18F-FDG PET and PET/CT in the evaluation of cancer treatment response

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.

The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT

BACKGROUND

[18F]FDG PET denoising by SubtlePET™ using deep learning artificial intelligence (AI) was previously found to induce slight modifications in lesion and reference organs' quantification and in lesion detection. As a next step, we aimed to evaluate its clinical impact on [18F]FDG PET solid tumour treatment response assessments, while comparing "standard PET" to "AI denoised half-duration PET" ("AI PET") during follow-up.

RESULTS

110 patients referred for baseline and follow-up standard digital [18F]FDG PET/CT were prospectively included. "Standard" EORTC and, if applicable, PERCIST response classifications by 2 readers between baseline standard PET1 and follow-up standard PET2 as a "gold standard" were compared to "mixed" classifications between standard PET1 and AI PET2 (group 1; n = 64), or between AI PET1 and standard PET2 (group 2; n = 46). Separate classifications were established using either standardized uptake values from ultra-high definition PET with or without AI denoising (simplified to "UHD") or EANM research limited v2 (EARL2)-compliant values (by Gaussian filtering in standard PET and using the same filter in AI PET). Overall, pooling both study groups, in 11/110 (10%) patients at least one EORTCUHD or EARL2 or PERCISTUHD or EARL2 mixed vs. standard classification was discordant, with 369/397 (93%) concordant classifications, unweighted Cohen's kappa = 0.86 (95% CI: 0.78-0.94). These modified mixed vs. standard classifications could have impacted management in 2% of patients.

CONCLUSIONS

Although comparing similar PET images is preferable for therapy response assessment, the comparison between a standard [18F]FDG PET and an AI denoised half-duration PET is feasible and seems clinically satisfactory.

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET

BACKGROUND

Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

METHODS

Clinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference.

RESULTS

CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results.

CONCLUSIONS

CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.

In Vivo PET Detection of Lung Micrometastasis in Mice by Targeting Endothelial VCAM-1 Using a Dual-Contrast PET/MRI Probe

Current clinical diagnostic imaging methods for lung metastases are sensitive only to large tumours (1-2 mm cross-sectional diameter), and early detection can dramatically improve treatment. We have previously demonstrated that an antibody-targeted MRI contrast agent based on microparticles of iron oxide (MPIO; 1 μm diameter) enables the imaging of endothelial vascular cell adhesion molecule-1 (VCAM-1). Using a mouse model of lung metastasis, upregulation of endothelial VCAM-1 expression was demonstrated in micrometastasis-associated vessels but not in normal lung tissue, and binding of VCAM-MPIO to these vessels was evident histologically. Owing to the lack of proton MRI signals in the lungs, we modified the VCAM-MPIO to include zirconium-89 (89Zr, t1/2 = 78.4 h) in order to allow the in vivo detection of lung metastases by positron emission tomography (PET). Using this new agent (89Zr-DFO-VCAM-MPIO), it was possible to detect the presence of micrometastases within the lung in vivo from ca. 140 μm in diameter. Histological analysis combined with autoradiography confirmed the specific binding of the agent to the VCAM-1 expressing vasculature at the sites of pulmonary micrometastases. By retaining the original VCAM-MPIO as the basis for this new molecular contrast agent, we have created a dual-modality (PET/MRI) agent for the concurrent detection of lung and brain micrometastases.

Feasibility study of multimodal imaging for redox status and glucose metabolism in tumor

Understanding the tumor redox status is important for efficient cancer treatment. Here, we noninvasively detected changes in the redox environment of tumors before and after cancer treatment in the same individuals using a novel compact and portable electron paramagnetic resonance imaging (EPRI) device and compared the results with glycolytic information obtained through autoradiography using 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG). Human colon cancer HCT116 xenografts were used in the mice. We used 3-carbamoyl-PROXYL (3CP) as a paramagnetic and redox status probe for the EPRI of tumors. The first EPRI was followed by the intraperitoneal administration of buthionine sulfoximine (BSO), an inhibitor of glutathione synthesis, or X-ray irradiation of the tumor. A second EPRI was performed on the following day. Autoradiography was performed after the second EPRI. After imaging, the tumor sections were evaluated by histological analysis and the amount of reducing substances in the tumor was measured. BSO treatment and X-ray irradiation significantly decreased the rate of 3CP reduction in tumors. Redox maps of tumors obtained from EPRI can be compared with tissue sections of approximately the same cross section. BSO treatment reduced glutathione levels in tumors, whereas X-ray irradiation did not alter the levels of any of the reducing substances. Comparison of the redox map with the autoradiography of [18F]FDG revealed that regions with high reducing power in the tumor were active in glucose metabolism; however, this correlation disappeared after X-ray irradiation. These results suggest that the novel compact and portable EPRI device is suitable for multimodal imaging, which can be used to study tumor redox status and therapeutic efficacy in cancer, and for combined analysis with other imaging modalities.

Targeting Pivotal Hallmarks of Cancer for Enhanced Therapeutic Strategies in Triple-Negative Breast Cancer Treatment-In Vitro, In Vivo and Clinical Trials Literature Review

This literature review provides a comprehensive overview of triple-negative breast cancer (TNBC) and explores innovative targeted therapies focused on specific hallmarks of cancer cells, aiming to revolutionize breast cancer treatment. TNBC, characterized by its lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), presents distinct features, categorizing these invasive breast tumors into various phenotypes delineated by key elements in molecular assays. This article delves into the latest advancements in therapeutic strategies targeting components of the tumor microenvironment and pivotal hallmarks of cancer: deregulating cellular metabolism and the Warburg effect, acidosis and hypoxia, the ability to metastasize and evade the immune system, aiming to enhance treatment efficacy while mitigating systemic toxicity. Insights from in vitro and in vivo studies and clinical trials underscore the promising effectiveness and elucidate the mechanisms of action of these novel therapeutic interventions for TNBC, particularly in cases refractory to conventional treatments. The integration of targeted therapies tailored to the molecular characteristics of TNBC holds significant potential for optimizing clinical outcomes and addressing the pressing need for more effective treatment options for this aggressive subtype of breast cancer.

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.

From FDG and beyond: the evolving potential of nuclear medicine

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Engineered live bacteria as disease detection and diagnosis tools

Sensitive and minimally invasive medical diagnostics are essential to the early detection of diseases, monitoring their progression and response to treatment. Engineered bacteria as live sensors are being developed as a new class of biosensors for sensitive, robust, noninvasive, and in situ detection of disease onset at low cost. Akin to microrobotic systems, a combination of simple genetic rules, basic logic gates, and complex synthetic bioengineering principles are used to program bacterial vectors as living machines for detecting biomarkers of diseases, some of which cannot be detected with other sensing technologies. Bacterial whole-cell biosensors (BWCBs) can have wide-ranging functions from detection only, to detection and recording, to closed-loop detection-regulated treatment. In this review article, we first summarize the unique benefits of bacteria as living sensors. We then describe the different bacteria-based diagnosis approaches and provide examples of diagnosing various diseases and disorders. We also discuss the use of bacteria as imaging vectors for disease detection and image-guided surgery. We conclude by highlighting current challenges and opportunities for further exploration toward clinical translation of these bacteria-based systems.

