Recommendations on the Use of 18F-FDG PET in Oncology

Redox and actin, a fascinating story

Actin is an extraordinarily complex protein whose functions are essential to cell motility, division, contraction, signaling, transport, tissular structures, DNA repair, and many more cellular activities critical to life for both animals and plants. It is one of the most abundant and conserved proteins and it exists in either a soluble, globular (monomeric, G-actin) or an insoluble, self-assembled (polymerized or filamentous actin, F-actin) conformation as a key component of the cytoskeleton. In the early 1990's little, if anything, was known about the impact of reactive oxygen species (ROS) on the biology of actin except that ROS could disrupt the actin cytoskeleton. Instructively, G-actin is susceptible to alteration by ROS, and thus, purification of G-actin is typically performed in the presence of strong antioxidants (like dithiothreitol) to limit its oxidative degradation. In contrast, F-actin is a more stable conformation and thus actin can be kept relatively intact in purified preparations as filaments at low temperature for extended periods of time. Both G- and F-actin interact with a myriad of intracellular proteins and at least with a couple of extracellular proteins, and these interactions are essential to the many actin functions. This review will show how, over the past 30 years, our understanding of the role of ROS for actin biology has evolved from noxious denaturizing agents to remarkable regulators of the actin cytoskeleton in cells and consequent cellular functions.

The prognostic value of pretreatment [18F]FDG PET/CT parameters in esophageal cancer: a meta-analysis

OBJECTIVES

This study aims to evaluate the prognostic implications of pretreatment [18F]FDG-PET metrics in esophageal cancer patients through a meta-analysis of the existing literature.

METHODS

We carefully searched electronic databases, including PubMed and Embase, from inception to April 1, 2024, to identify studies describing the prognostic value of pretreatment PET metrics for advanced esophageal cancer. Clinical endpoints examined were overall survival (OS), recurrence-free survival (RFS)/disease-free survival (DFS), and progression-free survival (PFS). Hazard ratios (HRs) for PFS and OS were taken directly from the original reports.

RESULTS

Forty-seven publications, including 5504 patients, were included in our analysis. OS and PFS were analyzed in 31 and nine studies, respectively, and DFS/RFS was analyzed in 16 studies. The comprehensive pooled analysis revealed significant associations between metabolic parameters derived from positron emission tomography (PET) imaging and clinical outcomes. Expressly, the pooled HR indicated that patients with higher SUVmax were significantly associated with poor PFS (HR: 1.06; 95% CI: 1.01-1.12, p = 0.011) and poor RFS/DFS (HR: 1.09; 95% CI: 1.02-1.18, p = 0.019). Patients with higher SUVmean were significantly associated with poorer OS (HR: 1.07; 95% CI: 1.01-1.14, p = 0.025). High MTV was significantly associated with inferior OS (HR: 1.02; 95% CI: 1.00-1.05, p = 0.049). High TLG was significantly associated with poorer RFS/DFS (HR: 2.02; 95% CI: 1.11-3.68, p = 0.022).

CONCLUSION

This study unveiled pretreatment FDG-derived parameters as valuable prognostic indicators in assessing esophageal cancer outcomes. Specifically, SUVmax is associated with PFS and RFS/DFS. SUVmean and MTV were correlated with OS, and TLG was only associated with RFS/DFS.

KEY POINTS

Question Inconsistent findings on the prognostic value of pretreatment [18F]FDG PET parameters in esophageal cancer require comprehensive analysis to clarify their role in outcome prediction. Findings Higher pretreatment [18F]FDG-PET metrics (SUVmax, SUVmean, MTV, TLG) are associated with poor survival outcomes, emphasizing their potential value in enhancing prognostic assessments for esophageal cancer. Clinical relevance This study highlights the prognostic significance of pretreatment [18F]FDG-PET metrics in esophageal cancer, providing valuable insights for patient outcome prediction and potentially guiding personalized treatment strategies.

Sequential synthesis of [18F]FDG and [68Ga]Ga-DOTA-TATE on the AllinOne radiosynthesizer: A fully automated dual-radionuclides protocol with single cassette and reagents loading

BACKGROUND

The development of a unified and efficient protocol for the automated synthesis of PET imaging agents is crucial for enhancing operational efficiency and safety. This study aimed to streamline the sequential production process of 18F-fluorodeoxyglucose ([18F]FDG) and [68Ga]Ga-DOTA-TATE with only single cassette and reagents loading using the AllinOne 36-valve synthesis module, with the goal of improving hot cell utilization and minimizing radiation exposure for operators. The protocol was designed to allow for sequential synthesis without the need for repeated hot cell access or waiting for radiation levels to decrease, thereby reducing the time and resources required for PET imaging agent production.

RESULTS

Our study demonstrated the stability and reliability of the newly designed synthesis protocol. The activity yields for two batches of [18F]FDG were 73 ± 6.2 % and 64 ± 4.7 % (n = 3), respectively, all with synthesis times about 23 min, and with a radiochemical purity consistently over 96 %. For [68Ga]Ga-DOTA-TATE, the yield was 71 ± 5.8 % with synthesis times about 18 min (n = 3), with a purity exceeding 97 %. The synthesized products met all quality control criteria, including appearance, pH value, radioactivity concentration, sterility, endotoxin levels, and solvent residue.

CONCLUSION

The single-cassette protocol significantly improved efficiency and reduced radiation exposure. High yields and purities confirm its clinical feasibility, ensuring PET imaging agent availability. This scalable solution enhances patient care by simplifying the production process and meeting the demands of various patients for different PET imaging agents within a single day, demonstrating its potential in clinical settings.

Enhancing 18F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm

BACKGROUND

As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to identify methods that ensure consistent diagnostic accuracy and maintain image quality. Deep progressive learning (DPL) algorithm, an Artificial Intelligence(AI)-based PET reconstruction technique, offers a promising solution.

METHODS

150 patients underwent 18F-FDG PET/CT scans and were categorized by BMI into underweight, normal, and overweight groups. PET images were reconstructed using both OSEM and DPL and their image quality was assessed both visually and quantitatively. Visual assessment employed a 5-point Likert scale to evaluate overall score, image sharpness, image noise, and diagnostic confidence. Quantitative assessment parameters included the background liver image-uniformity-index ([Formula: see text]) and signal-to-noise ratio ([Formula: see text]). Additionally, 466 identifiable lesions were categorized by size: sub-centimeter and larger. We compared maximum standard uptake value ([Formula: see text]), signal-to-background ratio ([Formula: see text]), [Formula: see text], contrast-to-background ratio ([Formula: see text]), and contrast-to-noise ratio ([Formula: see text]) of these lesions to evaluate the diagnostic performance of the DPL and OSEM algorithms across different lesion sizes and BMI categories.

RESULTS

DPL produced superior PET image quality compared to OSEM across all BMI groups. The visual quality of DPL showed a slight decline with increasing BMI, while OSEM exhibited a more significant decline. DPL maintained a stable [Formula: see text] across BMI increases, whereas OSEM exhibited increased noise. In the DPL group, quantitative image quality for overweight patients matched that of normal patients with minimal variance from underweight patients. In contrast, OSEM demonstrated significant declines in quantitative image quality with rising BMI. DPL yielded significantly higher contrast ([Formula: see text], [Formula: see text],[Formula: see text]) and [Formula: see text] than OSEM for all lesions across all BMI categories.

CONCLUSION

DPL consistently provided superior image quality and lesion diagnostic performance compared to OSEM across all BMI categories in 18F-FDG PET/CT scans. Therefore, we recommend using the DPL algorithm for 18F-FDG PET/CT image reconstruction in all BMI patients.

