Dependence of FDG uptake on tumor microenvironment

Role of Textural Analysis Parameters Derived from FDG PET/CT in Diagnosing Cardiac Sarcoidosis

Introduction  Texture and radiomic analysis characterize the lesion's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET/CT) images in differentiating patients with cardiac sarcoidosis (CS) from patients with physiologic myocardial uptake. Methods  This is a retrospective, single-center study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients. These patients underwent FDG PET/CT for the diagnosis of CS. The non-CS group underwent 18F-FDG PET/CT for other oncological indications. The PET/CT images were then processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between the CS group and the non-CS group. Receiver operating characteristics (ROC) curves were used to identify cutoff values for textural features with a p -value < 0.05 for statistical significance. These parameters were then passed through a principle component analysis algorithm. Five different machine learning classifiers were then tested on the derived parameters. Results  A retrospective study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients, was done. Twelve textural analysis parameters were significant in differentiating between the CS group and the non-CS group. Cutoff values were calculated for these parameters according to the ROC curves. The parameters were Discretized_HISTO_Entropy, GLCM_Homogeneity, GLCM_Energy, GLRLM_LRE, GLRLM_LGRE, GLRLM_SRLGE, GLRLM_LRLGE, NGLDM_Coarseness, GLZLM_LZE, GLZLM_LGZE, GLZLM_SZLGE, and GLZLM_LZLGE. The gradient boosting classifier gave best results on these parameters with 85.71% accuracy and an F1 score of 0.86 (max 1.0) on both classes, indicating the classifier is performing well on both classes. Conclusion  Textural analysis parameters could successfully differentiate between the CS and non-CS groups noninvasively. Larger multicenter studies are needed for better clinical prognostication of these parameters.

Biological effectiveness of uniform and nonuniform dose distributions in radiotherapy for tumors with intermediate oxygen levels

Objective. We propose a criterion of biological effectiveness of nonuniform hypoxia-targeted dose distributions in heterogeneous hypoxic tumors based on equivalent uniform aerobic dose (EUAD). We demonstrate the utility of this criterion by applying it to the model problems in radiotherapy for tumors with different levels of oxygen enhancement ratio (OER) and different degrees of dose nonuniformity.Approach. The EUAD is defined as the uniform dose that, under well-oxygenated conditions, produces equal integrated survival of clonogenic cells in radiotherapy for heterogeneous hypoxic tumors with a non-uniform dose distribution. We define the dose nonuniformity effectiveness (DNE) in heterogeneous tumors as the ratio of the EUAD(DN) for a non-uniform distributionDNand the reference EUAD(DU) for the uniform dose distributionDUwith equal integral tumor dose. The DNE concept is illustrated in a radiotherapy model problem for non-small cell lung cancer treated with hypoxia targeted dose escalation. A two-level cell population tumor model was used to consider the hypoxic and oxygenated tumor cells.Results. Theoretical analysis of the DNE shows that the entire region of the OER can be separated in two regions by a threshold OERth: (1) OER > OERthwhere DNE > 1 indicating higher effectiveness of nonuniform dose distributions and (2) OER < OERthwhere DNE < 1 indicating higher effectiveness of uniform dose distributions. Our simulations show that the value of the threshold OERthin radiotherapy with conventional fractionation is significant in the range of about 1.2-1.6 depending on selected radiotherapy parameters. In general, the OERthincreases with reoxygenation rate, relative hypoxic volume and dose escalation factor. The threshold value of OERthis smaller of about 1.1 for hypofractionated radiotherapy.Significance. The analysis of dose distributions using the DNE shows that the uniform dose distributions may improve biological cell killing effect in heterogeneous tumors with intermediate oxygen levels compared to targeted nonuniform dose distribution.

Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients

INTRODUCTION

Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.

MATERIALS AND METHODS

Two hundred and twelve primary breast cancer patients underwent 18 F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P -value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.

RESULTS

A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.

CONCLUSION

Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

The combined treatment with ketogenic diet and metformin slows tumor growth in two mouse models of triple negative breast cancer

Background

Many tumors contain hypoxic microenvironments caused by inefficient tumor vascularization. Hypoxic tumors have been shown to resist conventional cancer therapies. Hypoxic cancer cells rely on glucose to meet their energetic and anabolic needs to fuel uncontrolled proliferation and metastasis. This glucose dependency is linked to a metabolic shift in response to hypoxic conditions.