Comparison of positional artifacts in myocardial perfusion imaging in supine and semi-reclining position using dedicated D-SPECT cardiac camera: validation using CT based attenuation correction

BACKGROUND

Soft-tissue attenuation remains a major limitation of SPECT-MPI which interferes with the diagnosis of CAD. The current study aims to evaluate the pattern of attenuation artifacts in supine and semi-reclining positions on CZT cardiac camera and their interaction with gender, BMI and stress protocols.

METHODS

We prospectively analysed 150 patients acquired in supine and semi-reclining positions on CZT camera. The images were evaluated for severity and extent of defect using 17-segment model. An additional CT scan was acquired to generate AC image in the first 50 patients studied to assist investigator learning for comparison of artifact vs true defects in the two SPECT systems. The defects present in one position or showing change in severity within two positions were considered as positional artifacts and further validated using CTAC supine image.

RESULTS

In overall analysis, higher extent and severity of positional artifacts were observed more in semi-reclining position affecting the apex, apico-inferior, inferolateral and inferoseptal segments. Females showed more positional artifacts than males with inferior wall attenuation in the semireclining position and anterior wall attenuation in the supine position. A positive correlation of the extent and severity of positional artifacts was noted with an increasing BMI. In patients with BMI > 30, mid inferior and inferolateral segments were most affected followed by anterior wall segments. Highest correction of artifactual perfusion defects by CTAC was noted in inferior wall followed by inferolateral segments.

CONCLUSION

The incidence of positional artifacts was greater in semi-reclining position in females, higher BMI groups and adenosine stress subsets. Knowledge of the pattern of positional artifacts appears to be a reliable alternative of CTAC for correct interpretation of myocardial perfusion images.

PET assessment of acute gastrointestinal graft versus host disease

Acute gastrointestinal graft versus host disease (GI-GVHD) is a common complication following allogeneic haematopoietic cell transplantation (HCT), and is characterised by severe morbidity, frequent treatment-refractoriness, and high mortality. Early, accurate identification of GI-GVHD could allow for therapeutic interventions to ameliorate its severity, improve response rates and survival; however, standard endoscopic biopsy is inadequately informative in terms of diagnostic sensitivity or outcome prediction. In an era where rapid technological and laboratory advances have dramatically expanded our understanding of GI-GVHD biology and potential therapeutic targets, there is substantial scope for novel investigations that can precisely guide GI-GVHD management. In particular, the combination of tissue-based biomarker assessment (plasma cytokines, faecal microbiome) and molecular imaging by positron emission tomography (PET) offers the potential for non-invasive, real-time in vivo assessment of donor:recipient immune activity within the GI tract for GI-GVHD prediction or diagnosis. In this article, we review the evidence regarding GI-GVHD diagnosis, and examine the potential roles and translational opportunities posed by these novel diagnostic tools, with a focus on the evolving role of PET.

Liquid metal microspheres with an eddy-thermal effect for magnetic hyperthermia-enhanced cancer embolization-immunotherapy

Patients with hepatocellular carcinoma (HCC) display poor prognosis because HCC involves a high rate of metastasis and regrowth. Herein, we present an effective strategy to treat HCC using magnetic hyperthermia therapy (MHT)-enhanced cancer immunotherapy combined with transcatheter arterial embolization (TAE). Uniform liquid metal microspheres (LM MSs) obtained by microfluidic technology with powerful eddy-thermal effects could be used as both MHT and TAE agents for effective cancer therapy. The eddy-thermal effect of LM MSs demonstrated effective MHT, whereas LM MS-induced MHT boosted the immune system, promoted immune cell infiltration, and further stimulated powerful immune responses to suppress the growth of distant tumors, together with immune checkpoint blockade therapy. Furthermore, LM MS-lipiodol dispersion displayed excellent efficacy of the combined MHT-TAE in the orthotopic rabbit liver cancer model. Our work not only highlighted that LM MSs could act as effective MHT agents to achieve MHT-enhanced immunotherapy but also presented the significant promise of combining MHT with TAE for the efficient treatment of large orthotopic liver tumors.

A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer

INTRODUCTION

We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC).

METHODS

Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were selected by logistic regression analysis. The performances of seven ML algorithms to predict high TMB were compared by the area under the receiver's operating characteristic curves (AUCs) and validated by five-fold cross-validation. A PET radiomic score was calculated by averaging the z-score of each radiomic feature. The prognostic power of the PET radiomic score was assessed using Cox proportional hazards regression analysis.

RESULTS

Ten significant radiomic features associated with TMB were selected: surface-to-volume ratio, total lesion glycolysis, tumor volume, area, compacity, complexity, entropy, correlation, coarseness, and zone size non-uniformity. The k-nearest neighbors model obtained the good performance for prediction of high TMB (AUC: 0.791, accuracy: 0.814, sensitivity: 0.619, specificity: 0.871). On multivariable Cox regression analysis, the PET radiomic score (Hazard ratio = 4.498, 95% confidential interval = 1.024-19.759; p = 0.046) was a significant independent prognostic factor for OS.

CONCLUSIONS

This study demonstrates that PET-based radiomics are useful image biomarkers for the prediction of TMB status in stage IV CRC. PET radiomic score, which integrates significant radiomic features, has the potential to predict survival in stage IV CRC patients.

Dietary Manipulation of Amino Acids for Cancer Therapy

Cancer cells cannot proliferate and survive unless they obtain sufficient levels of the 20 proteinogenic amino acids (AAs). Unlike normal cells, cancer cells have genetic and metabolic alterations that may limit their capacity to obtain adequate levels of the 20 AAs in challenging metabolic environments. However, since normal diets provide all AAs at relatively constant levels and ratios, these potentially lethal genetic and metabolic defects are eventually harmless to cancer cells. If we temporarily replace the normal diet of cancer patients with artificial diets in which the levels of specific AAs are manipulated, cancer cells may be unable to proliferate and survive. This article reviews in vivo studies that have evaluated the antitumor activity of diets restricted in or supplemented with the 20 proteinogenic AAs, individually and in combination. It also reviews our recent studies that show that manipulating the levels of several AAs simultaneously can lead to marked survival improvements in mice with metastatic cancers.

Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer

BACKGROUND

Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.

METHODS

Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.

RESULTS

The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.

CONCLUSION

The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.

Added value of digital FDG-PET/CT in disease staging and restaging in patients with resectable or borderline resectable pancreatic cancer

BACKGROUND

We studied the added value of digital FDG-PET/CT in disease staging and restaging compared to the standard work-up with contrast enhanced CT (ceCT) and CA19-9 in patients with resectable or borderline resectable pancreatic cancer who received neo-adjuvant therapy. Primary endpoints were tumor response compared to ceCT and CA19.9 as well as the ability to detect distant metastatic disease.

