Clinical applications of PET in oncology

PET/CT for the Opportunistic Screening of Osteoporosis and Fractures in Cancer Patients

PURPOSE OF REVIEW

In this review, we outline the different etiologies of osteoporosis in the oncologic setting and describe the basis for using PET/CT as screening tool for osteoporosis with a focus on the radiotracers [18F]FDG and [18F]NaF.

RECENT FINDINGS

Osteoporosis is a condition commonly affecting cancer patients due to their age, cancer-specific treatment agents, and effects of cancer. In terms of the unifying mechanism, decreased ratio of osteoblast-bone formation to osteoclast-bone resorption is responsible for causing osteoporosis. PET/CT, a crucial metabolic imaging modality in the oncologic imaging, could be a useful tool for the opportunistic screening of osteoporosis. There are two approaches with which osteoporosis could be identified with PET/CT-using either the (1) CT- based or (2) PET- based approaches. While the CT-based approach has been used with [18F]FDG PET/CT, both CT- and PET-based approaches can be employed with [18F]NaF-PET/CT as [18F]NaF is a radiotracer specific for osteoblast activity.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing

BACKGROUND

The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations.

METHODS

Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV.

RESULTS

Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing.

CONCLUSION

Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

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.

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Focal incidental colorectal fluorodeoxyglucose uptake: Should it be spotlighted?

Fluorine-18 fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) has emerged as a cornerstone in cancer evaluation imaging, with a well-established history spanning several years. This imaging modality, encompassing the examination of the body from the base of the skull to the upper thighs, comprehensively covers the chest and abdominopelvic regions in a singular scan, allowing for a holistic assessment of nearly the entire body, including areas of marginal interest. The inherent advantage of this expansive scan range lies in its potential to unveil unexpected incidental abnormal hypermetabolic areas. The identification of incidental focal FDG uptake within colorectal regions during PET/CT scans is not an uncommon occurrence, albeit fraught with challenges associated with non-specific FDG uptake. The presence of benign colorectal lesions or physiological uptake poses a particular obstacle, as these may manifest with FDG uptake levels that mimic malignancy. Consequently, physicians are confronted with a diagnostic dilemma when encountering abnormal FDG uptake in unexpected colorectal areas. Existing studies have presented divergent results concerning these uptakes. Standardized uptake value and its derivatives have served as pivotal metrics in quantifying FDG uptake in PET images. In this article, we aim to succinctly explore the distinctive characteristics of FDG, delve into imaging findings, and elucidate the clinical significance of incidental focal colorectal uptake. This discussion aims to contribute valuable insights into the nuanced interpretation of such findings, fostering a comprehensive understanding.

Unexpected focal fluorodeoxyglucose uptake in main organs; pass through or pass by?

Since the inception of fluorine-18 fluorodeoxyglucose (F-18 FDG), positron emission tomography/computed tomography (PET/CT) utilizing F-18 FDG has become widely accepted as a valuable imaging modality in the field of oncology, with global prevalence in clinical practice. Given that a single Torso PET/CT scan encompasses the anatomical region from the skull base to the upper thigh, the detection of incidental abnormal focal hypermetabolism in areas of limited clinical interest is both feasible and not uncommon. Numerous investigations have been undertaken to delineate the distinctive features of these findings, yet the outcomes have proven inconclusive. The incongruent results of these studies present a challenge for physicians, leaving them uncertain about the appropriate course of action. This article provides a succinct overview of the characteristics of fluorodeoxyglucose, followed by a comprehensive discussion of the imaging findings and clinical significance associated with incidental focal abnormal F-18 FDG activity in several representative organs. In conclusion, while the prevalence of unrecognized malignancy varies across organs, malignancies account for a substantial proportion, ranging from approximately one-third to over half, of incidental focal uptake. In light of these rates, physicians are urged to exercise vigilance in not disregarding unexpected uptake, facilitating more assured clinical decisions, and advocating for further active evaluation.

Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.

PETformer network enables ultra-low-dose total-body PET imaging without structural prior

Objective.Positron emission tomography (PET) is essential for non-invasive imaging of metabolic processes in healthcare applications. However, the use of radiolabeled tracers exposes patients to ionizing radiation, raising concerns about carcinogenic potential, and warranting efforts to minimize doses without sacrificing diagnostic quality.Approach.In this work, we present a novel neural network architecture, PETformer, designed for denoising ultra-low-dose PET images without requiring structural priors such as computed tomography (CT) or magnetic resonance imaging. The architecture utilizes a U-net backbone, synergistically combining multi-headed transposed attention blocks with kernel-basis attention and channel attention mechanisms for both short- and long-range dependencies and enhanced feature extraction. PETformer is trained and validated on a dataset of 317 patients imaged on a total-body uEXPLORER PET/CT scanner.Main results.Quantitative evaluations using structural similarity index measure and liver signal-to-noise ratio showed PETformer's significant superiority over other established denoising algorithms across different dose-reduction factors.Significance.Its ability to identify and recover intrinsic anatomical details from background noise with dose reductions as low as 2% and its capacity in maintaining high target-to-background ratios while preserving the integrity of uptake values of small lesions enables PET-only fast and accurate disease diagnosis. Furthermore, PETformer exhibits computational efficiency with only 37 M trainable parameters, making it well-suited for commercial integration.

