Abstract
Purpose: To analyze and review the progress of glucose metabolism-based molecular imaging in detecting tumors to guide clinicians for new management strategies.
Summary: When metabolic abnormalities occur, termed the Warburg effect, it simultaneously enables excessive cell proliferation and inhibits cell apoptosis. Molecular imaging technology combines molecular biology and cell probe technology to visualize, characterize, and quantify processes at cellular and subcellular levels in vivo. Modern instruments, including molecular biochemistry, data processing, nanotechnology, and image processing, use molecular probes to perform real-time, non-invasive imaging of molecular and cellular events in living organisms.
Conclusion: Molecular imaging is a non-invasive method for live detection, dynamic observation, and quantitative assessment of tumor glucose metabolism. It enables in-depth examination of the connection between the tumor microenvironment and tumor growth, providing a reliable assessment technique for scientific and clinical research. This new technique will facilitate the translation of fundamental research into clinical practice.
Introduction
Metabolic reprogramming is a cancer characteristic in which cells are rewired to enable optimized neoplastic development (Madhavan and Nagarajan, 2020; Lin et al., 2022). Typically, differentiated cells rely on oxidative phosphorylation within mitochondria to produce the energy required for cellular processes. In contrast, most cancer cells rely on aerobic glycolysis, a phenomenon known as the “Warburg effect” (Sieow et al., 2023). Given the dramatic increase in glucose absorption during this process, glycolysis is a promising early target for cancer therapy (Rangel Rivera et al., 2021). Therefore, study of cancer metabolism is important for understanding its basic pathophysiology and clinical oncology (Karlstaedt et al., 2021). This review focuses on the processes behind the non-invasive study of glucose metabolism dysregulation in cancer.
The critical role of aerobic glycolysis in tumor metabolism
A greater understanding of the interplay between systemic and tumor metabolism could uncover new treatment targets (). Active glycolysis pathways, decreased oxygen consumption, and enhanced glucose absorption are hallmarks of solid malignant tumors. At the cellular level, glucose enters tumor cells by glucose transporters. Subsequently, it is transformed into pyruvate by a series of glycolytic enzymes. Finally, lactate dehydrogenase converts pyruvate into lactate (Guo et al., 2022; Norton et al., 2022) (Figure 1). As a proinflammatory and immunosuppressive mediator, lactic acid contributes to the malignant growth of tumors, and its accumulation in significant amounts alters the pH of the tumor microenvironment (De Jesus et al., 2022). Lactic acid can also activate matrix metalloproteinase (MMP) and directly enhance cell migration ability (). In conclusion, the glucose metabolic process is helpful for tumor cell proliferation, invasion, and metastasis, as well as radiotherapy and chemotherapy resistance, which complicates treatment decisions (Zhu and Thompson, 2019; Gao et al., 2023).
FIGURE 1
Molecular imaging enables visualization of glycolytic effects
Medical imaging techniques such as nuclear medicine, magnetic resonance imaging (MRI), and optical imaging, are at the heart of molecular imaging technology. These allow for non-invasive, real-time imaging of the cellular and molecular levels of physiological and pathological processes inside the human body (Tian et al., 2021; Hod et al., 2023). Owing to advancements in artificial intelligence and innovative imaging agents, molecular imaging may soon become an essential part of therapeutic care (Rowe and Pomper, 2022). The fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is a functional molecular imaging technique that takes advantage of the enhanced glycolysis of cancer cells to obtain both structural information and metabolic activity (Vaarwerk et al., 2021). PET with innovative radiotracers and MR-based approaches provides exciting new ways to study glycolysis and broadens the metabolic imaging toolkit (Edmonds et al., 2022). To completely define tumor biology and better guide therapy, multiparametric imaging must combine various modalities.
Principal patterns of molecular imaging to detect glycolytic effects
PET imaging of tumor glycolysis using pyruvate kinase M2 (PKM2)
The core of tumor metabolism is the glycolytic pathway, which promotes tumor growth through the production of ATP and synthesis of biosynthetic reaction intermediates. PKM2 catalyzes the final rate-limiting steps in tumor glycolysis and controls the balance between energy production and synthesis of metabolic precursors (Ouyang et al., 2018). Considering the importance of PKM2 in regulating tumor metabolism, scientists have attempted to measure its expression non-invasively by developing specific radioactive preparations of PKM2. Witney et al. (2015) described the manufacture and assessment of [11C]DASA-23, a PET radiotracer that offers a direct, non-invasive measurement of PKM2 expression in preclinical models of glioblastoma multiforme (GBM). This study laid the foundation for the clinical use of [11C]DASA-23, which could be used for imaging primary brain tumors and other tumors that may metastasize to the brain. ; ) reported that a novel radiotracer, 1-((2-fluoro- 6-[18F]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([18F]DASA- 23), can be safely used to evaluate pyruvate kinase M2 levels. Its feasibility and safety were further verified through animal experiments and clinical trials (Saidi et al., 2018; ).
