spinal cord and increased T2 signal intensity on MRI, which was suspected to be lymphoma involvement. The patient was diagnosed with diffuse large B-cell lymphoma involving the right maxillofacial region, right cervical lymph nodes, cervix, brain and spinal cord (stage IV of non-germinal center B-cell origin). After chemotherapy, the spinal [¹8F]FDG uptake level decreased significantly, which was considered to be a partial metabolic response. Our case was different from prior, which indicated the pattern of spinal cord involvement by lymphoma was focal.

Prognostic Significance of 18F-FDG PET/CT and Tumor Metabolic Changes in Patients With Pancreatic Ductal Adenocarcinoma.

BACKGROUND/AIM: 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is reportedly associated with the malignant potential of cancer. This study aimed to evaluate the association between FDG accumulation and tumor metabolism in pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: A prognostic analysis of data from 131 patients with PDAC who underwent FDG-PET/CT before curative-intent pancreatic surgery was performed. Capillary electrophoresis-mass spectrometry (CE-MS) was used to analyze the metabolome of tumor and non-neoplastic pancreas from 80 patients. These patients were divided into two groups: low SUVmax group (SUVmax <6.09) and high SUVmax group (SUVmax ≥6.09). RESULTS: Carbohydrate antigen 19-9 (CA19-9), maximum standardized uptake value (SUVmax) of PET, N stage, and postoperative chemotherapy were identified as significant prognostic factors by univariate analysis. SUVmax emerged as an independent prognostic factor for overall survival [hazard ratio (HR)=1.88, p<0.05] and disease-free survival (HR=2.01, p<0.05) in multivariate analysis. Metabolic analyses confirmed that 43 metabolites significantly differed depending on the accumulation of SUV in tumors. Metabolites involved in the removal of reactive oxygen species (e.g., hypotaurine, glutathione, Met), treatment resistance (UDP-N-acetylglucosamine), and proliferation (e.g., choline, leucine, isoleucine) were increased in the high SUVmax group. CONCLUSION: FDG accumulation is an important independent prognostic factor reflecting tumor activity associated with metabolic changes in cancer cells.

Potential Application of the Myocardial Scintigraphy Agent [123I]BMIPP in Colon Cancer Cell Imaging.

[123I]ß-methyl-p-iodophenyl-pentadecanoic acid ([123I]BMIPP), which is used for nuclear medicine imaging of myocardial fatty acid metabolism, accumulates in cancer cells. However, the mechanism of accumulation remains unknown. Therefore, this study aimed to elucidate the accumulation and accumulation mechanism of [123I]BMIPP in cancer cells. We compared the accumulation of [123I]BMIPP in cancer cells with that of [18F]FDG and found that [123I]BMIPP was a much higher accumulation than [18F]FDG. The accumulation of [123I]BMIPP was evaluated in the presence of sulfosuccinimidyl oleate (SSO), a CD36 inhibitor, and lipofermata, a fatty acid transport protein (FATP) inhibitor, under low-temperature conditions and in the presence of etomoxir, a carnitine palmitoyl transferase I (CPT1) inhibitor. The results showed that [123I]BMIPP accumulation was decreased in the presence of SSO and lipofermata in H441, LS180, and DLD-1 cells, suggesting that FATPs and CD36 are involved in [123I]BMIPP uptake in cancer cells. [123I]BMIPP accumulation in all cancer cell lines was significantly decreased at 4 °C compared to that at 37 °C and increased in the presence of etomoxir in all cancer cell lines, suggesting that the accumulation of [123I]BMIPP in cancer cells is metabolically dependent. In a biological distribution study conducted using tumor-bearing mice transplanted with LS180 cells, [123I]BMIPP highly accumulated in not only LS180 cells but also normal tissues and organs (including blood and muscle). The tumor-to-intestine or large intestine ratios of [123I]BMIPP were similar to those of [18F]FDG, and the tumor-to-large-intestine ratios exceeded 1.0 during 30 min after [123I]BMIPP administration in the in vivo study. [123I]BMIPP is taken up by cancer cells via CD36 and FATP and incorporated into mitochondria via CPT1. Therefore, [123I]BMIPP may be useful for imaging cancers with activated fatty acid metabolism, such as colon cancer. However, the development of novel imaging radiotracers based on the chemical structure analog of [123I]BMIPP is needed.