An evaluation of the physiological uptake range of 18F-fluoro-2-deoxy-D-glucose in normal ovaries of seven dogs using positron emission tomography/computed tomography

Introduction

This study evaluated the physiological uptake range of 18F-fluoro-2-deoxy-D-glucose (18F-FDG) in the normal ovaries of seven dogs using positron emission tomography/computed tomography (PET/CT).

Materials and methods

The dogs were subjected to general anesthesia and were positioned in ventral recumbency for PET/CT scans. The dosage of 18F-FDG ranged from 0.14 to 0.17 mCi/kg and was administered intravenously followed by 0.9% NaCl flushing; PET/CT images of each dog were obtained precisely 60 min after the injection of 18F-FDG. The regions of interest were drawn manually, and standardized uptake values (SUV) were calculated to evaluate the 18F-FDG uptake in each ovary. The maximum and mean SUVs (SUV max and SUV mean) for all the ovaries of the dogs were then computed.

Results

The range of SUV max and SUV mean of the normal ovaries of the dogs were 1.28-1.62 and 1.07-1.31 (mean ± standard deviation), respectively.

Conclusion

This is the first study to investigate the normal 18F-FDG uptake baseline data of normal canine ovaries using PET/CT scans. These data will help clinicians in identifying malignant tumors before anatomical changes in the ovary through PET/CT scans.

Metabolic Responses of Lung Adenocarcinoma Cells to Survive under Stressful Conditions Associated with Tumor Microenvironment

Solid tumors frequently present a heterogeneous tumor microenvironment. Because tumors have the potential to proliferate quickly, the consequence is a reduction in the nutrients, a reduction in the pH (<6.8), and a hypoxic environment. Although it is often assumed that tumor clones show a similar growth rate with little variations in nutrient consumption, the present study shows how growth-specific rate (µ), the specific rates of glucose, lactate, and glutamine consumption (qS), and the specific rates of lactate and glutamate production (qP) of 2D-cultured lung tumor cells are affected by changes in their environment. We determined in lung tumor cells (A427, A549, Calu-1, and SKMES-1) the above mentioned kinetic parameters during the exponential phase under different culture conditions, varying the predominant carbon source, pH, and oxygen tension. MCF-7 cells, a breast tumor cell line that can consume lactate, and non-transformed fibroblast cells (MRC-5) were included as controls. We also analyzed how cell-cycle progression and the amino acid transporter CD98 expression were affected. Our results show that: (1) In glucose presence, μ increased, but qS Glucose and qP Lactate decreased when tumor cells were cultured under acidosis as opposed to neutral conditions; (2) most lung cancer cell lines consumed lactate under normoxia or hypoxia; (3) although qS Glutamine diminished under hypoxia or acidosis, it slightly increased in lactate presence, a finding that was associated with CD98 upregulation; and (4) under acidosis, G0/G1 arrest was induced in A427 cancer cells, although this phenomenon was significantly increased when glucose was changed by lactate as the predominant carbon-source. Hence, our results provide an understanding of metabolic responses that tumor cells develop to survive under stressful conditions, providing clues for developing promising opportunities to improve traditional cancer therapies.

Distinct FDG PET/CT avidity among newly diagnosed intravascular large B-cell lymphoma patients: a descriptive observational study

Intravascular large B-cell lymphoma (IVLBCL) is a rare type of aggressive B-cell non-Hodgkin lymphoma that poses a great diagnostic challenge due to its highly heterogenous clinical manifestations. Although 18F-fluorodeoxyglucose (FDG) is widely used as a diagnostic tool for patients suspected of having lymphoma, as it reveals FDG-avid lesions, the FDG avidity of IVLBCL has not been extensively characterized. Here, we present a comprehensive report of FDG avidity in IVLBCL and its association with clinicopathological features and survival. This descriptive observational study included consecutive patients aged at least 18 years diagnosed with IVLBCL in Peking Union Medical Hospital across 9 years. Among 50 screened IVLBCL patients, 42 had undergone 18F-FDG PET/CT to detect possible lesions for biopsy before pathological diagnosis; their FDG PET/CT (positron emission computed tomography, PET/CT) reports were retrospectively reviewed. The primary endpoint was the clinical description of FDG avidity of newly diagnosed intravascular large B-cell lymphoma and frequency. A total of 73.8% patients showed FDG-avid lesions, with a median SUVmax of 7.4 (range 1-27.7), which was lower than that for other aggressive lymphomas. Clinicopathological features were the same between the FDG-avid group and the non-FDG-avid group, except that the latter had a higher Ki-67 index (median 90% in the nonavid group vs. 80% in the avid group, P = 0.043). The overall survival rate was not different between the PET/CT groups. Our findings demonstrate that FDG PET/CT is a useful diagnostic tool for detecting FDG-avid lesions in IVLBCL patients. A random skin biopsy is essential for assisting in the diagnosis of IVLBCL, even for those with negative PET/CT.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis

BACKGROUND

Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.

METHODS

Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.

RESULTS

Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or >  200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).

CONCLUSIONS

Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.

Zinc-Mediated Radiosynthesis of Unprotected Fluorine-18 Labelled α-Tertiary Amides

This report describes the development of a Zn(OTf)2 -mediated method for converting α-tertiary haloamides to the corresponding fluorine-18 labelled α-tertiary fluoroamides with no-carrier-added [18 F]tetramethylammonium fluoride. 1,5,7-Triazabicyclo[4.4.0]dec-5-ene is an essential additive for achieving high radiochemical conversion. Under the optimised conditions, radiofluorination proceeds at sterically hindered tertiary sites in high radiochemical conversions, yields, and purities. This method has been successfully automated and applied to access >200 mCi (>7.4 GBq) of several model radiofluorides. Mechanistic studies led to the development of a new, nucleophilic C-H radiofluorination process using N-sulphonyloxyamide substrates.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

BACKGROUND

The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT.