Beyond fluorodeoxyglucose: Molecular imaging of cancer in precision medicine

Cancer molecular imaging is the noninvasive visualization of a process unique to or altered in neoplasia, such as proliferation, glucose metabolism, and receptor expression, which is relevant to patient management. Several molecular imaging modalities are now available, including magnetic resonance, optical, and nuclear imaging. Nuclear imaging, particularly using fluorine-18-fluorodeoxyglucose positron emission tomography, is widely used in the staging and response assessment of multiple cancer types. However, at this writing, new nuclear medicine probes, especially positron emission tomography tracers, are increasingly used or are being investigated for cancer evaluation. This review focuses on these probes, their biologic targets, and the applications or potential applications for their use in the assessment of various neoplasms, including both probes available for commercial use-such as somatostatin receptor ligands in neuroendocrine tumors, prostate-specific membrane antigen ligands in prostate cancer, norepinephrine analogs in neural crest tumors like neuroblastoma, and estrogen analogs in breast cancer-and others in clinical development, such as fibroblast-activating protein inhibitors, C-X-C chemokine receptor type 4 ligands, and monoclonal antibodies targeting receptor tyrosine kinases, CD4-positive or CD8-positive tumor-infiltrating lymphocytes, tumor-associated macrophages, and cancer stem cell biomarkers. These developments represent a major step toward the integration of molecular imaging as a powerful tool in precision medicine, with an expectedly significant impact on patient management and outcome.

The metabolic underpinnings of sebaceous lipogenesis

Sebaceous glands synthesize and secrete sebum, a mélange of lipids and other cellular products that safeguards the mammalian integument. Differentiating sebocytes delaminate from the basal membrane and dislodge towards the gland's middle, where they eventually undergo a poorly understood death mode in which the whole cell becomes a secretion product (holocrine secretion). Supported by recent transcriptomics data, this review examines the idea that peripheral sebocytes have a remarkable ability to draw nutrients from the blood and become committed to unrestrainedly invest all available resources into synthetic processes for accomplishing sebum synthesis, thereby exploiting core metabolic fluxes as glycogen turnover, glutamine-directed anaplerosis, the pentose phosphate pathway and de novo lipogenesis. Finally, we propose that metabolic-driven processes are an important mechanistic component of holocrine secretion. A deeper understanding of these metabolic adaptations could indicate novel strategies for modulating sebum synthesis, a key pathogenic factor in acne and other skin diseases.

Efficacy of 18F-Fluoro-2-Deoxyglucose Positron Emission Tomography as a Predictor of Treatment Response to Neoadjuvant S-1 + Oxaliplatin Chemotherapy for Gastric Cancer

BACKGROUND

Neoadjuvant chemotherapy is widely recognized as the established treatment for advanced gastric cancer. However, predicting its efficacy before surgery remains challenging.

AIM

The present study aimed to evaluate the effectiveness of 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET) as a predictor of treatment response to the S-1+Oxaliplatin regimen (SOX).

METHODS AND RESULTS

Thirty patients who underwent gastrectomy following neoadjuvant SOX between January 2021 and July 2023 were included. Patients underwent FDG-PET pre- and postsurgery. The maximum standardized uptake value (SUVmax) from FDG-PET was examined in relation to histological tumor response and prognosis. SUVmax decreased significantly after chemotherapy in all patients (p < 0.001), especially in those with Grade 1a, 2, and 3 tumors (p < 0.05). SUV reduction increased stepwise with the histological response grade. Optimal cut-off values for the percentage decrease in SUVmax (ΔSUVmax) predictive of histologic efficacy were identified as 53% (area under curve 0.855, p = 0.0018) for Grade 1b or higher and 75% (area under curve 0.806, p = 0.0044) for Grade 2 or higher. Patients with ΔSUVmax > 50% had improved recurrence-free survival (p = 0.027).

CONCLUSION

FDG-PET may be useful as a predictor of treatment response in neoadjuvant SOX therapy for gastric cancer. The determination of the optimal ΔSUVmax value may enhance the precision of histological tumor response prediction.

Novel Clinical PET Tracers in the Pipeline for Melanoma

PURPOSE

The aim of this review is to provide an overview of novel clinical PET tracers in the pipeline for melanoma. Secondarily, to provide a head-to-head comparison with the current clinical standard used in clinical practice, [18F]FDG, if available.

RECENT FINDINGS

[18F]FDG PET/CT has become important in the clinical setting for melanoma as it serves many purposes, but lacks other important qualities due its nonspecific nature. There is an increased clinical need for specific tracers. Many new PET tracers, such as melanin-targeted and antibody-based probes, have been studied in melanoma with the intention of achieving high sensitivity detection of metastases and small lesions. There are four main groups of PET tracers in de pipeline for melanoma: melanin-, FAP-, PD-1/PD-L1- and CD8+ T cell-tracers. Melanin-targeted tracers and FAP inhibitors revealed potential for diagnostic application, whilst PD-1/PD-L1 and CD8+ T cell tracers demonstrated potential for response assessment and prediction. In conclusion, research has revealed promising results from current (ongoing) studies; however, more melanoma patients need to be included to further assess the value of these tracers.

Advances and future directions in radiopharmaceutical delivery for cancer treatment

INTRODUCTION

Targeted radiopharmaceutical therapies (RPTs) have emerged as a promising approach for the precise treatment of various cancers. Delivering ionizing radiation directly to cancer cells while sparing surrounding healthy tissue, radiopharmaceuticals offer enhanced efficacy and reduced toxicity compared to conventional external beam radiation therapy (i.e. photons and electrons).

AREAS COVERED

In the current era of personalized cancer care, the appropriate choice of RPTs for a clinical condition and the specific patient's care needs to be better understood. Several available RPT agents with their respective clinical applicability along with rapidly ongoing research in this field have now given RPTs the ability to lend themselves to a personalized medicine focus. This review provides an overview of recent advancements in RPT, including nuclide selection and development, molecular targeting strategies, radiopharmaceutical development, and clinical applications.

EXPERT OPINION

We discuss the underlying principles, challenges, and opportunities for future development. Furthermore, we explore emerging technologies and future directions in the field, highlighting the potential impact on personalized cancer care.

Advances in the clinical diagnosis of lung cancer using contrast-enhanced ultrasound

Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the urgent need for innovative diagnostic tools to improve early detection and patient outcomes. Contrast-enhanced ultrasound (CEUS) has emerged as a promising complement to conventional imaging modalities, offering distinct advantages such as real-time dynamic imaging, cost-effectiveness, and the absence of ionizing radiation. By enhancing the visualization of tumor vascularization, CEUS enables differentiation between benign and malignant pulmonary nodules while providing valuable insights into tumor angiogenesis, a hallmark of malignancy, and therapeutic response. Additionally, CEUS demonstrates utility in assessing regional lymph nodes, detecting distant metastases, and analyzing blood flow dynamics through quantitative methods such as time-intensity curve analysis. Despite these benefits, certain limitations persist, including reduced efficacy in imaging deep-seated lesions, variability due to patient-specific physiological factors, and dependency on operator expertise. However, advancements in targeted contrast agents, integration with multimodal imaging techniques, and the application of artificial intelligence hold significant potential to address these challenges. This review systematically evaluates the clinical applications, advantages, and limitations of CEUS in LC diagnosis, providing a comprehensive understanding of its role in modern precision oncology. Furthermore, it highlights future research directions aimed at enhancing diagnostic accuracy, improving clinical workflows, and expanding the adoption of CEUS in routine practice.

Positron Emission Tomography-Magnetic Resonance Imaging, a New Hybrid Imaging Modality for Dentomaxillofacial Malignancies-A Systematic Review

Background/Objectives: Emerging hybrid imaging modalities, like Positron Emission Tomography/Computed Tomography (PET/CT) and Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI), are useful for assessing head and neck cancer (HNC) and its prognosis during follow-up. PET/MRI systems enable simultaneous PET and MRI scans within a single session. These combined PET/MRI scanners merge MRI's better soft tissue contrast and the molecular metabolic information offered by PET. Aim: To review scientific articles on the use of hybrid PET/MRI techniques in diagnosing dentomaxillofacial malignancies. Method: The available literature on the use of PET/MRI for the diagnosis of dentomaxillofacial malignancies in four online databases (Scopus, PubMed, Web of Science, and the Cochrane Library) was searched. Eligible for this review were original full-text articles on PET/MRI imaging, published between January 2010 and November 2024, based on experimental or clinical research involving humans. Results: Out of the 783 articles retrieved, only twelve articles were included in this systematic review. Nearly half of the articles (5 out of 12) concluded that PET/MRI is superior to PET, MRI, and PET/CT imaging in relation to defining malignancies' size. Six articles found no statistically significant results and the diagnostic accuracy presented was similar in PET/MRI versus MRI and PET/CT images. Regarding the overall risk of bias, most articles had a moderate risk. Conclusions: The use of PET/MRI in HNC cases provides a more accurate diagnosis regarding dimensions of the tumor and thus a more accurate surgical approach if needed. Further prospective studies on a larger cohort of patients are required to obtain more accurate results on the application of hybrid PET/MRI.