18F-FDG-PET/CT was used to visualize the metabolic activity of surviving tumor cells
FDG is a radiolabeled glucose analog that is transported into tumor cells and phosphorylated by hexokinase (Dezhakam et al., 2022). Therefore, FDG-PET can indicate the need for glucose by cells or tumors and provide some information about the tumor’s processing of glucose into usable components (). Yu and his team (Liang et al., 2020) used 18F-FDG-PET/CT to visualize the metabolic activity of surviving tumor cells. The maximum standardized uptake value (SUVmax) has been used as a prognostic indicator of pancreatic ductal adenocarcinoma (PDAC). PET/CT volume parameters, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) fully reveal tumor metabolic activity and volume. The transition to aerobic glycolysis is a characteristic of advanced cancers and is easily assessed by 18F-FDG-PET (Zhao et al., 2022). A crucial route governing the development and metabolism of these malignancies is the mechanistic target of the rapamycin (mTOR) pathway, which can be efficiently addressed by utilizing selective catalytic mTOR kinase inhibitors (Ferrara and Roz, 2022). This was investigated in mice with lung cancer, where therapeutic response was evaluated using 18F-FDG-PET and computed tomography (CT) imaging before and after the administration of mTOR inhibitor MLN0128 (Momcilovic et al., 2018a; Momcilovic et al., 2018b). After targeted therapy intervention, the results demonstrated that 18F-FDG-PET/CT could evaluate dynamic changes in glucose metabolism in lung cancer within mice.
Lactate concentration was measured by proton magnetic resonance spectroscopy (1H-MRS) and SUV by FDG-PET
PET detection of the radioactive glucose analog 18FDG is the only commonly accessible metabolic imaging technology in current clinical practice. However, 18FDG-PET findings are unclear in tissues with intrinsically high glucose absorption, such as the brain, and provides little information on the metabolic processes that occur after glucose intake (De Feyter et al., 2018; Zhang et al., 2022). 1H-MRS is a non-invasive imaging tool for brain function that can reliably detect and quantitatively evaluate brain metabolites for subjective identification of gliomas. 1H-MRS has good accuracy and specificity in diagnosis and grading, as well as the ability to assess the glioma lesion spectrum (Rodriguez-Nieto et al., 2023). This technology is also suitable for simultaneously analyzing multiple target tissue regions, thereby enabling 1H-MRS imaging (1H-MRSI). 1H-MRSI generates a “voxel” table, which can “map” lactate levels (; Lussey-Lepoutre et al., 2020). Esfahani et al. (2022) evaluated the use of hyperpolarized (HP) [1-13C]pyruvate magnetic resonance spectroscopic imaging (HP-13C MRSI) to quantitatively measure early changes in glycolytic metabolism and its ability to predict the response to pan-tyrosine kinase inhibitor (pan-TKI) therapy in gastric cancer (GC). HP-13C MRSI was found to be a more representative biomarker of early metabolic changes in response to pan-TKIs in GC than 18F-FDG-PET, and can be used for the early prediction of response to targeted therapies. The combination of FDG-PET and MRSI may provide a more sensitive clinical indicator of aerobic glycolysis (; Tarumi et al., 2020). Therefore, an imaging technology that can identify aerobic glycolysis in tumors non-invasively would enhance cancer research and monitoring (Figure 2; Table 1 (Witney et al., 2015; ; Momcilovic et al., 2018a; ; Momcilovic et al., 2018b; ; Hundshammer et al., 2018; Saidi et al., 2018; Scroggins et al., 2018; Zhao et al., 2018; ; ; Yamamoto et al., 2020; ; Esfahani et al., 2022)).
FIGURE 2
TABLE 1
| Target | Imaging modality | Radiotracer | Model | Disease model/Patient type | Preclinical/Clinical | Comments | Ref |
|---|---|---|---|---|---|---|---|
| PKM2 | GE PETtrace cyclotron | [18F]DASA-23 | Cell line (HeLa cells) | Cervical cancer | Preclinical | They identified an F-18-labeled PKM2-specific radiotracer which shows potential for in vivo glycolysis imaging | |
| PKM2 | GE PETtrace cyclotron | [18F]DASA-23 and [18F]FDG | Cell line (U87 cells) | Glioblastoma multiforme (GBM) | Preclinical | Evaluation of glycolytic response to multiple classes of anti-glioblastoma drugs | |
| PKM2 | PET/CT and PET/MRI | [18F]DASA-23 | Cell line, Mouse and human | GBM | Preclinical | These findings suggest the future clinical study of [18F]DASA-23 for imaging therapy-induced normalization of cancer metabolism | |
| PKM2 | Cobra II Auto-Gamma Counter | [18F]DASA-23 | Cell line | GBM | Preclinical | This research has shown that tumor-treating fields inhibit hGBM abnormal glycolytic metabolism through decreased PKM2 expression, which can be measured non-invasively by the [18F]DASA-23 radiotracer | Saidi et al. (2018) |
| PKM2 | PET | [11C]DASA-23 | Nude mice | GBM | Preclinical | These data provide imaging agents that target this critical gatekeeper PKM2 of tumor glycolysis | Witney et al. (2015) |