METHODS

A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots.

RESULTS

In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations.

CONCLUSION

This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.

Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance

PURPOSE

Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance.

METHODS

The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18 F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18 F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions.

RESULTS

Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUV mean , SUV max , and SUV peak , respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction.

CONCLUSION

We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18 F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.

Tumor Progression from a Fibroblast Activation Protein Perspective: Novel Diagnostic and Therapeutic Scenarios for Colorectal Cancer

In 2020, the Global Cancer Observatory estimated the incidence of colorectal cancer (CRC) at around 10.7% coupled with a mortality rate of 9.5%. The explanation for these values lies in the tumor microenvironment consisting of the extracellular matrix and cancer-associated fibroblasts (CAFs). Fibroblast activation protein (FAP) offers a promising target for cancer therapy since its functions contribute to tumor progression. Immunohistochemistry examination of FAP, fibronectin ED-B, and CXCR4 in primary tumors and their respective synchronous and/or metachronous metastases along with semiquantitative analysis have been carried out on histological samples of 50 patients diagnosed with metastatic CRC. The intensity of FAP, articulated by both "Intensity %" and "Intensity score", is lower in the first metastasis compared to the primary tumor with a statistically significant correlation. No significant correlations have been observed regarding fibronectin ED-B and CXCR4. Tumors that produce FAP have an ambivalent relationship with this protein. At first, they exploit FAP, but later they reduce its expressiveness. Although our study has not directly included FAP-Inhibitor (FAPI) PET/CT, the considerable expression of FAP reveals its potential as a diagnostic and therapeutic tool worthy of further investigation. This dynamic relationship between cancer and FAP has substantial diagnostic and therapeutic implications.

Multi-institutional PET/CT image segmentation using federated deep transformer learning

BACKGROUND AND OBJECTIVE

Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation.

METHODS

A dataset consisting of 328 FL (HN) cancer patients who underwent clinical PET/CT examinations gathered from six different centers was enrolled. A pure transformer network was implemented as fully core segmentation algorithms using dual channel PET/CT images. We evaluated different frameworks (single center-based, centralized baseline, as well as seven different FL algorithms) using 68 PET/CT images (20% of each center data). In particular, the implemented FL algorithms include clipping with the quantile estimator (ClQu), zeroing with the quantile estimator (ZeQu), federated averaging (FedAvg), lossy compression (LoCo), robust aggregation (RoAg), secure aggregation (SeAg), and Gaussian differentially private FedAvg with adaptive quantile clipping (GDP-AQuCl).

RESULTS

The Dice coefficient was 0.80±0.11 for both centralized and SeAg FL algorithms. All FL approaches achieved centralized learning model performance with no statistically significant differences. Among the FL algorithms, SeAg and GDP-AQuCl performed better than the other techniques. However, there was no statistically significant difference. All algorithms, except the center-based approach, resulted in relative errors less than 5% for SUVmax and SUVmean for all FL and centralized methods. Centralized and FL algorithms significantly outperformed the single center-based baseline.

CONCLUSIONS

The developed FL-based (with centralized method performance) algorithms exhibited promising performance for HN tumor segmentation from PET/CT images.

The Diagnostic Value of 18F-FDG PET/CT Scan in Characterizing Adrenal Tumors

CONTEXT

Imaging plays an important role in the characterization of adrenal tumors, but findings might be inconclusive. The clinical question is whether 18F fluodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is of diagnostic value in this setting.

OBJECTIVE

This meta-analysis was aimed at the diagnostic value of 18F-FDG PET/CT in differentiating benign from malignant adrenal tumors discovered either as adrenal incidentaloma or during staging or follow-up of oncologic patients.

DATA SOURCES

PubMed, EMBASE, Web of Science, and Cochrane Library were searched to select articles between 2000 and 2021.

STUDY SELECTION

We included studies describing the diagnostic value of 18F-FDG PET/CT in adult patients with an adrenal tumor. Exclusion criteria were 10 or fewer participants, insufficient data on histopathology, clinical follow-up, or PET results. After screening of title and abstract by 2 independent reviewers, 79 studies were retrieved, of which 17 studies met the selection criteria.

DATA EXTRACTION

Data extraction using a protocol and quality assessment according to QUADAS-2 was performed independently by at least 2 authors.

DATA SYNTHESIS

A bivariate random-effects model was applied using R (version 3.6.2.). Pooled sensitivity and specificity of 18F-FDG PET/CT for identifying malignant adrenal tumors was 87.3% (95% CI, 82.5%-90.9%) and 84.7% (95% CI, 79.3%-88.9%), respectively. The pooled diagnostic odds ratio was 9.20 (95% CI, 5.27-16.08; P < .01). Major sources of heterogeneity (I2, 57.1% [95% CI, 27.5%-74.6%]) were in population characteristics, reference standard, and interpretation criteria of imaging results.

CONCLUSIONS

18F-FDG PET/CT had good diagnostic accuracy for characterization of adrenal tumors. The literature, however, is limited, in particular regarding adrenal incidentalomas. Large prospective studies in well-defined patient populations with application of validated cutoff values are needed.