METHODS

35 patients were included in this dual-center prospective study. FDG-PET using digital photon counting technology combined with CT scans were acquired before (T1) and after neo-adjuvant therapy (T2). Patients were staged and restaged based on standard protocol with ceCT and CA 19.9, while all PET/CT scans were stored securely and not included in clinical decision making. After the pancreatic resection, an expert team retrospectively assessed the CT tumor diameter, CA19-9, tumor FDG-uptake, and appearance of metastatic disease of all patients for both time points.

RESULTS

CA19-9 levels, CT tumor diameter, and tumor FDG-uptake on PET significantly decreased from T1 to T2 (p = 0.017, p = 0.001, and p < 0.0001). The change in FDG-uptake values showed a strong positive correlation with the change in CT tumor diameter and change in CA19-9 (R = 0.75 and R = 0.73, respectively). In addition, small-volume liver lesions were detected on digital PET/CT in 5/35 patients (14%), 4 of which were pathology confirmed at laparotomy. Only one of these five cases was detected on baseline staging ceCT (3%).

CONCLUSION

We found that adding digital PET/CT strengthens restaging after neo-adjuvant therapy based on the observed strong correlation with ceCT tumor diameter and Ca19.9. Also, digital PET/CT was found to detect occult metastatic disease not visualized on ceCT, that would have resulted in altered disease staging and therapeutic strategy in a substantial proportion of patients.

A Small Sugar Molecule with Huge Potential in Targeted Cancer Therapy

The number of cancer-related diseases is still growing. Despite the availability of a large number of anticancer drugs, the ideal drug is still being sought that would be effective, selective, and overcome the effect of multidrug resistance. Therefore, researchers are still looking for ways to improve the properties of already-used chemotherapeutics. One of the possibilities is the development of targeted therapies. The use of prodrugs that release the bioactive substance only under the influence of factors characteristic of the tumor microenvironment makes it possible to deliver the drug precisely to the cancer cells. Obtaining such compounds is possible by coupling a therapeutic agent with a ligand targeting receptors, to which the attached ligand shows affinity and is overexpressed in cancer cells. Another way is to encapsulate the drug in a carrier that is stable in physiological conditions and sensitive to conditions of the tumor microenvironment. Such a carrier can be directed by attaching to it a ligand recognized by receptors typical of tumor cells. Sugars seem to be ideal ligands for obtaining prodrugs targeted at receptors overexpressed in cancer cells. They can also be ligands modifying polymers' drug carriers. Furthermore, polysaccharides can act as selective nanocarriers for numerous chemotherapeutics. The proof of this thesis is the huge number of papers devoted to their use for modification or targeted transport of anticancer compounds. In this work, selected examples of broad-defined sugars application for improving the properties of both already-used drugs and substances exhibiting anticancer activity are presented.

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.

Cisplatin conjugation with an exopolysaccharide extracted from Lactobacillus gasseri potentiates its efficacy and attenuates its toxicity

The development of newer cisplatin analogs is constantly being investigated owing to its low solubility, poor pharmacokinetics, and dose-related toxicity. In order to address the limitations of current cisplatin therapy, the present study was undertaken. Cisplatin conjugation with an exopolysaccharide extracted from Lactobacillus gasseri (LG-EPS) showed remarkably enhanced and selective anticancer activity by targeting tumor cells overexpressing glucose transporter 1 (GLUT1). The EPS-cisplatin complex exhibited a 600-fold increase in aqueous solubility with a better pharmacokinetic profile (longer half-life) in comparison to cisplatin. Cell viability assay and western blotting demonstrated a strong correlation between the cytotoxicity profile and GLUT1 expressions in different cell lines. The concentration of DNA-bound platinum was also found to be significantly higher in EPS-cisplatin-treated cells. Quercetin, a competitive inhibitor of GLUTs, was shown to prevent this selective uptake of EPS-cisplatin complex. Surprisingly, EPS-cisplatin complex showed an exceptionally safer profile (4 times the maximum tolerated dose of cisplatin) in the acute toxicity study and was also more efficacious against the xenograft mice model. The study suggests that this green glycoconjugation can be an effective and safer strategy to broaden the therapeutic potential of anti-cancer drugs in general and cisplatin in particular.

Assessment of Brown and Beige Adipose Tissue Activation in Mice Using PET/CT Imaging

In response to cold induction, brown adipose tissues (BAT) and emerged brown-like adipocytes (beige adipocytes) in subcutaneous white adipose tissues (WAT browning/beiging) are activated. Thermogenesis is increased during glucose and fatty acid uptake and metabolism in adult humans and mice. This activation of BAT or WAT beiging to generate heat helps to counteract diet-induced obesity. This protocol applies the glucose analog radiotracer ¹⁸F-fluorodeoxyglucose (FDG), coupled with positron emission tomography and computed tomography (PET/CT) scanning to evaluate cold-induced thermogenesis in the active BAT (interscapular region) and browned/beiged WAT (subcutaneous adipose region) in mice. The PET/CT scanning technique not only can quantify cold-induced glucose uptake in well-known BAT and beige-fat depots but also helps to visualize the anatomical location of novel uncharacterized mouse BAT and beige fat where cold-induced glucose uptake is high. Histological analysis is further employed to validate signals of delineated anatomical regions in PET/CT images as bona fide mouse BAT or beiged WAT fat depots.

PPP1R26 drives hepatocellular carcinoma progression by controlling glycolysis and epithelial-mesenchymal transition

Background

Hepatocellular carcinoma (HCC) is usually diagnosed at an advanced stage due to rapid progression. Glycolysis supports anabolic growth and metastasis to promote HCC progression. However, the molecular mechanisms linking glycolysis and metastasis in HCC are not completely defined.

Methods

The expression of PPP1R26 in human HCC tissues was evaluated by immunohistochemistry, and the clinical significance of PPP1R26 in the progression and prognosis of the HCC patients were analyzed. The PPP1R26-binding proteins were determined by mass spectrometry analysis. The function of PPP1R26 in glycolysis, EMT and tumorigenesis were evaluated in HCC cells. Glucose uptake and tumor growth were evaluated using PET imaging in mouse xenografts in vivo. Protein binding was confirmed by co-immunoprecipitation and immunofluorescence co-localization. Protein-RNA binding was determined by RNA-immunoprecipitation (RIP) experiment. The binding of protein on the promoter was evaluated by chromatin immunoprecipitation assay (ChIP).

Results

PPP1R26 is upregulated in human HCC tissues and its upregulation is significantly associated with metastasis and the poor survival of the patients. PPP1R26 activates glycolysis in HCC cells and in mouse xenografts in vivo. PPP1R26 drives glycolysis by binding to PTBP1 to facilitate the mRNA splicing of PKM2. Simultaneously, overexpressed PPP1R26 induces the nuclear accumulation of PKM2 to inhibit the expression of E-cadherin further to drive EMT. Mechanistically, PPP1R26 binds with Ser37-phosphorylated PKM2 and TGIF2 in the nucleus and blocks the binding of TGIF2 with CDH1 promoter to inhibit the transcription of CDH1.