| PKM2 | PET/MRI | [18F]DASA-23 | Human | Five healthy volunteers | Clinical Trial (NCT03539731) | Their results indicate that [18F]DASA-23 can be used safely in humans to evaluate pyruvate kinase M2 levels | |
| PKM2 | PET | [18F]DASA-23 via fluorination of 1-((2-fluoro-6-nitrophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine with K [18F]F/K2.2.2 | Mice | Prostate cancer | Preclinical | They observed rapid metabolism of [18F]DASA-23 in mouse plasma | |
| PKM2 | PET/CT | conventional parameters, total lesion glycolysis, and heterogeneity parameters | Nude mice | HR+HER2- metastatic breast cancer | Preclinical | 18F-FDG based intra-tumor heterogeneity appears to be a potential predictor of the efficacy of fulvestrant among HR+HER2- metastatic breast cancer patients | Zhao et al. (2018) |
| 18F-FDG | PET/CT | standard uptake values (SUV) | Genetically engineered mouse models | Lung cancer | Preclinical | 18F-FDG PET/CT can be used to evaluate dynamic changes in glucose metabolism in lung cancer mice after the targeted therapy intervention | Momcilovic et al. (2018a),Momcilovic et al. (2018b) |
| 18F-FDG | new hyperpolarized MRI and EPR imaging procedures | pO2 and18F-FDG uptake, lactate dehydrogenase activity | Nude mice | Pancreatic tumor | Preclinical | They developed a novel multimodal molecular imaging technique to reveal three companion imaging biomarkers and describe the relationship between hypoxia, glucose uptake, and glycolysis in the tumor microenvironment | Yamamoto et al. (2020) |
| 18F-FDG and13C-pyruvate | PET/MR | SUV and apparent diffusion coefficients (ADC) | Rats | Breast cancer | Preclinical | They concluded that changes in cell density affect PET and13C data in a similar way. The correlation of longitudinal metabolic data seems to reflect biochemical processes and tumor cell structure | Hundshammer et al. (2018) |
| Hyperpolarized Sodium [1- 13C]-Glycerate | Magnetic resonance spectroscopy (MRS) | [13C]-labeled pyruvate and lactate | Rats | Healthy male | Preclinical | This study established a hyperpolarized [13C]-Glyc, which provides an opportunity to evaluate the redox state of cells in biochemical research | |
| Hyperpolarized [1-13C]-pyruvate | Magnetic resonance spectroscopic imaging (MRSI) | [1-13C]-pyruvate | Mouse | Prostate cancer | Preclinical | Hyperpolarized [1-13C]-pyruvate MRSI of prostate cancer predicts the efficacy of targeting the Warburg effect | Scroggins et al. (2018) |
| Hyperpolarized [1-13C]Pyruvate | MRSI | Hyperpolarized [1-13C]Pyruvate | Mouse | Gastric cancer (GC) | Preclinical | HP-13C MRSI is a more representative indicator of early metabolic alterations in response to pan-TKI in GC than [18F]FDG PET. | Esfahani et al. (2022) |
Summary of glycolysis by molecular imaging.
Molecular imaging technology combined with glycolysis products
FDG-PET in conjunction with HP MRI
HP MRI provides additional information that cannot be inferred from FDG-PET or hypoxia measures (Zhao et al., 2018). The tissue contrast of 18F-FDG-PET in the prostate is poor and its application is complicated because of its proximity to the excretory system. In addition, the fundamental change in prostate cancer is a change in carbon use, not just glucose uptake. Therefore, after the injection of HP [1-13C] pyruvate, rapid 13C MR was used to measure the metabolic conversion of this aggressive cancer and its response to treatment, with direct biological significance (Fantin et al., 2006; Keshari et al., 2013a; Keshari et al., 2013b; Wilson and Kurhanewicz, 2014). Using this approach, Qi et al. (2022) utilized 18F-FDG-PET and HP [1-13C]pyruvate MRSI with chemical exchange saturation transfer (CEST) MRI measures to investigate the effect of glucose infusion on intracellular pH and its relationship to the tumor.
Chemokine receptor-4 targeted PET/CT with 68Ga-Pentixafor
While 18F-FDG-PET/CT glucose absorption measurements provides useful metabolic information for evaluating most malignant tumors it is limited in evaluating multiple myeloma (MM) (Zamagni et al., 2021). Since chemokine receptor four is overexpressed in MM, Pan and his team (Pan et al., 2020) conducted a prospective cohort study to compare the performance of 68Ga-pentixafor and 18F-FDG-PET/CT in newly diagnosed MM. Quantitative analysis revealed that the bone marrow uptake value of 68Ga-Pentixafor (TBmUCXCR4, SUVmax, and SUVmean) was positively correlated with tumor burden-related end-organ damage, and the stage was positively associated with laboratory biomarkers. 68Ga-Pentixafor was more abundant than 18F-FDG in newly diagnosed MM. These results indicated that 68Ga-pentixafor might be a superior biomarker to 18F-FDG for PET quantification assessment of tumor burden in newly diagnosed MM. However, further investigations are needed to determine whether this affects patient prognosis and survival.