Neurosarcoidosis With Multi-Organ Involvement: A Case Report and Literature Review

Sarcoidosis is a multisystemic disease that, in rare cases, can involve the central nervous system (CNS). We present a case of sarcoidosis with intracranial and multi-organ involvement. The patient presented with a one-month history of headaches. Imaging revealed leptomeningeal nodular enhancement (LNE), and a PET/CT scan of the chest and abdomen showed bilateral hilar, retroperitoneal, and inguinal lymphadenopathy. The diagnosis of sarcoidosis was confirmed by an ultrasound-guided inguinal lymph node biopsy. The patient was started on a combination of corticosteroids and immunosuppressive drugs, with a gradual improvement in symptoms and radiological findings over several months.

Reactive Palladium-Ligand Complexes for 11C-Carbonylation at Ambient Pressure: A Breakthrough in Carbon-11 Chemistry

The Pd-Xantphos-mediated ¹¹C-carbonylation protocol (also known as the "Xantphos- method"), due to its simplistic and convenient nature, has facilitated researchers in meeting a longstanding need for preparing ¹¹C-carbonyl-labeled radiopharmaceuticals at ambient pressure for positron emission tomography (PET) imaging and drug discovery. This development could be viewed as a breakthrough in carbon-11 chemistry, as evidenced by the rapid global adoption of the method by the pharmaceutical industry and academic laboratories worldwide. The method has been fully automated for the good manufacturing practice (GMP)-compliant production of novel radiopharmaceuticals for human use, and it has been adapted for "in-loop" reactions and microwave technology; an impressive number of ¹¹C-labeled compounds (>100) have been synthesized. Given the simplicity and efficiency of the method, as well as the abundance of carbonyl groups in bioactive drug molecules, we expect that this methodology will be even more widely adopted in future PET radiopharmaceutical research and drug development.

Brain glucose metabolism on [18F]-FDG PET/CT: a dynamic biomarker predicting depression and anxiety in cancer patients

Objectives

To explore the correlation between the incidence rates of depression and anxiety and cerebral glucose metabolism in cancer patients.

Methods

The experiment subjects consisted of patients with lung cancer, head and neck tumor, stomach cancer, intestinal cancer, breast cancer and healthy individuals. A total of 240 tumor patients and 39 healthy individuals were included. All subjects were evaluated by the Hamilton depression scale (HAMD) and Manifest anxiety scale (MAS), and were examined by whole body Positron Emission Tomography/Computed Tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG). Demographic, baseline clinical characteristics, brain glucose metabolic changes, emotional disorder scores and their relations were statistically analyzed.

Results

The incidence rates of depression and anxiety in patients with lung cancer were higher than those in patients with other tumors, and Standard uptake values (SUVs) and metabolic volume in bilateral frontal lobe, bilateral temporal lobe, bilateral caudate nucleus, bilateral hippocampus, left cingulate gyrus were lower than those in patients with other tumors. We also found that poor pathological differentiation, and advanced TNM stage independently associated with depression and anxiety risk. SUVs in the bilateral frontal lobe, bilateral temporal lobe, bilateral caudate nucleus, bilateral hippocampus, left cingulate gyrus were negatively correlated with HAMD and MAS scores.

Conclusion

This study revealed the correlation between brain glucose metabolism and emotional disorders in cancer patients. The changes in brain glucose metabolism were expected to play a major role in emotional disorders in cancer patients as psychobiological markers. These findings indicated that functional imaging can be applied for psychological assessment of cancer patients as an innovative method.

The Clinical Significance of Incidental GIT Uptake on PET/CT: Radiologic, Endoscopic, and Pathologic Correlation

Incidental gastrointestinal tract (GIT) [18F]-Fluorodeoxyglucose (FDG) uptake in positron emission technology/computed tomography (PET/CT) is an unexpected and often complicated finding for clinicians. This retrospective study reviewed 8991 charts of patients who underwent PET/CT: 440 patients had incidental GIT uptake, of which 80 underwent endoscopy. Patient characteristics, imaging parameters, and endoscopic findings were studied. Of the 80 patients, 31 had cancer/pre-cancer lesions (16 carcinomas; 15 pre-malignant polyps). Compared to patients with benign/absent lesions, patients with cancer/pre-cancer lesions were significantly older (p = 0.01), underwent PET/CT for primary evaluation/staging of cancer (p = 0.03), had focal GIT uptake (p = 0.04), and had lower GIT uptake (p = 0.004). Among patients with focal uptake, an SUVmax of 9.2 had the highest sensitivity (0.76) and specificity (0.885) in detecting cancer/pre-cancerous lesions. Lower GIT uptake was most common in the sigmoid colon, and upper GIT uptake was most frequent in the stomach. In a bivariate analysis, predictors of cancer/pre-cancer were older age, PET/CT indicated for primary evaluation, focal uptake, uptake in the lower GIT, and higher SUVmax. Further endoscopic investigation is warranted for patients with incidental GIT uptake, especially in the elderly or those presenting for primary evaluation with PET/CT, with the following findings on imaging: lower GIT uptake, focal uptake, or high SUVmax.

Use of 18 F-2-deoxy-2-fluoro-D-glucose positron emission tomography/computed tomography for staging thyroid carcinoma in a cat

Thyroid nodules are common in older cats and are mostly benign; however, carcinomas may occur infrequently. In cats, thyroid carcinomas tend to be highly metastatic. The role of ¹⁸ F-2-deoxy-2-fluoro-D-glucose (FDG) positron emission tomography (PET)/computed tomography (CT) in human thyroid carcinoma has been well established. However, guidelines have not yet been established for veterinary medicine. Metastasis assessment has typically been performed using CT in veterinary medicine; however, it is poorly sensitive in detecting regional lymph nodes or distant metastases if these lesions are not abnormally contrast-enhanced, enlarged or cause overt mass effects. This case suggested that FDG PET/CT may be used for staging feline thyroid carcinoma, and the results contributed to treatment recommendations.