Conclusion

PPP1R26 promotes glycolysis by enhancing PKM2 splicing and simultaneously activates EMT by forming a PPP1R26-PKM2-TGIF2 complex to drive HCC progression. Therefore, targeting PPP1R26 attenuates HCC progression and provides a potential therapeutic strategy for the HCC patients with upregulation of PPP1R26.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02302-8.

A Novel Glycolysis-Related Long Noncoding RNA Signature for Predicting Overall Survival in Gastric Cancer

Background: The aim of this study was to construct a glycolysis-related long noncoding RNA (lncRNA) signature to predict the prognosis of patients with gastric cancer (GC). Methods: Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB), lncRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas database (TCGA). Furthermore, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to construct prognostic glycolysis-related lncRNA signature. The specificity and sensitivity of the signature was verified by receiver operating characteristic (ROC) curves. We constructed a nomogram to predict the 1-year, 3-year, and 5-year survival rates of GC patients. Besides, the relationship between immune infiltration and the risk score was analyzed in the high and low risk groups. Multi Experiment Matrix (MEM) was used to analyze glycolysis-related lncRNA target genes. R "limma" package was used to analyze the mRNA expression levels of the glycolysis-related lncRNA target genes in TCGA. Gene set enrichment analysis (GSEA) was employed to further explore the biological pathways in the high-risk group and the glycolysis-related lncRNA target gene. Results: A prognostic signature was conducted based on nine glycolysis-related lncRNAs, which are AL391152.1, AL590705.3, RHOXF1-AS1, CFAP61-AS1, LINC00412, AC005165.1, AC110995.1, AL355574.1 and SCAT1. The area under the ROC curve (AUC) values at 1-year, 3-year, and 5-year were 0.765, 0.828 and 0.707 in the training set, and 0.669, 740 and 0.807 in the testing set, respectively. In addition, the nomogram could efficaciously predict the 1-year, 3-year, and 5-year survival rates of the GC patients. Then, we discovered that GC patients with high-risk scores were more likely to respond to immunotherapy. GSEA revealed that the signature was mainly associated with the calcium signaling pathway, extracellular matrix (ECM) receptor interaction, and focal adhesion in high-risk group, also indicated that SBSPON is related to aminoacyl-tRNA biosynthesis, citrate cycle, fructose and mannose metabolism, pentose phosphate pathway and pyrimidine metabolism. Conclusion: Our study shows that the signature can predict the prognosis of GC and may provide new insights into immunotherapeutic strategies.

Integrating 18 F-FDG PET/CT with lung dose-volume for assessing lung inflammatory changes after arc-based radiotherapy for esophageal cancer: A pilot study

OBJECTIVE

The incidence of radiation pneumonitis (RP) has a highly linear relationship with low-dose lung volume. We previously established a volume-based algorithm (VBA) method to improve low-dose lung volume in radiotherapy (RT). This study assessed lung inflammatory changes by integrating fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸ F-FDG PET/CT) with VBA for esophageal cancer patients undergoing arc-based RT.

METHODS

Thirty esophageal cancer patients received ¹⁸ F-FDG PET/CT imaging pre-RT and post-RT were included in a retrospective pilot study. We fused lung doses and parameters of PET/CT in RT planning. Based on VBA, we used the 5Gy isodose curve to define high-dose (HD) and low-dose (LD) regions in the lung volume. We divided patients into non-RP (nRP) and RP groups. The maximum, mean standardized uptake value (SUVmax, SUVmean), global lung glycolysis (GLG), mean lung dose (MLD) and V_5-30 in lungs were analyzed. Area under the curve values were utilized to identify optimal cut-off values for RP.

RESULTS

Eleven patients in the nRP group and 19 patients in the RP group were identified. In 30 RP lungs, post-RT SUVmax, SUVmean and GLG of HD regions showed significant increases compared to values for pre-RT lungs. There were no significant differences in values of 22 nRP lungs. Post-RT SUVmax and SUVmean of HD regions, MLD, and lung V₅ and V₁₀ in RP lungs were significantly higher than in nRP lungs. For detecting RP, the optimal cut-off values were post-RT SUVmax > 2.28 and lung V₅  > 47.14%.

CONCLUSION

This study successfully integrated ¹⁸ F-FDG PET/CT with VBA to assess RP in esophageal cancer patients undergoing RT. Post-RT SUVmax > 2.28 and lung V₅  > 47.14% might be potential indicators of RP.

Near-Infrared Optical Transducer for Dynamic Imaging of Cerebrospinal Fluid Glucose in Brain Tumor

Aberrant cerebral glucose metabolism is related to many brain diseases, especially brain tumor. However, it remains challenging to measure the dynamic changes in cerebral glucose. Here, we developed a near-infrared (NIR) optical transducer to sensitively monitor the glucose variations in cerebrospinal fluid in vivo. The transducer consists of an oxygen-sensitive nanoparticle combined with glucose oxidase (GOx), yielding highly sensitive NIR phosphorescence in response to blood glucose change. We demonstrated long-term continuous glucose monitoring by using the NIR transducer. After subcutaneous implantation, the glucose transducer provides a strong luminescence signal that can continuously monitor blood glucose fluctuations for weeks. By using the NIR emission of the transducer, we further observed abnormal dynamic changes in cerebrospinal fluid glucose and quantitatively assessed cerebral glucose uptake rates in transgenic mice bearing brain tumors. This study provides a promising method for the diagnosis of various metabolic diseases with altered glucose metabolism.

Targeting Glucose Metabolism Enzymes in Cancer Treatment: Current and Emerging Strategies

Simple Summary

Reprogramming of glucose metabolism is a hallmark of cancer and can be targeted by therapeutic agents. Some metabolism regulators, such as ivosidenib and enasidenib, have been approved for cancer treatment. Currently, more advanced and effective glucose metabolism enzyme-targeted anticancer drugs have been developed. Furthermore, some natural products have shown efficacy in killing tumor cells by regulating glucose metabolism, offering novel therapeutic opportunities in cancer. However, most of them have failed to be translated into clinical applications due to low selectivity, high toxicity, and side effects. Recent studies suggest that combining glucose metabolism modulators with chemotherapeutic drugs, immunotherapeutic drugs, and other conventional anticancer drugs may be a future direction for cancer treatment.