Methods

To leverage the glucose dependency of hypoxic tumor cells, we assessed the effects of a mild reduction in systemic glucose by controlling both dietary carbohydrates with a ketogenic diet and endogenous glucose production by using metformin on two mouse models of triple-negative breast cancer (TNBC).

Results

Here, we showed that animals with TNBC treated with the combination regimen of ketogenic diet and metformin (a) had their tumor burden lowered by two-thirds, (b) displayed 38% slower tumor growth, and (c) showed 36% longer latency, compared to the animals treated with a ketogenic diet or metformin alone. As a result, lowering systemic glucose by this combined dietary and pharmacologic approach improved overall survival in our mouse TNBC models by 31 days, approximately equivalent to 3 years of life extension in human terms.

Conclusion

This preclinical study demonstrates that reducing systemic glucose by combining a ketogenic diet and metformin significantly inhibits tumor proliferation and increases overall survival. Our findings suggest a possible treatment for a broad range of hypoxic and glycolytic tumor types that can augment existing treatment options to improve patient outcomes.

Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer?

This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.

Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

BACKGROUND

Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated.

METHODS

We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach.

RESULTS

[18 F]FDG (2-deoxy-2-[fluorine-18]fluoro-d-glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one-carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18 F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade.

CONCLUSION

Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18 F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

The Combined Treatment with Ketogenic Diet and Metformin Slows Tumor Growth in Two Mouse Models of Triple Negative Breast Cancer

BACKGROUND

Many tumors contain hypoxic microenvironments caused by inefficient tumor vascularization. Hypoxic tumors have been shown to resist conventional cancer therapies. Hypoxic cancer cells rely on glucose to meet their energetic and anabolic needs to fuel uncontrolled proliferation and metastasis. This glucose dependency is linked to a metabolic shift in response to hypoxic conditions.

METHODS

To leverage the glucose dependency of hypoxic tumor cells, we assessed the effects of a controlled reduction in systemic glucose by combining dietary carbohydrate restriction, using a ketogenic diet, with gluconeogenesis inhibition, using metformin, on two mouse models of triple-negative breast cancer (TNBC).

RESULTS

We confirmed that MET - 1 breast cancer cells require abnormally high glucose concentrations to survive in a hypoxic environment in vitro. Then, we showed that, compared to a ketogenic diet or metformin alone, animals treated with the combination regimen showed significantly lower tumor burden, higher tumor latency and slower tumor growth. As a result, lowering systemic glucose by this combined dietary and pharmacologic approach improved overall survival in our mouse model by 31 days, which is approximately equivalent to 3 human years.

CONCLUSION

This is the first preclinical study to demonstrate that reducing systemic glucose by combining a ketogenic diet and metformin significantly inhibits tumor proliferation and increases overall survival. Our findings suggest a possible treatment for a broad range of hypoxic and glycolytic tumor types, one that can also augment existing treatment options to improve patient outcomes.

Cancer Metabolism as a Therapeutic Target and Review of Interventions

Cancer is amenable to low-cost treatments, given that it has a significant metabolic component, which can be affected through diet and lifestyle change at minimal cost. The Warburg hypothesis states that cancer cells have an altered cell metabolism towards anaerobic glycolysis. Given this metabolic reprogramming in cancer cells, it is possible to target cancers metabolically by depriving them of glucose. In addition to dietary and lifestyle modifications which work on tumors metabolically, there are a panoply of nutritional supplements and repurposed drugs associated with cancer prevention and better treatment outcomes. These interventions and their evidentiary basis are covered in the latter half of this review to guide future cancer treatment.

Update on Radiotherapy Changes of Nasopharyngeal Carcinoma Tumor Microenvironment

The utilization of radiotherapy (RT) serves as the principal approach for managing nasopharyngeal carcinoma (NPC). Consequently, it is imperative to investigate the correlation between the radiation microenvironment and radiation resistance in NPC. PubMed and China National Knowledge Infrastructure (CNKI) databases were accessed to perform a search utilizing the English keywords "nasopharyngeal cancer", "radiotherapy", and "microenvironment". The search time spanned from the establishment of the database until January 20, 2023. A total of 82 articles were included. The post-radiation tumor microenvironment (TME), or the radiation microenvironment, includes several components, such as the radiation-immune microenvironment and the radiation-hypoxic microenvironment. The radiation-immune microenvironment includes various factors like immune cells, signaling molecules, and extracellular matrix. RT can reshape the TME, leading to immune responses with both cytotoxic effects (T cells, B cells, natural killer (NK) cells) and immune escape mechanisms (regulatory T cells (Tregs), macrophages). RT enhances immune responses through DNA release, type I interferons, and immune cell recruitment. Radiation-hypoxic microenvironment affects metabolism and molecular changes. RT-induced hypoxia causes vascular changes, fibrosis, and vessel compression, leading to tissue hypoxia. Hypoxia activates hypoxia-inducible factor (HIF)-1α/2α, promoting angiogenesis and glycolysis in tumor cells. TME changes due to hypoxia also involve immune suppressive cells like myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), and Tregs. The radiation microenvironment is involved in radiation resistance and holds a significant effect on the prognosis of patients with NPC. Exploring the radiation microenvironment provides new insights into RT and NPC research.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Role of Textural Analysis of Pretreatment 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Response Prediction in Esophageal Carcinoma Patients