Optimal clinical protocols for total-body 18F-FDG PET/CT examination under different activity administration plans

BACKGROUND

Highly sensitive digital total-body PET/CT scanners (uEXPLORER) have great potential for clinical applications and fundamental research. Given their increasing sensitivity, low-dose scanning or snapshot imaging is now possible in clinics. However, a standardized total-body ¹⁸F-FDG PET/CT protocol is still lacking. Establishing a standard clinical protocol for total-body 18F-FDG PET/CT examination under different activity administration plans can help provide a theoretical reference for nuclear radiologists.

METHODS

The NEMA image quality (IQ) phantom was used to evaluate the biases of various total-body ¹⁸F-FDG PET/CT protocols related to the administered activity, scan duration, and iterations. Several objective metrics, including contrast recovery (CR), background variability (BV), and contrast-to-noise ratio (CNR), were measured from different protocols. In line with the European Association of Nuclear Medicine Research Ltd. (EARL) guidelines, optimized protocols were suggested and evaluated for total-body ¹⁸F-FDG PET/CT imaging for three different injected activities.

RESULTS

Our NEMA IQ phantom evaluation resulted in total-body PET/CT images with excellent contrast and low noise, suggesting great potential for reducing administered activity or shortening the scan duration. Different to the iteration number, prolonging the scan duration was the first choice for achieving higher image quality regardless of the activity administered. In light of image quality, tolerance of oncological patients, and the risk of ionizing radiation damage, the 3-min acquisition and 2-iteration (CNR = 7.54), 10-min acquisition and 3-iteration (CNR = 7.01), and 10-min acquisition and 2-iteration (CNR = 5.49) protocols were recommended for full-dose (3.70 MBq/kg), half-dose (1.95 MBq/kg), and quarter-dose (0.98 MBq/kg) activity injection schemes, respectively. Those protocols were applied in clinical practices, and no significant differences were observed for the SUV_max of large/small lesions or the SUV_mean of different healthy organs/tissues.

CONCLUSION

These findings support that digital total-body PET/CT scanners can generate PET images with a high CNR and low-noise background, even with a short acquisition time and low administered activity. The proposed protocols for different administered activities were determined to be valid for clinical examination and can maximize the value of this imaging type.

Preoperative Hilar and Mediastinal Lymph Node Staging in Patients with Suspected or Diagnosed Lung Cancer: Accuracy of 18F-FDG-PET/CT:A Retrospective Cohort Study of 138 Patients

The aim of this study was to evaluate the diagnostic accuracy of integrated 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG-PET/CT) in hilar and mediastinal lymph node (HMLN) staging of suspected or proven lung cancer, and to investigate potential risk factors for false negative and false positive HMLN metastases. We retrospectively analyzed 162 consecutive patients with suspected or pathologically proven non-small cell lung cancer (NSCLC). The receiver operating characteristic (ROC) curve was generated to determine the diagnostic efficacy of 18F-FDG-PET/CT. Univariate and multivariate analyses were conducted to detect risk factors of false positives and false negatives. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of integrated 18F-FDG-PET/CT in detecting HMLN metastases were 59.1% (26/44), 69.1% (65/94), 47.3% (26/55), 78.3% (65/83), and 65.9% (91/138), respectively. The ROC curve showed an area under the curve (AUC) of 0.625 (95%-CI 0.468-0.782). The incidence of false negative and false positive HMLN metastases was 21.7% (18/83) and 52.7% (29/55), respectively. Our data shows that integrated 18F-FDG-PET/CT staging provides lower specificity and sensitivity. This confirms the ESTS guideline on lymph node staging for PET-positive HMLN. Yet it advocates more invasive staging even for PET-negative HMLN.

Current advances in the application of nanomedicine in bladder cancer

Bladder cancer is the most common malignant tumor of the urinary system, however there are several shortcomings in current diagnostic and therapeutic measures. In terms of diagnosis, the diagnostic tools currently available are not sufficiently sensitive and specific, and imaging is poor, leading to misdiagnosis and missed diagnoses, which can delay treatment. In terms of treatment, current treatment options include surgery, chemotherapy, immunotherapy, gene therapy, and other emerging treatments, as well as combination therapies. However, the main reasons for poor efficacy and side effects during treatment are the lack of specificity and targeting, improper dose control of drugs and photosensitizers, damage to normal cells while attacking cancer cells, and difficulty in delivering siRNA to cancer cells. Nanomedicine is an emerging approach. Among the many nanotechnologies applied in the medical field, nanocarrier-assisted drug delivery systems have attracted extensive research interest due to their great translational value. Well-designed nanoparticles can deliver agents or drugs to specific cell types within target organs through active targeting or passive targeting (enhanced permeability and retention), which allows for imaging, diagnosis, as well as treatment of cancer. This paper reviews advances in the application of various nanocarriers and their advantages and drawbacks, with a focus on their use in the diagnosis and treatment of bladder cancer.

Virtual high-count PET image generation using a deep learning method

PURPOSE

Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images.

METHODS

The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images.

RESULTS

The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference.