Abstract

Reprogramming of glucose metabolism provides sufficient energy and raw materials for the proliferation, metastasis, and immune escape of cancer cells, which is enabled by glucose metabolism-related enzymes that are abundantly expressed in a broad range of cancers. Therefore, targeting glucose metabolism enzymes has emerged as a promising strategy for anticancer drug development. Although several glucose metabolism modulators have been approved for cancer treatment in recent years, some limitations exist, such as a short half-life, poor solubility, and numerous adverse effects. With the rapid development of medicinal chemicals, more advanced and effective glucose metabolism enzyme-targeted anticancer drugs have been developed. Additionally, several studies have found that some natural products can suppress cancer progression by regulating glucose metabolism enzymes. In this review, we summarize the mechanisms underlying the reprogramming of glucose metabolism and present enzymes that could serve as therapeutic targets. In addition, we systematically review the existing drugs targeting glucose metabolism enzymes, including small-molecule modulators and natural products. Finally, the opportunities and challenges for glucose metabolism enzyme-targeted anticancer drugs are also discussed. In conclusion, combining glucose metabolism modulators with conventional anticancer drugs may be a promising cancer treatment strategy.

Metabolic targeting of malignant tumors: a need for systemic approach

PURPOSE

Dysregulated metabolism is now recognized as a fundamental hallmark of carcinogenesis inducing aggressive features and additional hallmarks. In this review, well-established metabolic changes displayed by tumors are highlighted in a comprehensive manner and corresponding therapeutical targets are discussed to set up a framework for integrating basic research findings with clinical translation in oncology setting.

METHODS

Recent manuscripts of high research impact and relevant to the field from PubMed (2000-2021) have been reviewed for this article.

RESULTS

Metabolic pathway disruption during tumor evolution is a dynamic process potentiating cell survival, dormancy, proliferation and invasion even under dismal conditions. Apart from cancer cells, though, tumor microenvironment has an acting role as extracellular metabolites, pH alterations and stromal cells reciprocally interact with malignant cells, ultimately dictating tumor-promoting responses, disabling anti-tumor immunity and promoting resistance to treatments.

CONCLUSION

In the field of cancer metabolism, there are several emerging prognostic and therapeutic targets either in the form of gene expression, enzyme activity or metabolites which could be exploited for clinical purposes; both standard-of-care and novel treatments may be evaluated in the context of metabolism rewiring and indeed, synergistic effects between metabolism-targeting and other therapies would be an attractive perspective for further research.

Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[18F]FDG-PET/CT

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[¹⁸F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[¹⁸F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUV_max). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUV_max was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUV_max was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUV_max and a significantly improved conspicuity of small 2-[¹⁸F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[¹⁸F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

Formation of Water-Soluble Complexes from Fullerene with Biocompatible Block Copolymers Bearing Pendant Glucose and Phosphorylcholine

Double-hydrophilic diblock copolymers, PMPC₁₀₀-block-PGEMA_n (M₁₀₀G_n), were synthesized via reversible addition-fragmentation chain transfer radical polymerization using glycosyloxyethyl methacrylate and 2-(methacryloyloxy)ethyl phosphorylcholine. The degree of polymerization (DP) of the poly(2-(methacryloyloxy) ethylphosphorylcholine) (PMPC) block was 100, whereas the DPs (n) of the poly(glycosyloxyethyl methacrylate) PGEMA block were 18, 48, and 90. Water-soluble complexes of C₇₀/M₁₀₀G_n and fullerene (C₇₀) were prepared by grinding M₁₀₀G_n and C₇₀ powders in a mortar and adding phosphate-buffered saline (PBS) solution. PMPC can form a water-soluble complex with hydrophobic C₇₀ using the same method. Therefore, the C₇₀/M₁₀₀G_n complexes have a core-shell micelle-like particle structure possessing a C₇₀/PMPC core and PGEMA shells. The maximum amounts of solubilization of C₇₀ in PBS solutions using 2 g/L each of M₁₀₀G₁₈, M₁₀₀G₄₈, and M₁₀₀G₉₀ were 0.518, 0.358, and 0.257 g/L, respectively. The hydrodynamic radius (R_h) of C₇₀/M₁₀₀G_n in PBS solutions was 55-75 nm. Spherical aggregates with a similar size to the R_h were observed by transmission electron microscopy. When the C₇₀/M₁₀₀G_n PBS solutions were irradiated with visible light, singlet oxygen was generated from C₇₀ in the core. It is expected that the C₇₀/M₁₀₀G_n complexes can be applied to photosensitizers for photodynamic therapy treatments.

Challenges and opportunities in the development of metal-based anticancer theranostic agents

Around 10 million fatalities were recorded worldwide in 2020 due to cancer and statistical projections estimate the number to increase by 60% in 2040. With such a substantial rise in the global cancer burden, the disease will continue to impose a huge socio-economic burden on society. Currently, the most widely used clinical treatment modality is cytotoxic chemotherapy using platinum drugs which is used to treat variety of cancers. Despite its clinical success, critical challenges like resistance, off-target side effects and cancer variability often reduce its overall therapeutic efficiency. These challenges require faster diagnosis, simultaneous therapy and a more personalized approach toward cancer management. To this end, small-molecule ‘theranostic’ agents have presented a viable solution combining diagnosis and therapy into a single platform. In this review, we present a summary of recent efforts in the design and optimization of metal-based small-molecule ‘theranostic’ anticancer agents. Importantly, we highlight the advantages of a theranostic candidate over the purely therapeutic or diagnostic agent in terms of evaluation of its biological properties.

Reproducibility of Standardized Uptake Values Including Volume Metrics Between TOF-PET-MR and TOF-PET-CT

Purpose

To investigate the reproducibility of tracer uptake measurements, including volume metrics, such as metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) obtained by TOF-PET-CT and TOF-PET-MR.

Materials and Methods

Eighty consecutive patients with different oncologic diagnoses underwent TOF-PET-CT (Discovery 690; GE Healthcare) and TOF-PET-MR (SIGNA PET-MR; GE Healthcare) on the same day with single dose−18F-FDG injection. The scan order, PET-CT following or followed by PET-MR, was randomly assigned. A spherical volume of interest (VOI) of 30 mm was placed on the liver in accordance with the PERCIST criteria. For liver, the maximum and mean standard uptake value for body weight (SUV) and lean body mass (SUL) were obtained. For tumor delineation, VOI with a threshold of 40 and 50% of SUVmax was used (VOI40 and VOI50). The SUVmax, SUVmean, SUVpeak, MTV and TLG were calculated. The measurements were compared between the two scanners.

Results

In total, 80 tumor lesions from 35 patients were evaluated. There was no statistical difference observed in liver regions, whereas in tumor lesions, SUVmax, SUV mean, and SUVpeak of PET-MR were significantly underestimated (p < 0.001) in both VOI40 and VOI50. Among volume metrics, there was no statistical difference observed except TLG on VOI50 (p = 0.03). Correlation between PET-CT and PET-MR of each metrics were calculated. There was a moderate correlation of the liver SUV and SUL metrics (r = 0.63–0.78). In tumor lesions, SUVmax and SUVmean had a stronger correlation with underestimation in PET-MR on VOI 40 (SUVmax and SUVmean; r = 0.92 and 0.91 with slope = 0.71 and 0.72, respectively). In the evaluation of MTV and TLG, the stronger correlations were observed both on VOI40 (MTV and TLG; r = 0.75 and 0.92) and VOI50 (MTV and TLG; r = 0.88 and 0.95) between PET-CT and PET-MR.