Introduction

Positron emission tomography/computed tomography (PET/CT) is routinely used for staging, response assessment, and surveillance in esophageal carcinoma patients. The aim of this study was to investigate whether textural features of pretreatment 18F-fluorodeoxyglucose (18F-FDG) PET/CT images can contribute to prognosis prediction in carcinoma oesophagus patients.

Materials and Methods

This is a retrospective study of 30 diagnosed carcinoma esophagus patients. These patients underwent pretreatment 18F-FDG PET/CT for staging. The images were processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between progression group and nonprogression group. The original dataset was subject separately to receiver operating curve analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff values for textural features with a P < 0.05 for statistical significance. Feature selection was done with principal component analysis. The selected features of each evaluator were subject to 4 machine-learning algorithms. The highest area under the curve (AUC) values was selected for 10 features.

Results

A retrospective study of 30 primary carcinoma esophagus patients was done. Patients were followed up after chemo-radiotherapy and they underwent follow-up PET/CT. On the basis of their response, patients were divided into progression group and nonprogression group. Among them, 15 patients showed disease progression and 15 patients were in the nonprogression group. Ten textural analysis parameters turned out to be significant in the prediction of disease progression. Cutoff values were calculated for these parameters according to the ROC curves, GLZLM_long zone emphasis (Gray Level Zone Length Matrix)_long zone emphasis (44.9), GLZLM_low gray level zone emphasis (0.006), GLZLM_short zone low gray level emphasis (0.0032), GLZLM_long zone low gray level emphasis (0.185), GLRLM_long run emphasis (Gray Level Run Length Matrix) (1.31), GLRLM_low gray level run emphasis (0.0058), GLRLM_short run low gray level emphasis (0.005496), GLRLM_long run low gray level emphasis (0.00727), NGLDM_Busyness (Neighborhood Gray Level Difference Matrix) (0.75), and gray level co-occurrence matrix_homogeneity (0.37). Feature selection by principal components analysis and feature classification by the K-nearest neighbor machine-learning model using independent training and test samples yielded the overall highest AUC.

Conclusions

Textural analysis parameters could provide prognostic information in carcinoma esophagus patients. Larger multicenter studies are needed for better clinical prognostication of these parameters.

Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases

INTRODUCTION

Texture and radiomic analysis characterize the tumor's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-FDG PET/computed tomography (CT) images in differentiating hepatocellular carcinoma (HCC) and hepatic metastasis in patients with suspected liver tumors.

METHODS

This is a retrospective, single-center study of 30 patients who underwent FDG PET/CT for the characterization of liver lesions or for staging a suspected liver tumor. The histological diagnosis of either primary or metastatic tumor was obtained from CT-guided biopsy, ultrasound-guided biopsy, or surgical removal of a liver lesion. The PET/CT images were then processed in commercially available textural analysis software. Region of interest was drawn over the primary tumor with a 40% threshold and was processed further to derive 42 textural and radiomic parameters. These parameters were then compared between HCC group and hepatic metastases group. Receiver-operating characteristic (ROC) curves were used to identify cutoff values for textural features with a P value <0.05 for statistical significance.

RESULTS

A retrospective study of 30 patients with suspected liver tumors was done. After undergoing PET/CT, the histological diagnosis of these lesions was confirmed. Among these 30 patients, 15 patients had HCC, and 15 patients had hepatic metastases from various primary sites. Seven textural analysis parameters were significant in differentiating HCC from liver metastasis. Cutoff values were calculated for these parameters according to the ROC curves, standardized uptake value (SUV) Skewness (0.705), SUV Kurtosis (3.65), SUV Excess Kurtosis (0.653), gray-level zone length matrix_long zone emphasis (349.2), gray-level zone length matrix_long zone low gray-level emphasis (1.6), gray-level run length matrix_long run emphasis (1.38) and gray-level co-occurrence matrix_Homogeneity (0.406).