Combined molecular imaging of glycolysis with angiogenesis
used [68Ga] Ga-NODAGA-RGD-PET with vascular imaging and hyperpolarization [1-13C] pyruvate-MRSI to detect energy metabolism. [68Ga] Ga-NODAGA-RGD-PET and [1-13C] pyruvate-MRSI may provide additional information than either technique alone, indicating that non-invasive hyperPET combined with angiogenesis and glycolysis imaging can help to analyze cancer phenotypes. They showed that imaging of angiogenesis and tumor metabolism by [68Ga]Ga-NODAGA-RGD-PET and [1-13C] pyruvate-MRSI, respectively, is feasible in canine cancer patients.
Synergistic construction of aerobic and anaerobic glycolysis zones
As reported, tumor hypoxia and glycolysis, including low-oxygen glycolysis and anaerobic glycolysis, were predicted using 18F-FDG and 18F-fluoromisonidazole (FMISO) PET technologies (Leimgruber et al., 2020). This method has been applied in a prospective clinical trial of 10 patients with GBM who underwent 18F-FDG and 18F-FMISO PET and MRI studies after surgery, before radiotherapy, or early after radiotherapy. Spatial mapping of aerobic and anaerobic glycolysis can provide unique information regarding tumor metabolism and hypoxia through PET. This prospective preliminary study will facilitate the development of a powerful and intuitive software tool for assessing glucose metabolism under hypoxic conditions. In addition, a unique software algorithm, “Glyoxia,” has been developed (; Moscoso et al., 2019), which can detect glycolysis and hypoxia through the automatic processing of 18F-FDG and 18F-FMISO PET images. The algorithm allows serial dual-track molecular imaging comparisons to classify and delineate hypoxic areas at a risk of recurrence. The simplicity of this method can be extended to other hypoxic cancers and possibly to other molecular imaging compounds. By understanding the glucose metabolism in GBM under hypoxic conditions, the effects of aerobic glycolysis in patients and the relationship with treatment resistance can be assessed.
A new hybrid mode—PET/MR
Precision treatment is inseparable from the development of biomedical imaging technology, aiming to identify changes in tumor biology at the cellular and molecular levels (Goutsouliak et al., 2020). The novel PET/MR hybrid imaging modality allows for the simultaneous acquisition of high-resolution anatomical images and metabolic data. The critical step of the Warburg effect, a hallmark of tumors, can be measured non-invasively using this emerging technology. Hundshammer and his team (Hundshammer et al., 2018) utilized the multimodal imaging workflow of the PET/MR system, including proton MRI, to obtain accurate morphological information and diffusion-weighted imaging (DWI) to resolve tumor cellularity. Metabolic data were measured using dynamic 18F-FDG-PET and HP 13C-pyruvate MRSI. This technique was also used to determine how cell density influences variations in glycolytic parameters that occur due to cell expansion. Researchers have found that an increase in LDH activity corresponded with increased glucose absorption by tumor cells. Longitudinal DWI data showed that cell density decreased during tumor growth, affecting the quantification of PET and MRSI data. The workflow included multiparameter and non-invasive tumor characterization, which can be applied to larger animal models. This has the potential for clinical applications, paving the way for tailored and patient-specific treatment methods.
Visualization of glucose metabolism for cancer immunotherapy
Immunotherapy replaces traditional radiotherapies and chemotherapies with immune checkpoint inhibitors, which use the patient’s immune system to identify and target cancer cells (Vernieri et al., 2022). Despite the tremendous clinical success and aggressive research on immunotherapies, there remains a considerable unmet need for a rigorous technique to identify immunotherapy responders (Kraehenbuehl et al., 2022). Early and precise monitoring of immunotherapy response is essential for optimal medication development and tailored treatment (Goncalves et al., 2021). Texture characteristics generated from PET, which are known to suggest glucose absorption distribution, have been shown to be linked with the expression of PD-L1 mRNA, but not with the expression of PD-1, CTLA-4, or TMB. To differentiate between those who will respond to treatment and those who will not, non-invasive in vivo 18F-FDG-PET/CT imaging of glucose metabolism in lymphatic organs might be a valuable preclinical and clinical technique (Schwenck et al., 2020). Vos et al. (2022) discovered 18F-FDG-PET-based DeltaMTV and DeltaTLG reliably identified pathological responses to neoadjuvant immune checkpoint blockade (ICB) in head and neck squamous cell carcinoma (HNSCC) before surgery, surpassing the European Organisation For Research And Treatment Of Cancer (EORTC) criteria. However, neck lymph nodes exhibited pseudoprogression. Upon validation, 18F-FDG-PET might identify HNSCC patients for response-driven therapy adaptation in future clinical studies. PET biomarkers based on 18F-FDG imaging have been successfully used for prognosis and therapeutic response monitoring in patients undergoing anti-PD1 immunotherapy for malignant melanoma, where hematopoietic tissue metabolism correlates adversely with survival. Additionally, compared to CT imaging alone, metabolic imaging with FDG-PET at termination of immune checkpoint inhibitor medication helps identify melanoma patients with a low risk of recurrence and favorable prognosis (Ferdinandus et al., 2022). Yang’s group (Yang et al., 2022) demonstrated the promise of sensitive and comprehensive monitoring using label-free metabolic intravital imaging (LMII) for visualizing the dynamic changes in heterogeneous cell metabolism of cancer cells and immunological infiltrates in response to immunotherapy. The uptake of 18F-FDG and 18F-fluorodeoxythymidine (FLT) on PET may serve as an imaging biomarker for assessing the efficacy of immune checkpoint inhibitor therapy (Oh et al., 2022). Another research group (Wang Y. et al., 2020) looked at how the expression of intra-tumor immunomarkers in non-small cell lung carcinoma (NSCLC) patients correlated with their metabolic status, as measured by 18F-FDG-PET/CT. These findings showed that SUVmax on 18F-FDG-PET/CT may be a valuable predictor for identifying patients for immunotherapy by revealing a link between metabolic variables and immune cell expression in the tumor microenvironment. In oral squamous cell carcinoma, the absorption of 18F-FDG is an independent predictor of cold tumors. Imaging using 18F-FDG-PET may be a viable diagnostic technique for estimating the immunological state of tumors. However, quantitative 18F-FDG-PET imaging of the spleen performed poorly in predicting clinical improvement using ipilimumab (Sachpekidis et al., 2019).