3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation

OBJECTIVES

Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.

METHODS

This retrospective study was performed on 456 participants respectively scanned by three different PET scanners with two different tracers. A DL method called spatially aware noise reduction network (SANR) was proposed to recover 3D full-dose (FD) PET volumes from LD data. The performance of SANR was compared with a 2D DL method taking regular FD PET volumes as the reference. Wilcoxon signed-rank test was conducted to compare the image quality metrics across different DL denoising methods. For clinical evaluation, two nuclear medicine physicians examined the recovered FD PET volumes using a 5-point grading scheme (5 = excellent) and gave a binary decision (negative or positive) for diagnostic quality assessment.

RESULTS

Statistically significant differences (p < 0.05) were found in terms of image quality metrics when SANR was compared with the 2D DL method. For clinical evaluation, SANR achieved a lesion detection accuracy of 95.3% (95% CI: 90.1%, 100%), while the reference full-dose PET volumes obtained a lesion detection accuracy of 98.4% (95% CI: 95.4%, 100%). In Alzheimer's disease diagnosis, both the reference FD PET volumes and the FD PET volumes recovered by SANR exhibited the same accuracy.

CONCLUSION

Compared with reference FD PET, LD PET denoised by the proposed approach significantly reduced radiotracer dosage and showed noninferior diagnostic performance in brain lesion detection and Alzheimer's disease diagnosis.

KEY POINTS

Question The current trend in PET imaging is to reduce injected dosage, which leads to low-quality PET images and reduces diagnostic efficacy. Findings The proposed deep learning method could recover diagnostic quality PET images from data acquired with a markedly reduced radiotracer dosage. Clinical relevance The proposed method would enhance the utility of PET scanning at lower radiotracer dosage and inform future workflows for brain lesion detection and Alzheimer's disease diagnosis, especially for those patients who need multiple examinations.

Role of 18 FDG PET/CT in Detecting Primary Tumors in Patients with Carcinoma of Unknown Primary: Single-Center Cross-Sectional Study from 2017 to 2023 (Extension Study)

Background  Carcinoma of unknown primary (CUP) is a diverse group of cancers in which the primary tumor site remains occult despite detailed investigations. This is an extension of a published parent study with a smaller cohort, to further validate the published facts of detection efficiency of 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 FDG PET/CT) in patients with CUP over a larger sample from 2017 to 2023. Methods  Patients with CUP referred for 18 FDG PET/CT scan for detection of primary sites during the study period were recruited. 18 FDG PET/CT scan was acquired using a standardized protocol, and patients with suspected primary sites underwent biopsies. Scan findings and biopsy results were analyzed to find the detection rate, sensitivity, area under the curve (AUC), and positive predictive value (PPV). As no biopsy was performed in cases with negative scan, these cases were considered false negatives (FNs). Results  Total 230 patients with CUP were included with similar demographic trend (mean age: 58 ± 14 years; 63% male and 37% female; mean body mass index: 26.82 ± 5.4 kg/m 2 ); 138/230 (60 vs. 74% in parent study) patients were found to have a hypermetabolic focus suggestive of primary tumor sites and subjected to biopsy which turned out positive in 127/138 (true positive [TP]: 92 vs. 76% in parent study) and negative in 11/138 (true negative [TN]: 8 vs. 24% in parent study). Sensitivity and PPV of 18 FDG PET/CT were 58 and 92%, respectively (68 and 76%, respectively, in parent study). The remaining 92/230 (40%) patients with negative 18 FDG PET/CT for primary focus did not have biopsy. No significant demographic difference was seen in patients with TP and FN studies ( p  > 0.05). Receiver operating characteristics (ROC) curve revealed fair diagnostic strength of 18 FDG PET/CT for detecting unknown primary (AUC 0.710; p ≤ 0.05; standard error = 0.0167; confidence interval: 0.647-0.768; vs. nonsignificant in parent study). Conclusion  We conclude that this extension study with a larger cohort compared with the parent study has found a similar detection efficiency of 18 FDG PET/CT for identifying primary tumor in patients with CUP (58 vs. 57%) but with better PPV and sensitivity. Upfront use of 18 FDG PET/CT in CUP could preclude the use of many futile diagnostic procedures. Furthermore, the use of tumor-specific PET tracers, higher resolution scanners, and acquiring delayed images in patients with negative 18 FDG study could reduce FN results in patients with CUP.

18F-FDG PET/CT in the Preoperative Diagnostic and Staging of Lung Cancer and as a Predictor of Lymph Node Involvement

Background/Objectives: The aim of this study was to evaluate the efficacy and accuracy of PET imaging and performance in defining the preoperative TNM classification, especially the intrathoracic lymph node staging, of patients with lung cancer. Methods: A retrospective, single-institution study of consecutive patients with surgical therapy of lung cancer that were undergoing preoperative PET/CT scanning at the same center was conducted. A total of 104 patients were included. All patients underwent surgical evaluation with mediastinal and hilar lymph node sampling. Five patients with preoperative suspicion of N3 nodal status who were only tested for N2 were excluded from the observations and analyses of nodal status. Results: PET/CT staged the nodal status correctly in 85 out of 99 patients (85.9%); overstaging occurred in 7 patients (7.1%) and understaging in 7 patients (7.1%). The overall prevalence of lymph node metastases was 42.3%. When preoperative T classification was compared with postoperative histopathological T classification, 75% patients were correctly staged, 13.5% were overstaged, and 11.5% were understaged by PET/CT. In univariate analysis, lymph node involvement was significantly associated (p < 0.05) with the following primary tumor characteristics: increasing diameter (>35 mm), a maximum standardized uptake value > 9.5, and higher grading. The tumor diameter and the degree of differentiation were found to be factors influencing the SUVmax of the primary tumor as well. Conclusions: Our data show that integrated PET/CT provides high accuracy in the intrathoracic nodal staging and tumor expansion of lung cancer patients and emphasizes the continued need for surgical staging.

18F-FDG PET/CT assessment of metabolic tumor burden predicts survival in patients with metastatic posterior uveal melanoma

The prognostic value of metabolic tumor burden parameters obtained from 18F-FDG PET/CT imaging was evaluated in this retrospective national multicenter study of patients with metastatic posterior uveal melanoma (PUM) and compared to the largest diameter of the largest metastatic lesion (LDLM) and the American Joint Committee on Cancer (AJCC) staging system. The Maximal Standard Uptake Value (SUVmax), Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG) were obtained in 106 patients. Higher values of SUVmax (p = 0.007, log-rank), MTV (p < 0.001, log-rank), and TLG (p < 0.001, long-rank) were associated with shorter survival. The three parameters were also independent predictors in the multivariate Cox model, while the AJCC staging turned insignificant. Time-dependent positive predictive value (PPV) analysis and Receiver Operating Characteristics (ROC) curves showed that MTV (Area Under the Curve (AUC) = 0.78), TLG (AUC = 0.78), and LDLM (AUC = 0.76) were good predictors of 1-year survival. For the subset of 97 patients with liver metastases, the corresponding regional measurements in the liver tended to be even better predictors. In conclusion, MTV and TLG were found to be better predictors of survival in metastatic PUM than the AJCC staging system, but when LDLM was used as a continuous variable it showed an equally good prediction of 1-year survival.

A comparative study of preclinical and clinical molecular imaging response to EGFR inhibition using osimertinib in glioblastoma

Background

To demonstrate the potential value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) as a rapid, non-invasive metabolic imaging surrogate for pharmacological modulation of EGFR signaling in EGFR-driven GBM, we synchronously conducted a preclinical imaging study using patient-derived orthotopic xenograft (PDOX) models and validated it in a phase II molecular imaging study in recurrent GBM (rGBM) patients using osimertinib.