CONCLUSION

Textural analysis parameters could successfully differentiate HCC and hepatic metastasis non-invasively. Larger multi-center studies are needed for better clinical prognostication of these parameters.

Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma

PURPOSE

The aim of this study was to develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients.

PATIENTS AND METHODS

One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib was determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: SUVmax, metabolic tumor volume, and total lesion glycolysis.

RESULTS

The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), metabolic tumor volume (ROC AUC, 0.579; P = 0.412), total lesion glycolysis (ROC AUC, 0.576; P = 0.199), and a composite model built using all 3 (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model's performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomic model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001).

CONCLUSIONS

We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Hypoxia and the Tumor Microenvironment

Hypoxia is an important feature of the tumor microenvironment, and is closely associated with cell proliferation, angiogenesis, metabolism and the tumor immune response. All these factors can further promote tumor progression, increase tumor aggressiveness, enhance tumor metastatic potential and lead to poor prognosis. In this review, these effects of hypoxia on tumor biology will be discussed, along with their significance for tumor detection and treatment.

Influencing Factors and Prognostic Value of 18F-FDG PET/CT Metabolic and Volumetric Parameters in Non-Small Cell Lung Cancer

Objective

This study aims to explore factors influencing metabolic and volumetric parameters of [¹⁸F]fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging in non-small cell lung cancer (NSCLC) and the predictive value for prognosis of NSCLC.

Methods

Retrospective analysis was performed on 133 NSCLC patients who received ¹⁸F-FDG PET/CT imaging. After ¹⁸F-FDG injection at 3.7 MBq/kg, 1 h early imaging and 2 h delayed imaging were performed. The metabolic and volumetric parameters such as SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were measured. The tumor markers including CFYRA21-1, NSE, SCC-ag and the immunohistochemical biomarkers including Ki-67, P53 and CK-7 were examined. All patients were followed up for 24 months, and the 1-year and 2-year overall survival rate (OS) were recorded.

Results

There were significant differences in metabolic and volumetric parameters (SUVmax, SUVpeak, SULmax, SULpeak and TLG) between adenocarcinoma and squamous cell carcinoma of NSCLC. SUVmax, SUVpeak, SULmax, SULpeak, MTV and TLG were correlated with tumor marker NSE and TNM stage. MTV and TLG were related to CYFRA21-1, and only MTV was associated with SCC-ag. SUVpeak and SULmax were related to P53. In addition, early SULpeak and delayed MTV were significant prognostic factors of 1-year OS, while early SUVpeak, delayed TLG and delayed MTV were predictive factors of 2-year OS in NSCLC.

Conclusion

The metabolic and volumetric parameters of ¹⁸F-FDG PET/CT were related to a variety of factors such as NSE, CFYRA21-1, SCC-ag, P53 and TNM stage, and have a predictive value in prognosis of NSCLC.

Comparison of FDG and FMISO uptakes and distributions in head and neck squamous cell cancer tumors

Purpose

Glycolysis is increased by hypoxia, suggesting a possible correlation between the accumulation of 2-[18F]fluoro-2-deoxy-D-glucose (FDG) in malignant tumors and regional hypoxia defined by 1H-1-(3-[18F]fluoro-2-hydroxypropyl)-2-nitroimidazole (FMISO) PET. The aim of this study is to investigate the intra-tumoral spatial distribution and quantitative relationship between FDG and FMISO in a cohort of head and neck squamous cell cancer (HNSCC) patients.

Methods

Twenty HNSCC patients with 20 primary tumors and 19 metastatic lymph nodes (LNs) underwent FDG and FMISO PET within 1 week. The metabolic target volume (MTV) was defined on the FDG PET images using a region growing algorithm. The hypoxic volume (HV) was defined by the volume of voxels in an FMISO image within the MTV that satisfy a tumor-to-blood ratio (T/B) greater than 1.2. FDG and FMISO lesions were co-registered, and a voxel-by-voxel correlation between the two datasets was performed. FDG and FMISO TVs’ SUVs were also compared as well as the intra-tumoral homogeneity of the two radiotracers. Separate analysis was performed for the primary tumors and LNs.