Importance in clinical practice
By leveraging the radiological properties of SUVmean and entropy on 18F-FDG-PET/CT, researchers discovered a potential benefit of 2-year progression-free survival (PFS) (Mayerhoefer et al., 2019). Moreover, when combined with clinical/laboratory and biological parameters, they could obtain more valuable PFS and prognostic information. Ceriani and his team () established a multiparameter prediction model that integrated texture features and conventional PET indicators into incidental thyroid tumors. This model can be combined with three independent PET-derived prediction parameters: TLG, SUVmax, and shape sphericity. This approach was used in a study monitoring 19 patients with malignant pleural mesothelioma (MPM) before and after two cycles (6 weeks) of chemoimmunotherapy (Reynolds et al., 2020). To determine the malignancy of [18F]-fluorodeoxyglucose-avid thyroid incidentalomas, six separate radiological characteristics were used that describe the shape, heterogeneity, and intensity of lesion tracer uptake. A threshold-based Interactive Data Language (IDL) tool was then created to quantify tumor volume in 18F-FDG-PET scans. In this MPM study, there was a causal relationship between tumor volume and TLG. Research also showed that, regardless of whether the tumor size changed or responded to therapy, the overall intake of 18F-FDG is always proportional to the total metabolite volume (TMV) of the tumor. This finding was unexpected because standard teaching indicates that changes in metabolism, measured by FDG uptake, precede changes in volume. MPM may be a condition in which FDG uptake by each cell and its proliferation vary simultaneously. Additional clinical data are listed in Table 2 (Kahraman et al., 2012; Lee et al., 2013; Maharjan et al., 2013; Usmanij et al., 2013; ; ; ; Soussan et al., 2014; Elimova et al., 2015; Tateishi et al., 2015; Wong et al., 2016; Xie et al., 2016; Lim et al., 2017; Liu et al., 2017; Park et al., 2017; Salavati et al., 2017; ; Kaira et al., 2018; Lovinfosse et al., 2018; Wang et al., 2018; ; ; ; Mayerhoefer et al., 2019; van Berkel et al., 2019; Wang L. et al., 2020; Deckers et al., 2020; Fiore et al., 2020; Mathew et al., 2020; Mitchell et al., 2020; Pan et al., 2020; Dunet et al., 2021; Vos et al., 2022).