Methods

A GBM PDOX mouse model study was performed concurrently with an open-label, single-arm, single-center, phase II study of osimertinib (NCT03732352) that enrolled 12 patients with rGBM with EGFR alterations. Patients received osimertinib daily and 3 18F-FDG PET scans: two 24 h apart prior to dosing, and one 48 h after dosing.

Results

GBM PDOX models suggest osimertinib has limited impact on both 18F-FDG uptake (+ 9.8%-+25.9%) and survival (+ 15.5%; P = .01), which may be explained by insufficient exposure in the brain (Kpuu: 0.30) required to robustly inhibit the EGFR alterations found in GBM. Treatment with osimertinib had subtle, but measurable decreases in the linear rate of change of 18F-FDG nSUV growth rate averaging -4.5% per day (P = .01) and change in 18F-FDG uptake was correlated with change in tumor growth rate (R2 = 0.4719, P = .0195). No metabolic (PERCIST) or radiographic (RANO) responses were seen, and no improvements in PFS or OS were observed.

Conclusions

This study demonstrated the feasibility of using FDG PET as a clinically reliable imaging biomarker for assessing EGFR inhibition in GBM, while revealing osimertinib's limited impact on both metabolic activity and tumor growth in GBM, findings that were concordant between preclinical and clinical observations.

A [18F]FDG PET based nomogram to predict cancer-associated cachexia and survival outcome: A multi-center study

OBJECTIVES

Cancer patients with cachexia face poor prognosis and shortened survival. Early diagnosis and accurate prognosis prediction remain challenging. This multi-center study aims to develop and externally validate a nomogram integrating [18F]fluoro-2-deoxy-D-glucose ([18F]FDG) PET findings and routine clinical biochemistry tests for predicting cancer-associated cachexia, while also assessing its potential prognostic value.

RESEARCH METHODS & PROCEDURES

A retrospective analysis of 658 cancer patients (390 in the development cohort, 268 in the validation cohort) utilized [18F]FDG PET/CT data from two centers. Logistic regression identified organ-specific standardized uptake values (SUVs) and clinical variables associated with cancer-associated cachexia. Diagnostic accuracy, discriminative ability, and clinical effectiveness were assessed using area under the curve (AUC), calibration curve, and decision curve. Nomogram predictability for overall survival was evaluated through Cox regression and Kaplan-Meier curves.

RESULTS

The combined nomogram incorporating age (odds ratio [OR] = 1.893; P = 0.012), hemoglobin (OR = 2.591; P < 0.001), maximum SUV of the liver (OR = 3.646; P < 0.001), and minimum SUV of the subcutaneous fat (OR = 5.060; P < 0.001) achieved good performance in predicting cancer-associated cachexia (AUC = 0.807/0.726, development/validation). Calibration and decision curve analyses confirmed its clinical effectiveness. Kaplan-Meier curves analysis showed that overall survival can be categorized using the combined nomogram (P < 0.001).

CONCLUSION

Combining radiological information from clinical standard [18F]FDG PET data from cancer patients with biochemical results in their routine clinical blood tests through a well-constructed nomogram enables predicting cachexia and its effect on the prognosis of cancer patients.

Therapeutic effect of novel drug candidate, PRG-N-01, on NF2 syndrome-related tumor

BACKGROUND

NF2-related schwannomatosis (NF2-SWN) is associated with multiple benign tumors in the nervous system. NF2-SWN, caused by mutations in the NF2 gene, has developed into intracranial and spinal schwannomas. Because of the high surgical risk and frequent recurrence of multiple tumors, targeted therapy is necessary. However, there are no approved drugs.

METHODS

We examined the action mechanism of PRG-N-01, a candidate molecule for NF2-SWN, through the direct binding assay and mass spectrometry. For in vitro anti-proliferative experiments, primary cells derived from NF2 mouse model and patient tumors, were treated with PRG-N-01. The in vivo therapeutic and preventive efficacy was validated via intraperitoneal and oral administration in NF2 mouse model (Postn-Cre; Nf2f/f). Gene expression profile in the DRG of mouse model was explored by RNA sequencing. The pharmacological properties of PRG-N-01 were analyzed through the preclinical study.

RESULTS

PRG-N-01 binds to the N-terminal extremity of TGFβR1 (TβR1) kinase domain, where TβR1 and RKIP interact, inhibiting the binding and preventing degradation of RKIP. In vivo administration in the mouse model suppressed schwannoma progression in the DRG. Early oral administration of the PRG-N-01 also demonstrated preventive effects on NF2-SWN. PRG-N-01 treatment suppressed tumor growth genes while upregulating genes related to for normal cell metabolism and schwann cell differentiation in DRG. PRG-N-01 showed druggable properties through the preclinical study including ADME, pharmacodynamics, pharmacokinetics and toxicology.

CONCLUSIONS

Together, our study provides the rationale and critical data for a prospective clinical trial of PRG-N-01 in NF2-SWN patients indicating PRG-N-01 as a promising candidate for the treatment.

Mitochondria and Cancer

Mitochondria are semiautonomous organelles with diverse metabolic and cellular functions including anabolism and energy production through oxidative phosphorylation. Following the pioneering observations of Otto Warburg nearly a century ago, an immense body of work has examined the role of mitochondria in cancer pathogenesis and progression. Here, we summarize the current state of the field, which has coalesced around the position that functional mitochondria are required for cancer cell proliferation. In this review, we discuss how mitochondria influence tumorigenesis by impacting anabolism, intracellular signaling, and the tumor microenvironment. Consistent with their critical functions in tumor formation, mitochondria have become an attractive target for cancer therapy. We provide a comprehensive update on the numerous therapeutic modalities targeting the mitochondria of cancer cells making their way through clinical trials.

Subcutaneous adipose tissue radiodensity: An emerging risk factor for severe COVID-19

BACKGROUND

Adipose tissue radiodensity and metabolic activity may influence COVID-19 outcomes. This study evaluated the association between adipose tissue characteristics and clinical outcomes in COVID-19 patients.

METHODS

Two retrospective cohorts of hospitalized COVID-19 patients were analyzed. Subcutaneous adipose tissue radiodensity (SATR) and visceral adipose tissue radiodensity were assessed by computed tomography. Fluorine-18-labelled fluorodeoxyglucose PET/computed tomography measured adipose tissue metabolic activity. Associations with mortality, length of stay, ventilation requirement, and complications were examined using regression analyses.

RESULTS

High SATR was independently associated with increased mortality risk (OR: 2.70; P = 0.033), longer hospitalization (P < 0.001), higher rates of mechanical ventilation (P = 0.007), and complications: acute kidney injury (P = 0.001), secondary infection (P = 0.007), shock (P = 0.010), and pulmonary embolism (P = 0.011). SATR positively correlated with SAT glucose uptake (ρ = 0.52) and negatively with leptin levels (ρ = -0.48).

CONCLUSIONS

Elevated SATR at COVID-19 diagnosis predicts disease severity and worse outcomes. SATR is a potential prognostic biomarker for acute and chronic inflammatory conditions.

Dynamic FDG PET/CT for differentiating focal pelvic uptake in patients with gynecological cancer

This study aimed to evaluate the ability of serial whole-body dynamic PET/CT to differentiate physiological from abnormal 18F-FDG uptake in the abdomen and pelvis of gynecological cancer patients. We conducted a retrospective study of 61 18F-FDG PET/CT examinations for suspected gynecological malignancies or metastases between March 2018 and January 2020. Our protocol included four-phase dynamic whole-body scans. High-uptake foci with SUVmax > 2.5 in the abdominopelvic region caudal to the renal portal were picked up and visually evaluated as "changed" (disappeared during any phase or morphological changes in more than half of the foci) or "unchanged" in motion on the serial dynamic images. Focal 18F-FDG uptake was observed in 84 foci. Of the 58 foci determined pathologically or clinically to have pathological uptake, no change was observed on serial dynamic imaging in 54 foci (sensitivity, 93%). Of the 26 foci of physiological uptake, temporal changes in uptake were observed in 20 foci using dynamic imaging (specificity, 77%). The positive and negative predictive values were 90% and 83%, respectively, with an accuracy of 88%. Dynamic whole-body 18F-FDG PET/CT imaging allows for differentiation between pathological and physiological uptake in the abdominopelvic region of patients with gynecological cancer.

AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics

This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making.

Methods

We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test.

Results

The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p < 0.05).

Conclusions

The DenseNet-264 model significantly outperforms the radiomics model in predicting bone metastasis in lung cancer patients. The early and accurate prediction provided by the deep learning model can facilitate timely interventions and personalized treatment planning, potentially improving patient outcomes. Future studies should focus on validating these findings in larger, multi-center cohorts and integrating clinical data to further enhance predictive accuracy.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Dose estimation in patients from different protocols of 18F-FDG PET/CT studies and analysis of optimization strategies

This study aimed to evaluate the dose in different protocols of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) procedure. The retrospective study involves 207 patients with confirmed malignancies who underwent PET/CT. Effective dose (E) from PET was estimated based on injected activity and dose coefficient as per International Commission on Radiation Protection (ICRP) 128. Estimation of E from CT was done utilizing the dose length product (DLP) method and conversion factors as per ICRP 102. There was a significant statistical difference observed in E between different PET/CT protocols (P < .001). E of PET in the whole body (WB) was found to be 4.9 ± 0.9 mSv, whereas mean volume computed tomography dose indexvol, DLP, and E of CT in WB were 7.0 ± 0.2 mGy, 674.3 ± 80.7 mGy.cm, and 10.1 ± 1.2 mSv, respectively. No linear correlation was seen between the size-specific dose estimate and E of CT (r = -0.003; P = .978). The total mean E in WB PET/CT was 17.0 ± 1.7 mSv. CT dose was contributing more than PET dose in all protocols except brain PET/CT. Optimization strategies can be evaluated only if monitored periodically.

Refining penalty parameter selection in whole-body PET image reconstruction for lung cancer patients using the cross-validation log-likelihood method

Objective.Penalty parameters in penalized likelihood positron emission tomography (PET) reconstruction are typically determined empirically. The cross-validation log-likelihood (CVLL) method has been introduced to optimize these parameters by maximizing a CVLL function, which assesses the likelihood of reconstructed images using one subset of a list-mode dataset based on another subset. This study aims to validate the efficacy of the CVLL method in whole-body imaging for cancer patients using a conventional clinical PET scanner.Approach.Fifteen lung cancer patients were injected with 243.7 ± 23.8 MBq of [18F]FDG and underwent a 22 min PET scan on a Biograph mCT PET/CT scanner, starting at 60 ± 5 min post-injection. The PET list-mode data were partitioned by subsampling without replacement, with 20 minutes of data for image reconstruction using an in-house ordered subset expectation maximization algorithm and the remaining 2 minutes of data for cross-validation. Two penalty parameters, penalty strengthβand Fair penalty function parameterδ, were subjected to optimization. Whole-body images were reconstructed, and CVLL values were computed across various penalty parameter combinations. The optimal image corresponding to the maximum CVLL value was selected by a grid search for each patient.Main results.Theδvalue required to maximize the CVLL value was notably small (⩽10-6in this study). The influences of voxel size and scan duration on image optimization were investigated. A correlation analysis revealed a significant inverse relationship between optimalβand scan count level, with a correlation coefficient of -0.68 (p-value = 3.5 × 10-5). The optimal images selected by the CVLL method were compared with those chosen by two radiologists based on their diagnostic preferences. Differences were observed in the selection of optimal images.Significance.This study demonstrates the feasibility of incorporating the CVLL method into routine imaging protocols, potentially allowing for a wide range of combinations of injected radioactivity amounts and scan durations in modern PET imaging.

The impact of PET/CT and brain MRI for metastasis detection among patients with clinical T1-category lung cancer: Findings from a large-scale cohort study

PURPOSE

[18F]-FDG PET/CT and brain MRI are common approaches to detect metastasis in patients of lung cancer. Current guidelines for the use of PET/CT and MRI in clinical T1-category lung cancer lack risk-based stratification and require optimization. This study stratified patients based on metastatic risk in terms of the lesions' size and morphological characteristics.

METHODS

The detection rate of metastasis was measured in different sizes and morphological characteristics (solid and sub-solid) of tumors. To confirm the cut-off value for discriminating metastasis and overall survival (OS) prediction, the receiver operating characteristic (ROC) analysis was performed based on PET/CT metabolic parameters (SUVmax/SUVmean/SULpeak/MTV/TLG), followed by Kaplan-Meier analysis for survival in post-operation patients with and without PET/CT plus MRI.

RESULTS

2,298 patients were included. No metastasis was observed in patients with solid nodules < 8.0 mm and sub-solid nodules < 10.0 mm. The cut-off of PET/CT metabolic parameters on discriminating metastasis were 1.09 (SUVmax), 0.26 (SUVmean), 0.31 (SULpeak), 0.55 (MTV), and 0.81 (TLG), respectively. Patients undergoing PET/CT plus MRI exhibited longer OS compared to those who did not receive it in solid nodules ≥ 8.0 mm & sub-solid nodules ≥ 10.0 mm (HR, 0.44; p < 0.001); in solid nodules ≥ 8.0 mm (HR, 0.12; p<0.001) and in sub-solid nodules ≥ 10.0 mm (HR; 0.61; p=0.075), respectively. Compared to patients with metabolic parameters lower than cut-off values, patients with higher metabolic parameters displayed shorter OS: SUVmax (HR, 12.94; p < 0.001), SUVmean (HR, 11.33; p <0.001), SULpeak (HR, 9.65; p < 0.001), MTV (HR, 9.16; p = 0.031), and TLG (HR, 12.06; p < 0.001).

CONCLUSION

The necessity of PET/CT and MRI should be cautiously evaluated in patients with solid nodules < 8.0 mm and sub-solid nodules < 10.0 mm, however, these examinations remained essential and beneficial for patients with solid nodules ≥ 8.0 mm and sub-solid nodules ≥ 10.0 mm.

Learning CT-free attenuation-corrected total-body PET images through deep learning

OBJECTIVES

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning.

METHODS

Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements.

RESULTS

The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images.

CONCLUSION

Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices.

CLINICAL RELEVANCE STATEMENT

The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up.

KEY POINTS

• CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

Current Role of PET CT in Staging and Management of Penile Cancers

Penile cancer (PeCa) is a rare urological malignancy characterized by significant geographical variations in both incidence and mortality rates. Due to its rarity and the consequent lack of randomized trials, current management is based on retrospective studies and small prospective trials. In addition, both the diagnostic pathways and treatment strategies exhibit substantial heterogeneity, differing significantly between less-developed and well-developed countries. The prognosis of PeCas is determined by the presence and extent of regional lymph node (LN) involvement. Therefore, the early detection and treatment of LN metastasis is paramount to ensure better outcomes. In recent decades, overall survival of PeCas has increased, mainly due to advancements in imaging techniques and risk stratification. We aim to provide an overview of the current role of PET CT imaging in the management of patients with PeCa.

Atypical 18F-FDG PET-CT Findings in a Rare Case of Primary Hepatic Leiomyosarcoma

The primary hepatic leiomyosarcoma is a rare malignant tumor arising from the smooth muscle cells in the hepatic vessels, bile ducts, and ligamentum teres. It is considered a subtype of hepatic sarcomas. We report awkward 18F-FDG PET-CT findings of a primary hepatic leiomyosarcoma masquerading as a benign hepatic tumor, which were confirmed by histopathological and immunohistochemical examinations in a 78-year-old woman.

Validation of cardiac image-derived input functions for functional PET quantification

PURPOSE

Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations.

METHODS

Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard.

RESULTS

For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements.