Conclusion

PET metrics on TOF-PET-MR showed a good correlation with that of TOF-PET-CT. SUVmax and SUVpeak of tumor lesions were underestimated by 16% on PET-MRI. MTV with % threshold can be regarded as identical volumetric markers for both TOF-PET-CT and TOF-PET-MR.

Imaging pheochromocytoma in small animals: preclinical models to improve diagnosis and treatment

Pheochromocytomas (PCCs) and paragangliomas (PGLs), together referred to as PPGLs, are rare chromaffin cell-derived tumors. They require timely diagnosis as this is the only way to achieve a cure through surgery and because of the potentially serious cardiovascular complications and sometimes life-threatening comorbidities that can occur if left untreated. The biochemical diagnosis of PPGLs has improved over the last decades, and the knowledge of the underlying genetics has dramatically increased. In addition to conventional anatomical imaging by CT and MRI for PPGL detection, new functional imaging modalities have emerged as very useful for patient surveillance and stratification for therapy. The availability of validated and predictive animal models of cancer is essential for translating molecular, imaging and therapy response findings from the bench to the bedside. This is especially true for rare tumors, such as PPGLs, for which access to large cohorts of patients is limited. There are few animal models of PPGLs that have been instrumental in refining imaging modalities for early tumor detection, as well as in identifying and evaluating novel imaging tracers holding promise for the detection and/or treatment of human PPGLs. The in vivo PPGL models mainly include xenografts/allografts generated by engrafting rat or mouse cell lines, as no representative human cell line is available. In addition, there is a model of endogenous PCCs (i.e., MENX rats) that was characterized in our laboratory. In this review, we will summarize the contribution that various representative models of PPGL have given to the visualization of these tumors in vivo and we present an example of a tracer first evaluated in MENX rats, and then translated to the detection of these tumors in human patients. In addition, we will illustrate briefly the potential of ex vivo biological imaging of intact adrenal glands in MENX rats.

Exploring Carbohydrates for Therapeutics: A Review on Future Directions

Carbohydrates are important components of foods and essential biomolecules performing various biological functions in living systems. A variety of biological activities besides providing fuel have been explored and reported for carbohydrates. Some carbohydrates have been approved for the treatment of various diseases; however, carbohydrate-containing drugs represent only a small portion of all of the drugs on the market. This review summarizes several potential development directions of carbohydrate-containing therapeutics, with the hope of promoting the application of carbohydrates in drug development.

Overview and Future Perspectives on Tumor-Targeted Positron Emission Tomography and Fluorescence Imaging of Pancreatic Cancer in the Era of Neoadjuvant Therapy

BACKGROUND

Despite recent advances in the multimodal treatment of pancreatic ductal adenocarcinoma (PDAC), overall survival remains poor with a 5-year cumulative survival of approximately 10%. Neoadjuvant (chemo- and/or radio-) therapy is increasingly incorporated in treatment strategies for patients with (borderline) resectable and locally advanced disease. Neoadjuvant therapy aims to improve radical resection rates by reducing tumor mass and (partial) encasement of important vascular structures, as well as eradicating occult micrometastases. Results from recent multicenter clinical trials evaluating this approach demonstrate prolonged survival and increased complete surgical resection rates (R0). Currently, tumor response to neoadjuvant therapy is monitored using computed tomography (CT) following the RECIST 1.1 criteria. Accurate assessment of neoadjuvant treatment response and tumor resectability is considered a major challenge, as current conventional imaging modalities provide limited accuracy and specificity for discrimination between necrosis, fibrosis, and remaining vital tumor tissue. As a consequence, resections with tumor-positive margins and subsequent early locoregional tumor recurrences are observed in a substantial number of patients following surgical resection with curative intent. Of these patients, up to 80% are diagnosed with recurrent disease after a median disease-free interval of merely 8 months. These numbers underline the urgent need to improve imaging modalities for more accurate assessment of therapy response and subsequent re-staging of disease, thereby aiming to optimize individual patient's treatment strategy. In cases of curative intent resection, additional intra-operative real-time guidance could aid surgeons during complex procedures and potentially reduce the rate of incomplete resections and early (locoregional) tumor recurrences. In recent years intraoperative imaging in cancer has made a shift towards tumor-specific molecular targeting. Several important molecular targets have been identified that show overexpression in PDAC, for example: CA19.9, CEA, EGFR, VEGFR/VEGF-A, uPA/uPAR, and various integrins. Tumor-targeted PET/CT combined with intraoperative fluorescence imaging, could provide valuable information for tumor detection and staging, therapy response evaluation with re-staging of disease and intraoperative guidance during surgical resection of PDAC.

METHODS

A literature search in the PubMed database and (inter)national trial registers was conducted, focusing on studies published over the last 15 years. Data and information of eligible articles regarding PET/CT as well as fluorescence imaging in PDAC were reviewed. Areas covered: This review covers the current strategies, obstacles, challenges, and developments in targeted tumor imaging, focusing on the feasibility and value of PET/CT and fluorescence imaging for integration in the work-up and treatment of PDAC. An overview is given of identified targets and their characteristics, as well as the available literature of conducted and ongoing clinical and preclinical trials evaluating PDAC-targeted nuclear and fluorescent tracers.

A systematic review on application of deep learning in digestive system image processing

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence

PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-time limit of system components leads to the loss of the count rate; the scattered and random events received by the detector introduce additional noise; the characteristics of the detector limit the spatial resolution; and the low signal-to-noise ratio caused by the scan-time limit (eg, dynamic scans) and dose concern. The early PET reconstruction methods are analytical approaches based on an idealized mathematical model.

The Evolving Role of 18F-FDG PET/CT in Diagnosis and Prognosis Prediction in Progressive Prostate Cancer

Positron emission tomography/computed tomography (PET/CT) is widely used in prostate cancer to evaluate the localized tumor burden and detect symptomatic metastatic lesions early. ¹⁸F-FDG is the most used tracer for oncologic imaging, but it has limitations in detecting early-stage prostate cancer. ⁶⁸Ga-PSMA is a new tracer that has high specificity and sensibility in detecting local and metastatic tumors. But with the progression of prostate cancer, the enhancement of glucose metabolism in progressive prostate cancer provides a chance for ¹⁸F-FDG. This review focuses on PET/CT in the detection and prognosis of prostate cancer, summarizing the literature on ¹⁸F-FDG and ⁶⁸Ga-PSMA in prostate cancer and highlighting that ¹⁸F-FDG has advantages in detecting local recurrence, visceral and lymph node metastases compared to ⁶⁸Ga-PSMA in partial progressive prostate cancer and castration-resistant prostate cancer patients. We emphasize ¹⁸F-FDG PET/CT can compensate for the weakness of ⁶⁸Ga-PSMA PET/CT in progressive prostate cancer.