TABLE 2
| Imaging modality | Radiotracer | Predicator | Disease model/Patient type | Clinical application value | Ref |
|---|---|---|---|---|---|
| PET/CT | 18F-FDG | SUVmaxa, MTVb and TLGc | Advanced biliary tract cancer (BTC) | MTV may be considered an essential independent metabolic prognostic factor for the overall survival of patients with BTC, and it is a predictive marker for erlotinib treatment | |
| PET/CT | 18F-FDG | Tumor diameter, SUVmax, and TLG | Advanced gastric cancer (AGC) | Both tumor size and SUVmax are related to the patient’s OS and PFS, which can help predict the prognoses of AGC patients receiving chemotherapy | Park et al. (2017) |
| PET/CT and DW MRI | 18F-FDG | Tumor volume or TLG | Advanced head and neck squamous cell carcinoma (HNSCC) | The data indicate that the metabolic tumor volume or TLG caused by18F-FDG PET/CT obtained after one cycle of induction chemotherapy is an early predictive biomarker for the final response to subsequent radiotherapy and chemotherapy | Wong et al. (2016) |
| PET/CT | 18F-FDG | Median ΔSUVmax, ΔSUVmean, ΔMTV, and ΔTLG | HNSCC | Assessment of primary tumor responses to neoadjuvant immune checkpoint blockade in HNSCC using [18F]FDG-PET-based MTV and TLG reliably detects pathological responses early in treatment | Vos et al. (2022) |
| PET/CT | 18F-FDG | SUVd, MTV and TLG | Breast, endometrial, and ovarian cancer | Early response assessment with FDG-PET/CT can predict local remission (PR) and progression (PD) | |
| PET/CT | 18F-FDG | SUVmax, SUVmean, MTV and TLG | Cervical cancer | Among high-risk cervical cancer patients receiving mid-chemoradiation therapy (CRT) and brachytherapy, the TLG and metabolic tumor volume of FDG-PET/CT in CRT are related to OS. These indicators may provide early signals for enhancing dose escalation or selective treatment of adjuvant chemotherapy | |
| PET/CT | 18F-FDG | SUVmax, MTV and TLG | Cervical cancer | This study introduces the metabolic parameters of primary tumors and regional lymph nodes measured by F-18 FDG PET/CT pretreatment. The SUVmax of CT pretreatment may be a prognostic biomarker for predicting the recurrence of locally advanced cervical cancer | |
| PET/CT | 18F-FDG | SUVmax, MTV and TLG | Diffuse large B-cell lymphoma | This study found that the SUVmax value cannot predict PFS, and MTV and TLG may be critical prognostic markers of PFS in DLBCL. | Xie et al. (2016) |
| PET/CT | 18F-FDG | SUVmax, SUVmeane and TLG | High-Risk Locally Advanced Rectal Cancer | Early total-lesion glycolysis and its percentage change compared with baseline (ΔTLG-early) showed the highest accuracy in predicting the regression of advanced rectal cancer. It is possible to guide the decision to modify the treatment plan during preoperative radiotherapy and chemotherapy based on the particular response | |
| PET/CT | 18F-FDG | TLG | Locally advanced rectal cancer (LARC) | This study found that TLG of the primary tumor in FDG-PET/CT can be considered a prognostic factor for locally advanced rectal cancer patients receiving neoadjuvant chemoradiotherapy (CCRT) | Lee et al. (2013) |
| PET/CT | 18F-FDG | SUVmax, SUVmean, MTV, TLG, and histogram-intensity features | LARC | The histological analysis of baseline18F-FDG PET/CT can be a strong independent predictor for the survival of LARC patients | Lovinfosse et al. (2018) |
| PET/CT | 18F-FDG | SUVmean | Mantle cell lymphoma (MCL) | SUVmean and entropy can improve the prediction of PFS and PFS prognosis in 2 years. When metabolic indicators are combined with clinical, laboratory, and biological parameters, the best predictive analysis results can be achieved | Mayerhoefer et al. (2019) |
| PET/CT | 18F-FDG | TLG | Metastatic colorectal cancer | Measurement of TLG can predict the treatment outcome of regorafenib in mCRC. | Lim et al. (2017) |
| PET/CT | 68Ga-Pentixafor and 18F-FDG | Total bone marrow glycolysis, whole bone marrow uptake, total bone marrow volume [TBmV], SUVmean and SUVmax | Multiple myeloma | 68Ga-Pentixafor PET/CT is expected to evaluate newly diagnosed MM. | Pan et al. (2020) |
| PET/CT | F-FDG and F-DOPA | SUVmax, SUV threshold, MTV, TLG, dopaminergic tumor volume (DTV), and total lesion F-DOPA activity (TLDA) | Neuroblastoma | Both F-DOPA and F-FDG PET can be used for neuroblastoma risk stratification. The correlation between the volume index of F-DOPA and F-FDG PET and the risk group is higher | Liu et al. (2017) |
| PET/CT | FDG | MTV, TLG, SUVmax, SUVmean, partial-volume-corrected TLG (pvcTLG), and pvcSUVmean | Non-small cell lung cancer (NSCLC) | FDG-PET/CT parameters, including MTV, TLG, and pvcTLG, have a vital prognosis for OS in patients with locally advanced non-small cell lung cancer and have similar discrimination capabilities | Salavati et al. (2017) |
| CT and PET/CT | FDG | blood flow (BF), blood volume (BV), mean transit time, and peak enhancement intensity. SUVmax, SUVpeak, SUVmean, MTV and TLG | NSCLC | The larger the tumor, the lower the BF and BV; conversely, the higher the SUVpeak, MTV, and TLG. This index can be used for clinical diagnosis or treatment of NSCLC. | |