CONCLUSIONS

A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

Heterodimeric RGD-NGR PET Tracer for the Early Detection of Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal gastrointestinal cancer, and its poor prognosis is highly associated with the lack of an efficient early detection technology. Here, we report that RGD-NGR heterodimer labeled with PET isotope could be applied in PDAC early detection.

PROCEDURES

The RGD-NGR tracer was first compared with its corresponding monomeric counterparts via PET imaging studies using mice bearing a subcutaneous BxPC3 tumor. Subsequently, the RGD-NGR tracer was evaluated in autochthonous mouse models with spontaneously developed late stage PanIN lesions (KCER mice) or PDAC (KPC mice) via both PET imaging studies and ex vivo biodistribution studies. Furthermore, a comparison between 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) and the RGD-NGR tracer was conducted via PET imaging of the same KCH mouse bearing spontaneously developed PDAC. H&E staining was performed to confirm the malignant pancreatic tissue in the KCH mouse. Immunofluorescence staining was performed to confirm the expression of integrin αVβ3 and CD13.

RESULTS

The RGD-NGR tracer exhibited improved in vivo performance as compared with its corresponding monomeric counterparts on the subcutaneous BxPC3 tumor mouse model. Subsequent evaluation in autochthonous mouse models demonstrated its capability to detect both pre-malignant and malignant pancreases. Further comparison with [18F]F-FDG revealed the superiority of the proposed heterodimer in imaging spontaneously developed PDAC. H&E staining confirmed the malignant pancreatic tissue in the KCH mouse, while the expression of both integrin αVβ3 and CD13 receptors was demonstrated with immunofluorescence staining.

CONCLUSION

The proposed RGD-NGR heterodimer possesses the potential to be applied in the PDAC early detection for high-risk populations.

A personalized deep learning denoising strategy for low-count PET images

Objective. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels.Approach.We first investigated the impact of the noise level in the training images on the model performance. Five 3D U-Net models were trained on five groups of images at different noise levels, and a one-size-fits-all model was trained on images covering a wider range of noise levels. We then developed a personalized weighting method by linearly blending the results from two models trained on 20%-count level images and 60%-count level images to balance the trade-off between noise reduction and spatial blurring. By adjusting the weighting factor, denoising can be conducted in a personalized and task-dependent way.Main results.The evaluation results of the six models showed that models trained on noisier images had better performance in denoising but introduced more spatial blurriness, and the one-size-fits-all model did not generalize well when deployed for testing images with a wide range of noise levels. The personalized denoising results showed that noisier images require higher weights on noise reduction to maximize the structural similarity and mean squared error. And model trained on 20%-count level images can produce the best liver lesion detectability.Significance.Our study demonstrated that in deep learning-based low dose PET denoising, noise levels in the training input images have a substantial impact on the model performance. The proposed personalized denoising strategy utilized two training sets to overcome the drawbacks introduced by each individual network and provided a series of denoised results for clinical reading.

Diagnostic accuracy and clinical impact of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in Positron Emission Tomography - Computed Tomography (PET-CT)-positive mediastinal lymphadenopathies in patients with thoracic or extra-thoracic malignancies

BACKGROUND

The high sensitivity of PET-CT can identify hypermetabolic mediastinal adenopathies during cancer management, but specificity is low and a biopsy is sometimes required to eliminate benign adenopathies.

METHODS

This prospective diagnostic accuracy study included patients with hypermetabolic mediastinal lymphadenopathies revealed on PET-CT during either the initial management of a cancer, treatment evaluation, or monitoring. All patients underwent EUS-FNA. Diagnoses of malignancy based on cytological analysis following EUS-FNA were compared with clinical and radiological follow-up information. The treatment strategy decided before the results of the EUS-FNA pathology reports (Multidisciplinary Team Meeting [MTM-1]) was recorded and compared to the treatment strategy decided once pathological data from EUS-FNA were available (MTM-2).

MAIN FINDINGS

Between 2013 and 2018, 75 patients were included with 47 eligible and evaluable patients. Sensitivity, specificity, and positive and negative predictive values of EUS-FNA were 93%, 100%, 100% and 90%, respectively. The concordance value between the therapeutic strategies determined for MTM-1 and MTM-2 was 44.7%. There were no significant differences in the intensity of fixation on PET-CT between malignant and benign lesions.

CONCLUSION

The diagnostic accuracy of the minimally invasive EUS-FNA procedure is sufficiently robust to avoid the need for diagnostic surgery. The combination of PET-CT and EUS-FNA may alter the therapeutic strategy that would be considered after PET-CT alone.

REGISTRATION

NCT01892501.

Carbon-11 patents (2012-2022): synthetic methodologies and novel radiotracers for PET imaging

INTRODUCTION

Carbon-11 is a short-lived radionuclide with versatile applications in synthetic methodologies to develop a variety of novel PET radiotracers. Different primary and secondary carbon-11 precursors are generated from cyclotron produced [¹¹C]CO₂ and used to insert carbon-11 radionuclide into the target specific bioactive molecules.

AREAS COVERED

In this review, the patents as well as specific research articles on carbon-11 radiotracer synthesis and PET imaging applications in various diseases are mentioned since 2012 to 2022 through SciFinder database.