Results

Twenty-six percent of the primary tumors and 15% of LNs showed a strong correlation (R > 0.7) between FDG and FMISO intra-tumor distributions when considering the MTV. For the HV, only 19% of primary tumors and 12% of LN were strongly correlated. A weak and moderate correlation existed between the two markers SUV_avg, and SUV_max in the case of the primary tumors, respectively. However, this was not the case for the LNs. Good concordances were also observed between the primary tumor’s and LNs HV SUV_avgs as well as between the corresponding hypoxic fractions (HF’s).

Conclusions

A moderate correlation between FDG and hypoxia radiotracer distribution, as measured by FMISO, seems to exist for primary tumors. However, discordant results were found in the case of LNs. Hypoxia appears to be the dominant driver of high FDG uptake in selected tumors only, and therefore FDG PET images cannot be used as a universal surrogate to identify or predict intra-tumor hypoxia.

FDG PET correlates weakly with HIF-1 expression in solid tumors: a meta-analysis

BACKGROUND

Hypoxia-inducible factor (HIF)-1α plays a key role in hypoxic adaptation of tumor cells. Overexpression of HIF-1α is associated with tumor aggressiveness and worse prognosis in several malignancies. Presumably, expression of HIF-1a may be reflected by positron emission tomography with 2-deoxy-2 [fluorine-18] fluoro-D-glucose (18F-FDG PET). There are inconsistent data about relationships between FDG PET and HIF-1α.

PURPOSE

To provide evident data about associations between maximum standardized uptake value (SUVmax) and HIF-1α expression in solid tumors.

MATERIAL AND METHODS

MEDLINE, SCOPUS, and EMBASE databases were screened for relationships between SUV and HIF-1α up to August 2019. Overall, 21 studies with 1154 patients were identified. The following data were extracted from the literature: authors; year of publication; number of patients; and correlation coefficients.

RESULTS

Correlation coefficients between SUVmax and HIF-1α were in the range of -0.51-0.71. The pooled correlation coefficient was 0.27 (95% confidence interval [CI] = 0.14-0.41). Furthermore, correlation coefficients for some tumor entities were calculated. For this sub-analysis, data for primary tumors with >2 reports were included. The calculated correlation coefficients in the analyzed subgroups were as follows: head and neck squamous cell carcinoma: ρ = 0.25 (95% CI = 0.07-0.42); non-small lung cell cancer: ρ = 0.27 (95% CI = -0.14-0.67); uterine cervical cancer: ρ = -0.09 (95% CI = -0.89-0.71); thymic tumors: ρ = 0.39 (95% CI = 0.04-0.58).

CONCLUSION

SUVmax of FDG PET correlated weakly with expression of HIF-1α both in overall sample and tumor subgroups. Therefore, FDG PET cannot be used for prediction of hypoxia in clinical practice.

Architectural control of metabolic plasticity in epithelial cancer cells

Metabolic plasticity enables cancer cells to switch between glycolysis and oxidative phosphorylation to adapt to changing conditions during cancer progression, whereas metabolic dependencies limit plasticity. To understand a role for the architectural environment in these processes we examined metabolic dependencies of cancer cells cultured in flat (2D) and organotypic (3D) environments. Here we show that cancer cells in flat cultures exist in a high energy state (oxidative phosphorylation), are glycolytic, and depend on glucose and glutamine for growth. In contrast, cells in organotypic culture exhibit lower energy and glycolysis, with extensive metabolic plasticity to maintain growth during glucose or amino acid deprivation. Expression of KRAS^G12V in organotypic cells drives glucose dependence, however cells retain metabolic plasticity to glutamine deprivation. Finally, our data reveal that mechanical properties control metabolic plasticity, which correlates with canonical Wnt signaling. In summary, our work highlights that the architectural and mechanical properties influence cells to permit or restrict metabolic plasticity.

Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms

Aim: To evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs). Methods: Forty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs. Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (p < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs (p > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs. Conclusion: In our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.

PET and MRI guided adaptive radiotherapy: Rational, feasibility and benefit

Adaptive radiotherapy (ART) corresponds to various replanning strategies aiming to correct for anatomical variations occurring during the course of radiotherapy. The goal of the article was to report the rational, feasibility and benefit of using PET and/or MRI to guide this ART strategy in various tumor localizations. The anatomical modifications defined by scanner taking into account tumour mobility and volume variation are not always sufficient to optimise treatment. The contribution of functional imaging by PET or the precision of soft tissue by MRI makes it possible to consider optimized ART. Today, the most important data for both PET and MRI are for lung, head and neck, cervical and prostate cancers. PET and MRI guided ART appears feasible and safe, however in a very limited clinical experience. Phase I/II studies should be therefore performed, before proposing cost-effectiveness comparisons in randomized trials and before using the approach in routine practice.