CONCLUSION

Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Tumor-associated macrophages drive glycolysis through the IL-8/STAT3/GLUT3 signaling pathway in pancreatic cancer progression

Glycolytic metabolism is a hallmark of pancreatic ductal adenocarcinoma (PDAC), and tumor-associated stromal cells play important roles in tumor metabolism. We previously reported that tumor-associated macrophages (TAMs) facilitate PDAC progression. However, little is known about whether TAMs are involved in regulating glycolysis in PDAC. Here, we found a positive correlation between CD68+ TAM infiltration and FDG maximal standardized uptake (FDG SUVmax) on PET-CT images of PDAC. We discovered that the glycolytic gene set was prominently enriched in the high TAM infiltration group through Gene Set Enrichment Analysis using The Cancer Genome Atlas database. Mechanistically, TAMs secreted IL-8 to promote GLUT3 expression in PDAC cells, enhancing tumor glycolysis both in vitro and in vivo, whereas this effect could be blocked by the IL-8 receptor inhibitor reparixin. Furthermore, IL-8 promoted the translocation of phosphorylated STAT3 into the nucleus to activate the GLUT3 promoter. Overall, we demonstrated that TAMs boosted PDAC cell glycolysis through the IL-8/STAT3/GLUT3 signaling pathway. Our cumulative findings suggest that the abrogation of TAM-induced tumor glycolysis by reparixin might exhibit an antitumor impact and offer a potential therapeutic target for PDAC.

Improvement of motion artifacts using dynamic whole-body 18F-FDG PET/CT imaging

PURPOSE

Serial dynamic whole-body PET imaging is valuable for assessing serial changes in tracer uptake. The purpose of this study was to evaluate the improvement of motion artifacts in patients using serial dynamic whole-body 18F-fluorodeoxyglyucose (FDG) PET/CT imaging.

MATERIALS AND METHODS

In 797 consecutive patients, serial 3-min dynamic whole-body FDG PET imaging was performed seven times, at 60 or 90 min after FDG administration. In cases with large body motion during imaging, we tried to improve the images by summing the images before body motion. An image quality study was performed on another 50 patients without obvious body motion using the same acquisition mode.

RESULTS

Obvious body movement was observed in 106 of 797 cases (13.3%), and severe motion artifacts which interfered image interpretation were observed in 18 (2.3%). In these 18 cases, summation of the images before the body movement enabled us to obtain images that excluded the effect of the body motion. In the visual evaluation of the image quality in another 50 patients studied, acceptable image quality was obtained when 2 or more times the serial 3-min image data were added.

CONCLUSION

Serial dynamic whole-body FDG PET imaging can minimize body motion artifacts by summation of the images before the body motion. Such serial dynamic study may be a choice for PET imaging to eliminate motion artifacts.

Positron Emission Tomography/Computed Tomography Transformation of Oncology: Melanoma and Skin Malignancies

Skin cancers are the most common cancers, with melanoma resulting in the highest cause of death in this category. Accurate clinical, histologic, and imaging staging with fludeoxyglucose positron emission tomography (FDG PET) is most important to guide patient management. Whilst surgical excision with clear margins is the gold-standard treatment for primary cutaneous melanoma, targeted therapies have generated remarkable and rapid clinical responses in melanoma, for which FDG PET also plays an important role in assessment of treatment response and post-therapy surveillance. Non-FDG PET tracers, advanced PET technology, and PET radiomics may potentially change the landscape of the utilization of PET in the imaging of patients with cutaneous malignancies.

OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

Radionuclide-Labeled Antisilencing Function 1a Inhibitory Peptides for Tumor Identification and Individualized Therapy

Immune checkpoint blockade (ICB) therapy is promising to revolutionize cancer regimens, but the low response rate and the lack of a suitable patient stratification method have impeded universal profit to cancer patients. Noninvasive positron emission tomography (PET) imaging in the whole body, upon coupling with specific biomarkers closely related to the immune response, could provide spatiotemporal information to prescribe cancer therapy. Herein, we demonstrate that antisilencing function 1a (ASF1a) could serve as a biomarker target to delineate tumor immune microenvironments by immune PET (iPET). The iPET radiotracer (68Ga-AP1) is designed to target ASF1a in tumors and predict immune response, and the signal intensity predicts anti-PD-1 (αPD-1) therapy response in a negative correlation manner. The ICB-resistant tumors with a high level of ASF1a as revealed by iPET (ASF1aHigh-iPET) are prescribed to be treated by either the combined 177Lu-labeled AP1 and αPD-1 or the standalone α particle-emitting 225Ac-labeled AP1, both achieving enhanced therapeutic efficacy and prolonged survival time. Our study not only replenishes the iPET arsenal for immune-related response evaluation by designing a reliable biomarker and a facile radiotracer but also provides optional therapeutic strategies for ICB-resistant tumors with versatile radionuclide-labeled AP1 peptides, which is promising for real-time clinical diagnosis and individualized therapy planning simultaneously.

Evaluating immunotherapeutic outcomes in triple-negative breast cancer with a cholesterol radiotracer in mice

Evaluating the response to immune checkpoint inhibitors (ICIs) remains an unmet challenge in triple-negative breast cancer (TNBC). The requirement for cholesterol in the activation and function of T cells led us to hypothesize that quantifying cellular accumulation of this molecule could distinguish successful from ineffective checkpoint immunotherapy. To analyze accumulation of cholesterol by T cells in the immune microenvironment of breast cancer, we leveraged the PET radiotracer, eFNP-59. eFNP-59 is an analog of cholesterol that our group validated as an imaging biomarker for cholesterol uptake in preclinical models and initial human studies. In immunocompetent mouse models of TNBC, we found that elevated uptake of exogenous labeled cholesterol analogs functions as a marker for T cell activation. When comparing ICI-responsive and -nonresponsive tumors directly, uptake of fluorescent cholesterol and eFNP-59 increased in T cells from ICI-responsive tumors. We discovered that accumulation of cholesterol by T cells increased in ICI-responding tumors that received anti-PD-1 checkpoint immunotherapy. In patients with TNBC, tumors containing cycling T cells had features of cholesterol uptake and trafficking within those populations. These results suggest that uptake of exogenous cholesterol analogs by tumor-infiltrating T cells allows detection of T cell activation and has potential to assess the success of ICI therapy.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Nuclear Medicine and Molecular Imaging Applications in Gynecologic Malignancies: A Comprehensive Review

Gynecologic malignancies, consisting of endometrial, cervical, ovarian, vulvar, and vaginal cancers, pose significant diagnostic and management challenges due to their complex anatomic location and potential for rapid progression. These tumors cause substantial morbidity and mortality, often because of their delayed diagnosis and treatment. An estimated 19% of newly diagnosed cancers among women are gynecologic in origin. In recent years, there has been growing evidence supporting the integration of nuclear medicine imaging modalities in the diagnostic work-up and management of gynecologic cancers. The sensitivity of fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) combined with the anatomical specificity of computed tomography (CT) and magnetic resonance imaging (MRI) allows for the hybrid evaluation of metabolic activity and structural abnormalities that has become an indispensable tool in oncologic imaging. Lymphoscintigraphy, using technetium 99m (99mTc) based radiotracers along with single photon emission computed tomography/ computed tomography (SPECT/CT), holds a vital role in the identification of sentinel lymph nodes to minimize the surgical morbidity from extensive lymph node dissections. While not yet standard for gynecologic malignancies, promising therapeutic nuclear medicine agents serve as specialized treatment options for patients with advanced or recurrent disease. This article aims to provide a comprehensive review on the nuclear medicine applications in gynecologic malignancies through the following objectives: 1) To describe the role of nuclear medicine in the initial staging, lymph node mapping, response assessment, and recurrence/surveillance imaging of common gynecologic cancers, 2) To review the limitations of 18F-FDG PET/CT and promising applications of 18F-FDG PET/MRI in gynecologic malignancy, 3) To underscore the promising theragnostic applications of nuclear medicine, 4) To highlight the current role of nuclear medicine imaging in gynecologic cancers as per the National Comprehensive Cancer Network (NCCN), European Society of Surgical Oncology (ESGO), and European Society of Medical Oncology (ESMO) guidelines.