EXPERT OPINION

Carbon-11 is generally easier to insert into more organic scaffolds as a greater variety of functional groups. Despite the short half-life of carbon-11 radionuclide (t_1/2 = 20.4 min), it is widely used in PET radiotracer development due to its direct insertion into bioactive compounds and less isotopic dilution unlike other positron emitters like fluorine-18. Various synthons can be easily generated using the primary and secondary carbon-11 precursors like [¹¹C]CO₂, [¹¹C]CH₄, ¹¹CH₃I, ¹¹CO, ¹¹COCl_2, ¹¹CN, ¹¹CS_2, and ¹¹CH₃OTf etc. that would be useful to develop any PET radiotracers by adapting various organic methods. The carbon-11 radiotracers provide target-oriented information associated with the pharmacology, and physiological conditions of the disease status. Various protocols and automated methods were adapted for easy and convenient synthesis of carbon-11 radiotracers. The PET advances drug development and clinical trials by revealing biological target engagement, proof of mechanism, pharmacokinetic, and pharmacodynamic profiles of new drug candidates using selective radiotracers.

Positron Emission Tomography Molecular Imaging for Phenotyping and Management of Lymphoma

Positron emission tomography (PET) represents molecular imaging for non-invasive phenotyping of physiological and biochemical processes in various oncological diseases. PET imaging with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) for glucose metabolism evaluation is the standard imaging modality for the clinical management of lymphoma. One of the ¹⁸F-FDG PET applications is the detection and pre-treatment staging of lymphoma, which is highly sensitive. ¹⁸F-FDG PET is also applied during treatment to evaluate the individual chemo-sensitivity and accordingly guide the response-adapted therapy. At the end of the therapy regiment, a negative PET scan is indicative of a good prognosis in patients with advanced Hodgkin's lymphoma and diffuse large B-cell lymphoma. Thus, adjuvant radiotherapy may be alleviated. Future PET studies using non-¹⁸F-FDG radiotracers, such as ⁶⁸Ga-labeled pentixafor (a cyclic pentapeptide that enables sensitive and high-contrast imaging of C-X-C motif chemokine receptor 4), ⁶⁸Ga-labeled fibroblast activation protein inhibitor (FAPI) that reflects the tumor microenvironment, and ⁸⁹Zr-labeled atezolizumab that targets the programmed cell death-ligand 1 (PD-L1), may complement ¹⁸F-FDG and offer essential tools to decode lymphoma phenotypes further and identify the mechanisms of lymphoma therapy.

Histogram analysis of 18F-FDG PET imaging SUVs may predict the histologic grade of oral squamous cell carcinoma

OBJECTIVE

We tested the hypothesis that histogram analysis parameters of standardized uptake values (SUVs) obtained preoperatively using ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) are significantly influenced by differences in metabolic capacity due to the histologic grade of oral squamous cell carcinoma (OSCC).

STUDY DESIGN

The study included 62 patients who were clinically diagnosed with OSCC and received surgical treatment after an ¹⁸F-FDG PET examination. Histogram analysis was performed using all voxels contained in the tumor area of each patient with an SUV ≥2.5. The histogram parameters calculated were the mean and standard deviation of SUVs, maximum SUV, metabolic tumor volume, skewness, and kurtosis. Statistical analyses were performed using a Mann-Whitney U test to calculate the significance of differences in these parameters between groups with well- and moderately- or poorly-differentiated tumors. Statistical significance was assumed at P < .05.

RESULTS

Only a comparison of kurtosis in the histogram showed a significant difference between the well- and moderately/poorly-differentiated tumors (P = .0294).

CONCLUSIONS

The distribution of metabolic capacity in oral squamous cell carcinoma tissues revealed on an ¹⁸F-FDG PET examination may help identify the histologic grade. This finding may provide valuable information for determining the subsequent treatment plan and predicting disease prognosis.

Estimation of the Internal Dose Imparted by 18F-Fluorodeoxyglucose to Tissues by Using Fricke Dosimetry in a Phantom and Positron Emission Tomography

Purpose

Assessment of the radiation dose delivered to a tumor and different organs is a major issue when using radiolabelled compounds for diagnostic imaging or endoradiotherapy. The present article reports on a study to correlate the mean 18F-fluorodeoxyglucose (18F-FDG) activity in different tissues measured in a mouse model by positron emission tomography (PET) imaging, with the dose assessed in vitro by Fricke dosimetry.

Methods

The dose-response relationship of the Fricke dosimeter and PET data was determined at different times after adding 18F-FDG (0-80 MBq) to a Fricke solution (1 mM ferrous ammonium sulfate in 0.4 M sulfuric acid). The total dose was assessed at 24 h (~13 half-lives of 18F-FDG). The number of coincident events produced in 3 mL of Fricke solution or 3 mL of deionized water that contained 60 MBq of 18F-FDG was measured using the Triumph/LabPET8TM preclinical PET/CT scanner. The total activity concentration measured by PET was correlated with the calculated dose from the Fricke dosimeter, at any exposure activity of 18F-FDG.

Results

The radiation dose measured with the Fricke dosimeter increased rapidly during the first 4 h after adding 18F-FDG and then gradually reached a plateau. Presence of non-radioactive-FDG did not alter the Fricke dosimetry. The characteristic responses of the dosimeter and PET imaging clearly exhibit linearity with injected activity of 18F-FDG. The dose (Gy) to time-integrated activity (MBq.h) relationship was measured, yielding a conversion factor of 0.064 ± 0.06 Gy/MBq.h in the present mouse model. This correlation provides an efficient alternative method to measure, three-dimensionally, the total and regional dose absorbed from 18F-radiotracers.

Conclusions

The Fricke dosimeter can be used to calibrate a PET scanner, thus enabling the determination of dose from the measured radioactivity emitted by 18F-FDG in tissues. The method should be applicable to radiotracers with other positron-emitting radionuclides.