The Effect of Carbogen Breathing on 18F-FDG Uptake in Non-Small-Cell Lung Cancer

It has been reported that ¹⁸F-FDG uptake is higher in hypoxic cancer cells than in well-oxygenated cells. We demonstrated that ¹⁸F-FDG uptake in lung cancer would be affected by high concentration oxygen breathing. Methods. Overnight fasted non-small-cell lung cancer A549 subcutaneous (s.c.) xenografts bearing mice (n = 10) underwent ¹⁸F-FDG micro-PET scans, animals breathed room air on day 1, and same animals breathed carbogen (95% O₂ + 5% CO₂) on the subsequent day. In separated studies, autoradiography and immunohistochemical staining visualization of frozen section of A549 s.c. tumors were applied, and to compare between carbogen-breathing mice and those with air breathing, a combination of ¹⁸F-FDG and hypoxia marker pimonidazole was injected 1 h before animal sacrifice, and ¹⁸F-FDG accumulation was compared with pimonidazole binding and glucose transporter 1 (GLUT-1) expression. Results. PET studies revealed that tumor ¹⁸F-FDG uptake was significantly decreased in carbogen-breathing mice than those with air breathing (P < 0.05). Ex vivo studies confirmed that carbogen breathing significantly decreased hypoxic fraction detected by pimonidazole staining, referring to GLUT-1 expression, and significantly decreased ¹⁸F-FDG accumulation in tumors. Conclusions. High concentration of O₂ breathing during ¹⁸F-FDG uptake phase significantly decreases ¹⁸F-FDG uptake in non-small-cell lung cancer A549 xenografts growing in mice.

Voxel-based identification of local recurrence sub-regions from pre-treatment PET/CT for locally advanced head and neck cancers

Background

Overall, 40% of patients with a locally advanced head and neck cancer (LAHNC) treated by chemoradiotherapy (CRT) present local recurrence within 2 years after the treatment. The aims of this study were to characterize voxel-wise the sub-regions where tumor recurrence appear and to predict their location from pre-treatment ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images.

Materials and methods

Twenty-six patients with local failure after treatment were included in this study. Local recurrence volume was identified by co-registering pre-treatment and recurrent PET/CT images using a customized rigid registration algorithm. A large set of voxel-wise features were extracted from pre-treatment PET to train a random forest model allowing to predict local recurrence at the voxel level.

Results

Out of 26 expert-assessed registrations, 15 provided enough accuracy to identify recurrence volumes and were included for further analysis. Recurrence volume represented on average 23% of the initial tumor volume. The MTV with a threshold of 50% of SUVmax plus a 3D margin of 10 mm covered on average 89.8% of the recurrence and 96.9% of the initial tumor. SUV and MTV alone were not sufficient to identify the area of recurrence. Using a random forest model, 15 parameters, combining radiomics and spatial location, were identified, allowing to predict the recurrence sub-regions with a median area under the receiver operating curve of 0.71 (range 0.14–0.91).

Conclusion

As opposed to regional comparisons which do not bring enough evidence for accurate prediction of recurrence volume, a voxel-wise analysis of FDG-uptake features suggested a potential to predict recurrence with enough accuracy to consider tailoring CRT by dose escalation within likely radioresistant regions.

Exploring Tumor Heterogeneity Using PET Imaging: The Big Picture

Personalized medicine represents a major goal in oncology. It has its underpinning in the identification of biomarkers with diagnostic, prognostic, or predictive values. Nowadays, the concept of biomarker no longer necessarily corresponds to biological characteristics measured ex vivo but includes complex physiological characteristics acquired by different technologies. Positron-emission-tomography (PET) imaging is an integral part of this approach by enabling the fine characterization of tumor heterogeneity in vivo in a non-invasive way. It can effectively be assessed by exploring the heterogeneous distribution and uptake of a tracer such as 18F-fluoro-deoxyglucose (FDG) or by using multiple radiopharmaceuticals, each providing different information. These two approaches represent two avenues of development for the research of new biomarkers in oncology. In this article, we review the existing evidence that the measurement of tumor heterogeneity with PET imaging provide essential information in clinical practice for treatment decision-making strategy, to better select patients with poor prognosis for more intensive therapy or those eligible for targeted therapy.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

We investigated the association between the textural features obtained from ¹⁸F-FDG images, metabolic parameters (SUVmax_, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma

Objectives

The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL).