Circular RNAs Regulate Glucose Metabolism in Cancer Cells

Circular RNAs (circRNAs) were originally thought to result from RNA splicing errors. However, it has been shown that circRNAs can regulate cancer onset and progression in various ways. They can regulate cancer cell proliferation, differentiation, invasion, and metastasis. Moreover, they modulate glucose metabolism in cancer cells through different mechanisms such as directly regulating glycolytic enzymes and glucose transporter (GLUT) or indirectly regulating signal transduction pathways. In this review, we elucidate on the role of circRNAs in regulating glucose metabolism in cancer cells, which partly explains the pathogenesis of malignant tumors, and provides new therapeutic targets or new diagnostic and prognostic markers for human cancers.

Correlation Between 18F-FDG Uptake and Immune Cell Infiltration in Metastatic Brain Lesions

Background

The purpose of this study was to investigate the correlation between ¹⁸F-fluorodeoxyglucose (FDG) uptake and infiltrating immune cells in metastatic brain lesions.

Methods

This retrospective study included 34 patients with metastatic brain lesions who underwent brain ¹⁸F-FDG positron emission tomography (PET)/computed tomography (CT) followed by surgery. ¹⁸F-FDG uptake ratio was calculated by dividing the standardized uptake value (SUV) of the metastatic brain lesion by the contralateral normal white matter uptake value. We investigated the clinicopathological characteristics of the patients and analyzed the correlation between ¹⁸F-FDG uptake and infiltration of various immune cells. In addition, we evaluated immune-expression levels of glucose transporter 1 (GLUT1), hexokinase 2 (HK2), and Ki-67 in metastatic brain lesions.

Results

The degree of ¹⁸F-FDG uptake of metastatic brain lesions was not significantly correlated with clinical parameters. There was no significant relationship between the ¹⁸F-FDG uptake and degree of immune cell infiltration in brain metastasis. Furthermore, other markers, such as GLUT1, HK2, and Ki-67, were not correlated with degree of ¹⁸F-FDG uptake. In metastatic brain lesions that originated from breast cancer, a higher degree of ¹⁸F-FDG uptake was observed in those with high expression of CD68.

Conclusions

In metastatic brain lesions, the degree of ¹⁸F-FDG uptake was not significantly associated with infiltration of immune cells. The ¹⁸F-FDG uptake of metastatic brain lesions from breast cancer, however, might be associated with macrophage activity.

Application of carbohydrates in approved small molecule drugs: A review

Carbohydrates are an important energy source and play numerous key roles in all living organisms. Carbohydrates chemistry involved in diagnosis and treatment of diseases has been attracting increasing attention. Carbohydrates could be one of the major focuses of new drug discovery. Currently, however, carbohydrate-containing drugs account for only a small percentage of all drugs in clinical use, which does not match the important roles of carbohydrates in the organism. In other words, carbohydrates are a relatively untapped source of new drugs and therefore may offer exciting novel therapeutic opportunities. Here, we presented an overview of the application of carbohydrates in approved small molecule drugs and emphasized and evaluated the roles of carbohydrates in those drugs. The potential development direction of carbohydrate-containing drugs was presented after summarizing the advantages and challenges of carbohydrates in the development of new drugs.

Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

To evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10-25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

The role of GSK3 in metabolic pathway perturbations in cancer

Malignant transformation and tumor progression are accompanied by significant perturbations in metabolic programs. As such, cancer cells support high ATP turnover to construct the building blocks needed to fuel neoplastic growth. The coordination of metabolic networks in malignant cells is dependent on the collaboration with cellular signaling pathways. Glycogen synthase kinase 3 (GSK3) lies at the convergence of several signaling axes, including the PI3K/AKT/mTOR, AMPK, and Wnt pathways, which influence cancer initiation, progression and therapeutic responses. Accordingly, GSK3 modulates metabolic processes, including protein and lipid synthesis, glucose and mitochondrial metabolism, as well as autophagy. In this review, we highlight current knowledge of the role of GSK3 in metabolic perturbations in cancer.

A Preliminary Study to Use SUVmax of FDG PET-CT as an Identifier of Lesion for Artificial Intelligence

Background: Diagnostic reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily diagnostic reports of [¹⁸F] fluorodeoxyglucose (FDG) positron emission tomography (PET) - computed tomography (CT). If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret diagnostic reports. We aimed to clarify whether the lesion can be localized using SUVmax strings. Methods: The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax = 3.14 was given, the voxels with 3.135 ≤ SUVmax < 3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that "identical detection" was achieved when only single voxel satisfied the criterion. Results: A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax < 2), 79.5% (2 ≤ SUVmax < 5), and 97.8% (5 ≤ SUVmax) of lesions. Conclusion: In this preliminary study, SUVmax of FDG PET-CT could be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax < 2 were difficult to identify. The proposed method may have potential to make use of diagnostic reports retrospectively for constructing training datasets for AI.

Metabolic Classification and Intervention Opportunities for Tumor Energy Dysfunction

A comprehensive view of cell metabolism provides a new vision of cancer, conceptualized as tissue with cellular-altered metabolism and energetic dysfunction, which can shed light on pathophysiological mechanisms. Cancer is now considered a heterogeneous ecosystem, formed by tumor cells and the microenvironment, which is molecularly, phenotypically, and metabolically reprogrammable. A wealth of evidence confirms metabolic reprogramming activity as the minimum common denominator of cancer, grouping together a wide variety of aberrations that can affect any of the different metabolic pathways involved in cell physiology. This forms the basis for a new proposed classification of cancer according to the altered metabolic pathway(s) and degree of energy dysfunction. Enhanced understanding of the metabolic reprogramming pathways of fatty acids, amino acids, carbohydrates, hypoxia, and acidosis can bring about new therapeutic intervention possibilities from a metabolic perspective of cancer.

Comparison of [18F]FDG PET/CT and MRI for Treatment Response Assessment in Multiple Myeloma: A Meta-Analysis

The present study was designed to assess the additional value of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) to magnetic resonance imaging (MRI) in the treatment response assessment of multiple myeloma (MM). We performed a meta-analysis of all available studies to compare the detectability of treatment response of [¹⁸F]FDG PET/CT and MRI in treated MM. We defined detecting a good therapeutic effect as positive, and residual disease as negative. We determined the sensitivities and specificities across studies, calculated the positive and negative likelihood ratios (LR), and made summary receiver operating characteristic curves (SROC) using hierarchical regression models. The pooled analysis included six studies that comprised 278 patients. The respective performance characteristics (95% confidence interval (CI)) of [¹⁸F]FDG PET/CT and MRI were as follows: sensitivity of 80% (56% to 94%) and 25% (19% to 31%); specificity of 58% (44% to 71%) and 83% (71% to 91%); diagnostic odds ratio (DOR) of 6.0 (3.0-12.0) and 1.7 (0.7-2.7); positive LR of 1.8 (1.3-2.4) and 1.4 (0.7-2.7); and negative LR of 0.33 (0.21-0.53) and 0.81 (0.62-1.1). In the respective SROC curves, the area under the curve was 0.77 (SE, 0.038) and 0.59 (SE, 0.079) and the Q* index was 0.71 and 0.57. Compared with MRI, [¹⁸F]FDG PET/CT had higher sensitivity and better DOR and SROC curves. Compared with MRI, [¹⁸F]FDG PET/CT had greater ability to detect the treatment assessment of MM.