Carbon-13 Radiofrequency Amplification by Stimulated Emission of Radiation of the Hyperpolarized Ketone and Hemiketal Forms of Allyl [1-13C]Pyruvate

13C hyperpolarized pyruvate is an emerging MRI contrast agent for sensing molecular events in cancer and other diseases with aberrant metabolic pathways. This metabolic contrast agent can be produced via several hyperpolarization techniques. Despite remarkable success in research settings, widespread clinical adoption faces substantial roadblocks because the current sensing technology utilized to sense this contrast agent requires the excitation of 13C nuclear spins that also need to be synchronized with MRI field gradient pulses. Here, we demonstrate sensing of hyperpolarized allyl [1-13C]pyruvate via the stimulated emission of radiation that mitigates the requirements currently blocking broader adoption. Specifically, 13C Radiofrequency Amplification by Stimulated Emission of Radiation (13C RASER) was obtained after pairwise addition of parahydrogen to a pyruvate precursor, detected in a commercial inductive detector with a quality factor (Q) of 32 for sample concentrations as low as 0.125 M with 13C polarization of 4%. Moreover, parahydrogen-induced polarization allowed for the preparation of a mixture of ketone and hemiketal forms of hyperpolarized allyl [1-13C]pyruvate, which are separated by 10 ppm in 13C NMR spectra. This is a good model system to study the simultaneous 13C RASER signals of multiple 13C species. This system models the metabolic production of hyperpolarized [1-13C]lactate from hyperpolarized [1-13C]pyruvate, which has a similar chemical shift difference. Our results show that 13C RASER signals can be obtained from both species simultaneously when the emission threshold is exceeded for both species. On the other hand, when the emission threshold is exceeded only for one of the hyperpolarized species, 13C stimulated emission is confined to this species only, therefore enabling the background-free detection of individual hyperpolarized 13C signals. The reported results pave the way to novel sensing approaches of 13C hyperpolarized pyruvate, potentially unlocking hyperpolarized 13C MRI on virtually any MRI system─an attractive vision for the future molecular imaging and diagnostics.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Differentiation of cancer of unknown primary and lymphoma in head and neck metastatic poorly differentiated cancer using 18 F-FDG PET/CT tumor metabolic heterogeneity index

OBJECTIVE

To explore the value of 18 F-FDG PET/CT tumor metabolic heterogeneity index (HI) and establish and validate a nomogram model for distinguishing head and neck cancer of unknown primary (HNCUP) from lymphoma with head and neck metastatic poorly differentiated cancer.

METHODS

This retrospective analysis was conducted on 1242 patients with cervical metastatic poorly differentiated cancer. 108 patients, who were clinically and pathologically confirmed as HNCUP or lymphoma, were finally enrolled. Two independent sample t-tests and χ 2 test were used to compare the clinical and imaging features. Binary logistic regression was used to screen for independent predictive factors.

RESULTS

Among the 108 patients), 65 patients were diagnosed with HNCUP and 43 were lymphoma. Gender ( P  = 0.001), SUV max ( P  < 0.001), SUV mean ( P  < 0.001), TLG ( P  = 0.012), and HI ( P  < 0.001) had statistical significance in distinguishing HNCUP and lymphoma. Female ( OR  = 4.546, P  = 0.003) and patients with HI ≥ 2.37 ( OR  = 3.461, P  = 0.047) were more likely to be diagnosed as lymphoma.

CONCLUSION

For patients with cervical metastatic poorly differentiated cancer, gender and HI were independent predictors of pathological type. For such patients, clinical attention should be paid to avoid misdiagnosing lymphoma as HNCUP, which may delay treatment.

Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning

OBJECTIVE

To improve the PET image quality by a deep progressive learning (DPL) reconstruction algorithm and evaluate the DPL performance in lesion quantification.

METHODS

We reconstructed PET images from 48 oncological patients using ordered subset expectation maximization (OSEM) and deep progressive learning (DPL) methods. The patients were enrolled into three overlapped studies: 11 patients for image quality assessment (study 1), 34 patients for sub-centimeter lesion quantification (study 2), and 28 patients for imaging of overweight or obese individuals (study 3). In study 1, we evaluated the image quality visually based on four criteria: overall score, image sharpness, image noise, and diagnostic confidence. We also measured the image quality quantitatively using the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), contrast-to-background ratio (CBR), and contrast-to-noise ratio (CNR). To evaluate the performance of the DPL algorithm in quantifying lesions, we compared the maximum standardized uptake values (SUVmax), SBR, CBR, SNR and CNR of 63 sub-centimeter lesions in study 2 and 44 lesions in study 3.

RESULTS

DPL produced better PET image quality than OSEM did based on the visual evaluation methods when the acquisition time was 0.5, 1.0 and 1.5 min/bed. However, no discernible differences were found between the two methods when the acquisition time was 2.0, 2.5 and 3.0 min/bed. Quantitative results showed that DPL had significantly higher values of SBR, CBR, SNR, and CNR than OSEM did for each acquisition time. For sub-centimeter lesion quantification, the SUVmax, SBR, CBR, SNR, and CNR of DPL were significantly enhanced, compared with OSEM. Similarly, for lesion quantification in overweight and obese patients, DPL significantly increased these parameters compared with OSEM.

CONCLUSION

The DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.

Effects of isoflurane anaesthesia depth and duration on renal function measured with [99mTc]Tc-mercaptoacetyltriglycine SPECT in mice

BACKGROUND

The influence of anaesthetic depth and the potential influence of different anaesthetic beds and thus different handling procedures were investigated in 86 severe combined immunodeficient (SCID) mice using semi-stationary dynamic single photon emission computed tomography (SPECT) for kidney scintigraphy. Therefore, isoflurane concentrations were adjusted using respiratory rate for low (80-90 breath/min) and deep anaesthesia (40-45 breath/min). At low anaesthesia, we additionally tested the influence of single bed versus 3-mouse bed hotel; the hotel mice were anaesthetized consecutively at ~ 30, 20, and 10 min before tracer injections for positions 1, 2, and 3, respectively. Intravenous [99mTc]Tc-MAG3 injection of ~ 28 MBq was performed after SPECT start. Time-activity curves were used to calculate time-to-peak (Tmax), T50 (50% clearance) and T25 (75% clearance).

RESULTS

Low and deep anaesthesia corresponded to median isoflurane concentrations of 1.3% and 1.5%, respectively, with no significant differences in heart rate (p = 0.74). Low anaesthesia resulted in shorter aortic blood clearance half-life (p = 0.091) and increased relative renal tracer influx rate (p = 0.018). A tendency toward earlier Tmax occurred under low anaesthesia (p = 0.063) with no differences in T50 (p = 0.40) and T25 (p = 0.24). Variance increased with deep anaesthesia. Compared to single mouse scans, hotel mice in position 1 showed a delayed Tmax, T50, and T25 (p < 0.05 each). Furthermore, hotel mice in position 1 showed delayed Tmax versus position 3, and delayed T50 and T25 versus position 2 and 3 (p < 0.05 each). No difference occurred between single bed and positions 2 (p = 1.0) and 3 (p = 1.0).

CONCLUSIONS

Deep anaesthesia and prolonged low anaesthesia should be avoided during renal scintigraphy because they result in prolonged blood clearance half-life, delayed renal influx and/or later Tmax. Vice versa, low anaesthesia with high respiratory rates of 80-90 rpm and short duration (≤ 20 min) should be preferred to obtain representative data with low variance.