Methods

This retrospective study comprised nineteen non-Hodgkin's lymphoma (NHL) patients undergoing ¹⁸F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. ¹⁸F-FDG-PET images were reviewed independently by two board certificated radiologists of nuclear medicine, and the texture features were extracted from LifeX packages. The prognostic value of PET FDG-uptake parameters, patients' baseline characteristics, and textural parameters were analyzed using Kaplan–Meier analysis. Cox regression analysis was used to identify the independent prognostic factors among the imaging and clinical features.

Results

The overall survival of included patients was 18.84 ± 13.40 (mean ± SD) months. Univariate Cox analyses found that the tumor stage, GLCM (gray-level co-occurrence matrix) entropy, GLZLM_GLNU (gray-level nonuniformity), and GLZLM_ZLNU (zone length nonuniformity), values were significant predictors for OS. Among them, GLRLM_RLNU ≥216.6 demonstrated association with worse OS at multivariate analysis (HR 9.016, 95% CI 1.041–78.112, p=0.046).

Conclusions

The texture analysis of ¹⁸F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.

Prediction of occult lymph node metastasis using SUV, volumetric parameters and intratumoral heterogeneity of the primary tumor in T1-2N0M0 lung cancer patients staged by PET/CT

OBJECTIVE

The aim of this study was to identify whether PET/CT-related metabolic parameters of the primary tumor could predict occult lymph node metastasis (OLM) in patients with T1-2N0M0 NSCLC staged by ¹⁸F-FDG PET/CT.

METHODS

215 patients with clinical T1-2N0M0 (cT1-2N0M0) NSCLC who underwent both preoperative FDG PET/CT and surgical resection with the systematic lymph node dissection were included in the retrospective study. Heterogeneity factor (HF) was obtained by finding the derivative of the volume-threshold function from 40 to 80% of the maximum standardized uptake value (SUVmax). Univariate and multivariate stepwise logistic regression analyses were used to identify these PET parameters and clinicopathological variables associated with OLM.

RESULTS

Statistically significant differences were detected in sex, tumor site, SUVmax, mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis and HF between patients with adenocarcinoma (ADC) and squamous cell carcinoma (SQCC). OLM was detected in 36 (16.7%) of 215 patients (ADC, 27/152 = 17.8% vs. SQCC, 9/63 = 14.3%). In multivariate analysis, MTV (OR = 1.671, P = 0.044) in ADC and HF (OR = 8.799, P = 0.023) in SQCC were potent associated factors for the prediction of OLM. The optimal cutoff values of 5.12 cm³ for MTV in ADC, and 0.198 for HF in SQCC were determined using receiver operating characteristic curve analysis.

CONCLUSIONS

In conclusion, MTV was an independent predictor of OLM in cT1-2N0M0 ADC patients, while HF might be the most powerful predictor for OLM in SQCC. These findings would be helpful in selecting patients who might be considered as candidates for sublobar resection or new stereotactic ablative radiotherapy.

Texture analysis of computed tomography data using morphologic and metabolic delineation of esophageal cancer-relation to tumor type and neoadjuvant therapy response

The prognostic values of image-based tumor texture analysis based on computed tomography (CT) and of limiting the segmented tumor volume to metabolically active regions using fludeoxyglucose-positron emission tomography (FDG-PET) were studied in 25 patients with esophageal adenocarcinoma and 11 patients with squamous cell carcinoma. The aims of this study are to describe their CT-image-based texture characteristics before and after neoadjuvant therapy and to evaluate whether limiting the examined tumor volume to metabolically active regions detected with FDG-PET image data would further improve their value. Textural parameters (homogeneity, energy, entropy, contrast, and correlation) based on gray-level co-occurrence matrices (GLCM) were calculated for 3D volumes of segmented esophageal tumors before and after neoadjuvant chemotherapy or radiochemotherapy. Histopathological data after surgical resection and textural parameters before and after neoadjuvant treatment were compared using the Mann-Whitney U test. Significant differences in the textural parameters were observed between adenocarcinoma and squamous cell carcinoma for homogeneity, energy, inertia, and correlation. The use of contrast media during scanning resulted in significant differences in homogeneity, energy, entropy, and inertia for adenocarcinoma but not squamous cell carcinoma. There was also a significant difference in all textural parameters between pathological T status for ypT0-ypT2 and ypT3-ypT4 adenocarcinomas, but not in squamous cell carcinoma patients. No additional value was found from using PET image data to aid segmentation of CT images.