| PET/CT | 3′-deoxy3′-fluoro-18-fluorothymidine (18F-FLT) and18F-FDG | speak values and TLG | NSCLC | This study used semi-quantitative analysis to calculate the standard intake lean body mass (SULpeak) and TLG values of the hottest lesions in all patients. Among responders and non-responders, EGFR18F -FDG SULpeak is better than18F-FLT SULpeak in predicting OS and PFS in NSCLC patients treated with EGFR-TKI for 3 weeks | |
| PET/CT | 18F-FDG | SUVmax, SUVmean, MTV and TLG | NSCLC | This study points out that in locally advanced NSCLC, the ratio of MTV to TLG is the only indicator related to the survival of the tumor after induction chemotherapy | Soussan et al. (2014) |
| PET/CT | 18F-FDG | SUV, MTV, and TLG | NSCLC | This study proved that the use of molecular imaging to analyze the degree of change in TLG could predict the response of NSCLC to radiotherapy, chemotherapy, and PFS. | Usmanij et al. (2013) |
| PET/CT | 18F-FDG | TLG and tumor lesion proliferation (TLP) | NSCLC | This study found that in patients with advanced NSCLC, the percentage change of TLG and TLP and absolute residual TLG and TLP levels under erlotinib treatment have become essential predictors of PFS. | Kahraman et al. (2012) |
| PET/CT | 18F-FDG | SUVmax, MTV and TLG | NSCLC | Metabolic response by18F-FDG effectively predicted efficacy and survival 1 month after nivolumab treatment | Kaira et al. (2018) |
| PET/CT | 18F-FDG | SUVmax, SUVtotal, SUVmean and TLG | NSCLC | Studies have proven that18F-FDG SUVmax has potential value as a non-invasive clinical indicator of tumor immune metabolism phenotype in patients with resectable NSCLC and can be used as a potential predictor of response to treatment based on immunotherapy strategies | Mitchell et al. (2020) |
| PET/CT | 18F-FDG | SUVmean, SUVmax, MTV and TLG | NSCLC | The study found that in untreated stage IIIB-IV NSCLC, the use of pSUVmax on18F-FDG PET/CT may be a potential biomarker for pPDL1 TPS <1%, 1%–49%, and ≥50%, and may help determine the immunotherapy strategy for advanced NSCLC. | (Wang et al., 2020) |
| PET/CT | 18F-FDG | SUVmax and TLG | Esophageal cancer | This study found that PET parameters cannot predict complete pathological remission (pathCR) in patients with esophageal adenocarcinoma or squamous cell carcinoma suitable for triple therapy. Still, baseline and TLG after mid-term or chemical radiation (CTRT) can predict OS. | Elimova et al. (2015) |
| PET/CT | 18F-FDG | SUVmax, SUVmean, MTV, TLG, mean and minimum apparent diffusion coefficient (ADCmean and ADCmin), diffusion total volume (DTV), and MTV/ADCmin ratio | Pancreatic ductal adenocarcinoma (PDAC) | MTV is an independent predictor of OS and DSS, while DTV is an independent predictor of PFS. The study also found that ADC and SUV values are related and, combined with PET-MRI indicators, can help predict PDAC grade and patient survival | Dunet et al. (2021) |
| PET/CT | 18F-FDG | SUVmax, MTV and TLG | Locally advanced pancreatic cancer | Under baseline18F-FDG PET/CT and its combination, the level of CA19-9 before treatment and the MTV and TLG values of the primary tumor may predict early progression (EP), local progression (LP), and overall survival (OS) in LAPC patients | Fiore et al. (2020) |
| PET/CT | 18F-FDG | SUVmean, SUVmax, MTV and TLG | Pediatric anaplastic large cell lymphoma (ALCL) | Using MTV indicators for systemic tumor burden assessment and mid-term response may help ALCL patients who benefit from intensive treatment | Mathew et al. (2020) |
| Dynamic PET/CT | 18F-FDG | Glucose metabolic rate (MRglc) and SUVmax of the lesions | Pheochromocytomas and paragangliomas (PPGL) | Dynamic PET/CT scans to assess the pharmacokinetics of18F-FDG can perform in vivo metabolic tumor analysis in patients with PPGL, and cluster 1 PPGL can be identified | van Berkel et al. (2019) |
| PET/CT | 18F-FDG | SUVmax, MTV and TLG | Recurrent carcinoma of the cervix | This study found that SUVmax and distant metastasis on F-FDG PET-CT were independent predictors of PFS and OS in patients with recurrent cervical cancer | Maharjan et al. (2013) |
| PET/CT | 18F-FDG | SUVmax, SUL, MTV, TLG, and AUC-CSH of the lesion | Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) | The total lesion glycolysis calculated by PET/CT can be used to quantify the response to DLBCL treatment. It can also predict the progression-free survival of patients with relapsed or refractory DLBCL treated with bendamustine-rituximab after the last treatment cycle | Tateishi et al. (2015) |
| PET/CT | 18F-FDG | MATV, SUVmean/max and TLG in contrast to LDH | Stage IV melanoma | This study evaluated the association between biomarkers (S-100B, LDH) in stage IV melanoma and PET-derived indicators SUVmean/max, metabolically active tumor volume (MATV), and TLG. | Deckers et al. (2020) |
| PET/CT | 18F-FDG and68Ga-NOTA-PRGD2 | SUV, MTV, and TLG | Thyroid cancer | The combined application of18F-FDG and68Ga-NOTA-PRGD2 PET/CT can predict and evaluate early apatinib-treated radioactive iodine-refractory thyroid cancer | Wang et al. (2018) |
Clinical trials of glycolysis by FDG molecular imaging.