Overexpression of Adiponectin Receptor 1 Inhibits Brown and Beige Adipose Tissue Activity in Mice

Adult humans and mice possess significant classical brown adipose tissues (BAT) and, upon cold-induction, acquire brown-like adipocytes in certain depots of white adipose tissues (WAT), known as beige adipose tissues or WAT browning/beiging. Activating thermogenic classical BAT or WAT beiging to generate heat limits diet-induced obesity or type-2 diabetes in mice. Adiponectin is a beneficial adipokine resisting diabetes, and causing “healthy obese” by increasing WAT expansion to limit lipotoxicity in other metabolic tissues during high-fat feeding. However, the role of its receptors, especially adiponectin receptor 1 (AdipoR1), on cold-induced thermogenesis in vivo in BAT and in WAT beiging is still elusive. Here, we established a cold-induction procedure in transgenic mice over-expressing AdipoR1 and applied a live 3-D [¹⁸F] fluorodeoxyglucose-PET/CT (¹⁸F-FDG PET/CT) scanning to measure BAT activity by determining glucose uptake in cold-acclimated transgenic mice. Results showed that cold-acclimated mice over-expressing AdipoR1 had diminished cold-induced glucose uptake, enlarged adipocyte size in BAT and in browned WAT, and reduced surface BAT/body temperature in vivo. Furthermore, decreased gene expression, related to thermogenic Ucp1, BAT-specific markers, BAT-enriched mitochondrial markers, lipolysis and fatty acid oxidation, and increased expression of whitening genes in BAT or in browned subcutaneous inguinal WAT of AdipoR1 mice are congruent with results of PET/CT scanning and surface body temperature in vivo. Moreover, differentiated brown-like beige adipocytes isolated from pre-adipocytes in subcutaneous WAT of transgenic AdipoR1 mice also had similar effects of lowered expression of thermogenic Ucp1, BAT selective markers, and BAT mitochondrial markers. Therefore, this study combines in vitro and in vivo results with live 3-D scanning and reveals one of the many facets of the adiponectin receptors in regulating energy homeostasis, especially in the involvement of cold-induced thermogenesis.

Molecular imaging in immuno-oncology: current status and translational perspectives

Introduction: Only 20-40% of patients respond to therapy with immune checkpoint inhibitors (ICIs). Therefore, the early identification of subjects that can benefit from such therapeutic regimen is mandatory. Areas covered: The immunobiological mechanisms of ICIs are briefly illustrated. Furthermore, the limitations of traditional radiological approaches are covered. Then, the pros and cons of molecular imaging through positron emission computed tomography (PET/CT) are reviewed, with a particular focus on ¹⁸f-fluorodeoxyglucose (¹⁸F-FDG) and PET-derived metabolic parameters. Lastly, translational perspective of radiopharmaceuticals others than ¹⁸F-FDG such as ⁸⁹zirconium (⁸⁹Zr) or fluorine-18 (¹⁸F) labeled monoclonal antibodies (e.g.⁸⁹Zr-atezolizumab, ⁸⁹Zr-nivolumab) binding to specific biomarkers are discussed. Expert opinion: Molecular imaging presents a prominent role for the management of oncological patients treated with ICIs. Preliminary clinical data indicate that PET/CT with ¹⁸F-FDG is useful for assessing the response to treatment and for the imaging of immune-related adverse effects. Nevertheless, the methodological approach (iPERCIST, PERCIMT, or others) to be used for an optimal diagnostic accuracy and patients' evaluation is still a debated issue. PET/CT with radioligands directed toward ICIs biomarkers, although is still in a translational phase, holds the promise of accurately predicting the response to treatment and revealing the acquired resistance to immunotherapy.

Total-body PET Imaging: A New Frontier for the Assessment of Metabolic Disease and Obesity

Obesity and associated metabolic syndrome are a global public health issue. Understanding the pathophysiology of this systemic disease is of critical importance for the development of future therapeutic interventions to improve clinical outcomes. The multiorgan nature of the pathophysiology of obesity presents a unique challenge. Total-body PET imaging, either static or dynamic, provides a vital set of tools to study organ crosstalk. The visualization and quantification of tissue metabolic kinetics with total-body PET in health and disease provides essential information to better understand disease physiology and potentially develop diagnostic and therapeutic modalities.

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application

Purpose

To develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting ¹⁸FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT).

Methods

DDD-PIOP uses pre-radiotherapy ¹⁸FDG-PET/CT images and the planned spatial dose distribution as the inputs, and it predicts the ¹⁸FDG-PET image outcomes in response to the planned IMRT delivery. This AI agent centralizes a customized convolutional neural network (CNN) as a deep learning approach, and it incorporates a few designs to enhance prediction accuracy. 66 OPC patients who received IMRT treatment on a sequential boost regime (2 Gy/daily fraction) were studied for DDD-PIOP development. 61 patients were used for AI agent training/validation, and the remaining five were used as independent tests. To evaluate the developed AI agent’s performance, the predicted mean standardized uptake values (SUVs) of gross tumor volume (GTV) and clinical target volume (CTV) were compared with the ground truth values. Overall SUV distribution accuracy was evaluated by gamma test passing rates under different criteria.

Results

The developed DDD-PIOP successfully generated ¹⁸FDG-PET image outcome predictions for five test patients. The predicted mean SUV values of GTV/CTV were 3.50/1.41, which were close to the ground-truth values of 3.57/1.51. In 2D-based gamma tests, the average passing rate was 92.1% using 5%/10 mm criteria, which was improved to 95.9%/93.2% when focusing on GTV/CTV regions. 3D gamma test passing rates were 98.7% using 5%/10 mm criteria, and the corresponding GTV/CTV results were 99.8%/99.4%.

Conclusion

The reported results suggest that the developed AI agent DDD-PIOP successfully predicted ¹⁸FDG-PET image outcomes with high quantitative accuracy. The generated voxel-based image outcome predictions could be used for treatment planning optimization prior to radiation delivery for the best individual-based outcome.

Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods

The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application in PET attenuation correction, two general strategies are reviewed: 1) generating synthetic CT from MR or non-AC PET for the purposes of PET AC, and 2) direct conversion from non-AC PET to AC PET. For low-count PET reconstruction, recent deep learning-based studies and the potential advantages over conventional machine learning-based methods are presented and discussed. In each application, the proposed methods, study designs and performance of published studies are listed and compared with a brief discussion. Finally, the overall contributions and remaining challenges are summarized.