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for ¹⁸F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30-300 s and 1800 s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NEC_phantom), 10-mm sphere contrast (Q_H,10 mm), background variability (N_10 mm), contrast-to-noise ratio (Q_H,10 mm/N_10 mm), image noise level (CV_BG), and SUVmax and SUVpeak for hot spheres (10-37 mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: Q_H,10 mm/N_10 mm ≥ 2.5 and CV_BG ≤ 14.1%. The 10th-90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CV_BG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CV_BG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

FDG uptake in PET is associated with the tumor microenvironment in metastatic lymph nodes and prognosis in N2 lung adenocarcinoma

Positron emission tomography is a useful technique for diagnosing lymph node (LN) metastasis. This study aimed to elucidate the association between fluorodeoxyglucose accumulation and the microenvironment in metastatic LNs in lung adenocarcinoma. We retrospectively analyzed 62 patients with surgically resected pathological N2 lung adenocarcinoma who underwent preoperative PET. The maximum standardized uptake value (SUV_max) in the metastatic LNs was measured. Lymph node specimens were immunohistochemically analyzed for CD8⁺, FoxP3⁺, and CD79a⁺ lymphocytes, CD204⁺ tumor‐associated macrophages (TAMs), and alpha‐smooth muscle actin‐positive cancer‐associated fibroblasts (αSMA⁺ CAFs). We compared the clinicopathologic and immunohistochemical characteristics between two groups with high and low LN SUV_max. Using novel 3D hybrid spheroid models, we investigated the change in invasiveness of cancer cells in the presence of CAFs. In the multivariate analyses, LN SUV_max was an independent prognostic factor. The overall survival in the LN SUV_max high group was significantly worse than in the low group (P = .034). In the LN SUV_max high group, metastatic cancer cell invasion of extranodal tissue was more frequent (P = .005) and the number of CD204⁺ TAMs and αSMA⁺ CAFs in metastatic LNs was significantly higher than in the low group (P < .001 and P = .002, respectively). Hybrid spheroid models revealed that cancer cells coexisting with CAFs were more invasive than those without CAFs. Our results indicated a strong association between LN SUV_max and poor prognosis in patients with N2 lung adenocarcinoma. Moreover, LN SUV_max was suggested to be associated with the presence of tumor‐promoting stromal cells in metastatic LNs.

Correlation of 4'-[methyl-11C]-thiothymidine PET with Ki-67 immunohistochemistry separately in patients with newly diagnosed and recurrent gliomas

OBJECTIVE

4'-[methyl-11C]-thiothymidine (4DST) uptake on PET was correlated with proliferative activity separately in patients with newly diagnosed and recurrent gliomas.

METHODS

A total of 29 patients, 18 with newly diagnosed gliomas and 11 with recurrent gliomas who underwent 4DST PET/computed tomography (CT) were available for a retrospective analysis of prospectively collected data. The maximum standardized uptake value (SUVmax) of tumor (T) and the mean SUV of normal contralateral hemisphere (N) were calculated, and the tumor-to-normal (T/N) ratio was determined. Proliferative tumor volume (PTV) and total lesion proliferation (TLP) were also calculated. Proliferative activity as indicated by the Ki-67 index was estimated in tissue specimens. Immunohistochemical findings were correlated with 4DST PET parameters.

RESULTS

All gliomas but three newly diagnosed gliomas had 4DST uptake on PET. No significant differences in SUVmax, T/N ratio, PTV, or TLP were observed between the newly diagnosed and recurrent gliomas. In the former, correlations between SUVmax (r = 0.57, P = 0.02), T/N ratio (r = 0.51, P = 0.03), PTV (r = 0.74, P < 0.001), and TLP (r = 0.76, P < 0.001) and the Ki-67 index were found. In the latter, the results did not seem to suggest any correlations between any of the PET parameters and Ki-67 index.

CONCLUSION

Although preliminary, these results suggest that 4DST PET may be useful for the noninvasive evaluation of proliferation in patients with newly diagnosed gliomas. These data in a small recurrent patient population do not support a clear-cut correlation between 4DST uptake and proliferation.

Application of nanotechnology in the diagnosis and treatment of bladder cancer

Bladder cancer (BC) is a common malignancy in the genitourinary system and the current theranostic approaches are unsatisfactory. Sensitivity and specificity of current diagnosis methods are not ideal and high recurrence and progression rates after initial treatment indicate the urgent need for management improvements in clinic. Nanotechnology has been proposed as an effective method to improve theranosis efficiency for both non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). For example, gold nanoparticles (AuNPs) have been developed for simple, fast and sensitive urinary sample test for bladder cancer diagnosis. Nanoparticles targeting bladder cancers can facilitate to distinguish the normal and abnormal bladder tissues during cystoscopy and thus help with the complete removal of malignant lesions. Both intravenous and intravesical agents can be modified by nanotechnology for targeted delivery, high anti-tumor efficiency and excellent tolerability, exhibiting encouraging potential in bladder cancer treatment. Photosensitizers and biological agents can also be delivered by nanotechnology, intermediating phototherapy and targeted therapy. The management of bladder cancer remained almost unchanged for decades with unsatisfactory effect. However, it is likely to change with the fast-developed nanotechnology. Herein we summarized the current utility of nanotechnology in bladder cancer diagnosis and treatment, providing insights for the future designing and discovering novel nanoparticles for bladder cancer management.