SUVmax: the maximum standardized uptake value.
MTV: metabolic tumor volume.
TLG: total-lesion glycolysis.
SUV: standardized uptake value.
SUVmean: the average SUVs in the regions of interest.
Through different molecular imaging technologies, the movement of cells can be tracked to determine the dynamic process of aerobic glycolysis. Molecular imaging enables researchers to observe cellular processes in their natural environment in real time, thereby significantly increasing the value and authenticity of the observations (Yamamoto et al., 2020; ; Goncalves et al., 2021). Cancer cells divide rapidly as they expand and require enormous amounts of glucose for their high metabolic rates. As a result, tumor cells in vitro will incorporate a large amount of 18F-FDG. Positrons emitted by 18F can be captured and visualized by the PET/CT detector, showing the location, shape, size, number, and distribution of glucose metabolism in the tumor (Nakamoto et al., 2021). This method efficiently evaluates alterations in the tumor’s energy metabolism region, judgement of benign/malignant status, staging of the lesion, and survival status of tumor cells after radiation or chemotherapy (Gomes et al., 2017; ). PET/CT hypoxia imaging can detect the hypoxia status in tumor tissues in vivo using molecular probes. Hypoxia imaging agents such as 18F- FMISO, 18F-flortanidazole (HX)4, 18F-Fluoroazomycin Arabinoside (FAZA), and 64Cu-ATSM, have been exploited to effectively demonstrate the hypoxic status inside tumors ().
18F-FDG-PET/CT was shown to have 100% sensitivity and 78% specificity in monitoring mediastinal lymph node metastasis of NSCLC, according to a study published by Jimenez-Bonilla et al. (2016). Recently, Chinese researchers (Kang et al., 2016) performed 68Ga-labeled RGD peptide-alpha peptide II (68Ga-alpha II) and 18F-FDG-PET/CT imaging in patients with NSCLC and tuberculosis (TB), respectively, and 68Ga-alpha II imaging SUVmax and SUVmean. In detecting NSCLC metastatic lymph nodes, 68Ga-Alfatide II was more specific but less sensitive than 18F-FDG; the false positive rate of 68Ga-Alfatide for TB lymph node identification was much lower than that of 18F-FDG. These findings also indicate that FDG-PET may be used with other standard diagnostic procedures, such as breast ultrasonography and mammography, to further improve diagnostic accuracy. More clinical data are required to support the use of FDG-PET for evaluating lymph node metastases in the context of diagnosis (Stewart et al., 2018; Fowler and Strigel, 2022).
Conclusion
Aerobic glycolysis is a crucial characteristic of tumors and is intimately connected to the incidence and aggressive growth of tumors (Xia et al., 2021). Monitoring aerobic glycolysis in tumor tissue might lead to improved methods for diagnosing and treating cancer (Ma et al., 2022; Raskov et al., 2022). Molecular imaging is an emerging interdisciplinary subject in imaging, medicine, and molecular biology (Jan De Beur et al., 2022). Early detection, tailored treatment, and real-time tumor monitoring will benefit from non-invasive molecular imaging of glucose metabolism in cancer. However, current technology for molecular imaging has deficiencies in terms of resolution, detection limit, accessibility, and energy extension (). Thus, combining many imaging methods may broaden the possibilities of molecular imaging (Lewis et al., 2021). Future multimodal imaging must focus on safe, effective, dual-functional molecular detection and therapy of glucose metabolism to enable the widespread uptake of molecular imaging technologies.
Statements
Author contributions
GZ and JS prepared figures and tables. YM designed and wrote the manuscript. HP and TY contributed to the critical revision of the English language. YM was responsible for financial support. All authors approved the final manuscript.
Funding
This research work supported by “the Fundamental Research Funds for the Central Universities”, Natural Science Foundation (20180550488 and 2020-ZLLH-38 to YM) of Liaoning Province, Young and middle-aged technological innovation talents in Shenyang of YM (RC200491), and Excellent Talent Fund of Liaoning Province Cancer Hospital of YM.
Acknowledgments
Thanks to Liaoning Cancer Hospital for supporting scientific research.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Summary
Keywords
molecular imaging, tumor metabolism, PET, biological characteristic, aerobic glycolysis
Citation
Meng Y, Sun J, Zhang G, Yu T and Piao H (2023) Imaging glucose metabolism to reveal tumor progression. Front. Physiol. 14:1103354. doi: 10.3389/fphys.2023.1103354
Received
20 November 2022
Accepted
20 January 2023
Published
02 February 2023
Volume
14 - 2023
Edited by
Zhen Cheng, Shanghai Institute of Materia Medica (CAS), China
Reviewed by
Xilin Sun, The Fourth Hospital of Harbin Medical University, China
Updates
Copyright
© 2023 Meng, Sun, Zhang, Yu and Piao.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tao Yu, taoyulnszlyy@163.com; Haozhe Piao, haozhepiaolnzlyy@sina.com
This article was submitted to Medical Physics and Imaging, a section of the journal Frontiers in Physiology
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