M3S-Net: multi-modality multi-branch multi-self-attention network with structure-promoting loss for low-dose PET/CT enhancement

Objective.PET (Positron Emission Tomography) inherently involves radiotracer injections and long scanning time, which raises concerns about the risk of radiation exposure and patient comfort. Reductions in radiotracer dosage and acquisition time can lower the potential risk and improve patient comfort, respectively, but both will also reduce photon counts and hence degrade the image quality. Therefore, it is of interest to improve the quality of low-dose PET images.Approach.A supervised multi-modality deep learning model, named M3S-Net, was proposed to generate standard-dose PET images (60 s per bed position) from low-dose ones (10 s per bed position) and the corresponding CT images. Specifically, we designed a multi-branch convolutional neural network with multi-self-attention mechanisms, which first extracted features from PET and CT images in two separate branches and then fused the features to generate the final generated PET images. Moreover, a novel multi-modality structure-promoting term was proposed in the loss function to learn the anatomical information contained in CT images.Main results.We conducted extensive numerical experiments on real clinical data collected from local hospitals. Compared with state-of-the-art methods, the proposed M3S-Net not only achieved higher objective metrics and better generated tumors, but also performed better in preserving edges and suppressing noise and artifacts.Significance.The experimental results of quantitative metrics and qualitative displays demonstrate that the proposed M3S-Net can generate high-quality PET images from low-dose ones, which are competable to standard-dose PET images. This is valuable in reducing PET acquisition time and has potential applications in dynamic PET imaging.

A new simpler image quality index based on body size for FDG-PET/CT

Noise equivalent count density (NEC density ) is often used to evaluate the image quality of whole-body fluorodeoxyglucose tomography tests. However, this index is calculated using the patient volume, which is difficult to obtain at every facility. In this study, we proposed new image quality indices that can be evaluated at all facilities. In total, 94 patients were enrolled in the study. The correlations of patients' body weight and BMI with volume were examined. New image quality indices normalized by body weight and BMI were defined as NEC bw and NEC bmi , respectively. Correlations between NEC bw , NEC bmi , and NEC density were examined. Further, the correlations between these two new indices and visual scores were evaluated. Good correlations were observed between volume and body weight (r = 0.861, P  < 0.001) and between volume and BMI (r = 0.728, P  < 0.001). NEC bw and NEC bmi correlated well with NEC density (r = 0.954 for NEC bw and r = 0.897 for NEC bmi , P  < 0.001). These correlations improved when the examined bed positions were set to the same number. Additionally, the correlations of visual scores with NEC bw and NEC bmi were similar to those between the visual score and NEC density . Our investigation indicated that the newly proposed image quality metrics, NEC bw and NEC bmi , were easily calculated and as useful as NEC density for evaluating image quality when subjects had similar physiques.

Diagnostic performance of whole-body [18F]FDG PET/MR in cancer M staging: A systematic review and meta-analysis

OBJECTIVES

To calculate the pooled diagnostic performances of whole-body [18F]FDG PET/MR in M staging of [18F]FDG-avid cancer entities.

METHODS

A diagnostic meta-analysis was conducted on the [18F]FDG PET/MR in M staging, including studies: (1) evaluated [18F]FDG PET/MR in detecting distant metastasis; (2) compared[ 18F]FDG PET/MR with histopathology, follow-up, or asynchronous multimodality imaging as the reference standard; (3) provided data for the whole-body evaluation; (4) provided adequate data to calculate the meta-analytic performances. Pooled performances were calculated with their confidence interval. In addition, forest plots, SROC curves, and likelihood ratio scatterplots were drawn. All analyses were performed using STATA 16.

RESULTS

From 52 eligible studies, 2289 patients and 2072 metastases were entered in the meta-analysis. The whole-body pooled sensitivities were 0.95 (95%CI: 0.91-0.97) and 0.97 (95%CI: 0.91-0.99) at the patient and lesion levels, respectively. The pooled specificities were 0.99 (95%CI: 0.97-1.00) and 0.97 (95%CI: 0.90-0.99), respectively. Additionally, subgroup analyses were performed. The calculated pooled sensitivities for lung, gastrointestinal, breast, and gynecological cancers were 0.90, 0.93, 1.00, and 0.97, respectively. The pooled specificities were 1.00, 0.98, 0.97, and 1.00, respectively. Furthermore, the pooled sensitivities for non-small cell lung, colorectal, and cervical cancers were 0.92, 0.96, and 0.86, respectively. The pooled specificities were 1.00, 0.95, and 1.00, respectively.

CONCLUSION

[18F]FDG PET/MR was a highly accurate modality in M staging in the reported [18F]FDG-avid malignancies. The results showed high sensitivity and specificity in each reviewed malignancy type. Thus, our findings may help clinicians and patients to be confident about the performance of [18F]FDG PET/MR in the clinic.

CLINICAL RELEVANCE STATEMENT

Although [18F]FDG PET/MR is not a routine imaging technique in current guidelines, mostly due to its availability and logistic issues, our findings might add to the limited evidence regarding its performance, showing a sensitivity of 0.95 and specificity of 0.97.

KEY POINTS

• The whole-body [18F]FDG PET/MR showed high accuracy in detecting distant metastases at both patient and lesion levels. • The pooled sensitivities were 95% and 97% and pooled specificities were 99% and 97% at patient and lesion levels, respectively. • The results suggested that 18F-FDG PET/MR was a strong modality in the exclusion and confirmation of distant metastases.

Approach to the Patient With Parathyroid Carcinoma

Parathyroid carcinoma (PC) is usually associated with severe symptomatic primary hyperparathyroidism (PHPT) and accounts for less than 1% of all cases of PHPT and approximately 0.005% of all cancers. PC most commonly occurs as a sporadic disease and somatic CDC73 mutations can be detected in up to 80% of cases. Approximately 30% of patients harbor a germline mutation of the CDC73 gene. Preoperative diagnosis of PC is difficult because no disease-specific markers are available, and PC should be suspected in patients with severe hypercalcemia and end-organ complications. The diagnosis is based on the evidence of invasive tumor growth at histology and/or metastases. En bloc resection of the tumor, together with the ipsilateral thyroid lobe and adjacent structures, should be performed by an experienced surgeon when PC is suspected. This surgical approach reduces the risk of recurrence and metastasis and offers the highest chance of cure. Nonetheless, PC has a recurrence rate of 40% to 60% and, if feasible, multiple surgical procedures should be performed. When surgery is no longer an option, medical treatment is aimed to reduce hypercalcemia and target organ complications. Targeted agents have been effectively used in a few cases. We describe herein a patient with severe PHPT due to PC and provide a systematic diagnostic and treatment approach. A thorough review of the medical history, a typical clinical and biochemical phenotype and, in some cases, the revision of the histological examination provide the clues for the diagnosis of PC.

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

PURPOSE

The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs.

METHODS

The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings.

RESULTS

The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45 dB (p < 0.05), 33.77 ± 6.18 dB (p < 0.05), and 38.58 ± 7.28 dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach.

CONCLUSION

The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320 mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Impact of patient characteristic factors on the dynamics of liver glucose metabolism: Evaluation of multiparametric imaging with dynamic whole-body 18 F-fluorodeoxyglucose-positron emission tomography

AIMS

To assess the impact of various patient characteristics on the dynamics of liver glucose metabolism using automated multiparametric imaging with whole-body dynamic 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET).

MATERIALS AND METHODS

We retrospectively enrolled 540 patients who underwent whole-body dynamic FDG-PET. Three quantitative indices representing hepatic glucose metabolism [mean standardized uptake value normalized by lean body mass (SULmean), metabolic glucose rate (kinetic index) and distribution volume (DV)] were measured from multiparametric PET images produced automatically based on the Patlak plot model. Patient characteristics including age, sex, body mass index, fasting time, blood glucose level, and the presence of diabetes mellitus (DM) or hepatic steatosis (HS) were collected. We examined the correlations between the characteristic factors and three quantitative indices using multiple regression analysis.

RESULTS

The success rate of kinetic analysis using multiparametric PET imaging was 93.3% (504/540). Hepatic SULmean was significantly correlated with age (p < .001), sex (p < .001) and blood glucose level (p = .002). DV was significantly correlated with age (p = .033), sex (p < .001), body mass index (p = .002), fasting time (p = .043) and the presence of HS (p = .002). The kinetic index was significantly correlated with age (p < .001) and sex (p = .004). In the comparison of the healthy, DM and HS groups, patients with DM had a significantly increased SULmean, whereas patients with HS had a significantly decreased DV.

CONCLUSIONS

Our results showed that liver glucose metabolism was influenced by various patient characteristic factors. Multiparametric FDG-PET imaging can be used to analyse the kinetics of liver glucose metabolism in routine clinical practice.