Diagnostic value of positron-emission tomography textural indices for malignancy of 18F-fluorodeoxyglucose-avid adrenal lesions

BACKGROUND

PET Textural indices could have an add-on diagnostic value for diagnosis of malignancy in patients with FDG-avid adrenal lesions.

METHODS

Consecutive patients referred for a FDG-PET/CT to our nuclear medicine department from June 2012 to June 2017 were retrospectively screened. Inclusion criteria were: patients with a FDG-avid adrenal lesion (uptake≥liver background); malignant/benign lesion confirmed histologically or with follow-up imaging examination. Pheochromocytomas were not included in the analysis. For each adrenal lesion, 5 quantitative PET parameters (SUV<inf>max</inf>, MTV, TLG, TLR<inf>max</inf> and TLRmean<inf>)</inf> were calculated. Thirty-seven textural indices were extracted using LIFEx software^®. Diagnostic performance to determine malignancy was assessed with a ROC analysis. Parameters with a significantly AUC>0.5 were selected and groups of highly correlated (r>0.8) parameters were created. A scoring system combining PET and textural indices was examined.

RESULTS

PET textural indices were calculated for 53 lesions (37 malignant, 16 benign). Three PET metabolic parameters (SUV<inf>max</inf>, TLR<inf>max</inf>, TLRmean) and 13 textural indices had an AUC>0.5. Seven groups of highly correlated parameters (r>0.8) were extracted. For PET parameters, SUV<inf>max</inf> had the best AUC (0.89 95% CI [0.79-0.98]; cut-off=7.0). For textural indices, ZLNU had the best AUC (0.87 95% CI [0.78-0.96]; cut-off=34.7) and specificity of 100%. Three scores combining the best four textural indices alone (Contrast<inf>GLCM</inf>, LRHGE, SZE and ZLNU) or with one PET parameters (SUV<inf>max</inf>, TLR<inf>max</inf>) were developed but did not increase the diagnostic performance (AUC≤0.89). ZLNU was the best parameter to distinguish primary adrenal cancer from adrenal metastases in malignant lesions (P<0.001).

CONCLUSIONS

Our study highlighted excellent diagnostic performance of several PET textural indices comparable to that of PET metabolic parameters. However, our results did not find any additional diagnostic value of textural indices when combined with metabolic parameters.

Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer

AIM

To assess the prognostic value of 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-based radiomics using a machine learning approach in patients with non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Ninety-three patients with stage I-III NSCLC who underwent combined PET/computed tomography (CT) followed by curative resection. A total of 35 unique quantitative radiomic features was extracted from the PET images, which included imaging phenotypes such as pixel intensity, shape, and texture. Radiomic features were ranked based on score according to their correlation with disease recurrence status within a 3-year follow-up. The recurrence risk classification performances of machine learning algorithms (random forest, neural network, naive Bayes, logistic regression, and support vector machine) using the 20 best-ranked features were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method.

RESULTS

Contrast and busyness texture features from neighbourhood grey-level difference matrix were found to be the two best predictors of disease recurrence. The random forest model obtained the best performance (AUC: 0.956, accuracy: 0.901, F1 score: 0.872, precision: 0.905, recall: 0.842), followed by the neural network model (AUC: 0.871, accuracy: 0.780, F1 score: 0.708, precision: 0.755, recall: 0.666).

CONCLUSION

A PET-based radiomic model was developed and validated for risk classification in NSCLC. The machine learning approach with random forest classifier exhibited good performance in predicting the recurrence risk. Radiomic features may help clinicians to improve the risk stratification for clinical practice.

Volumetric and texture analysis of pretherapeutic 18F-FDG PET can predict overall survival in medullary thyroid cancer patients treated with Vandetanib

PURPOSE

The metabolically most active lesion in 2-deoxy-2-(¹⁸F)fluoro-D-glucose (¹⁸F-FDG) PET/CT can predict progression-free survival (PFS) in patients with medullary thyroid carcinoma (MTC) starting treatment with the tyrosine kinase inhibitor (TKI) vandetanib. However, this metric failed in overall survival (OS) prediction. In the present proof of concept study, we aimed to explore the prognostic value of intratumoral textural features (TF) as well as volumetric parameters (total lesion glycolysis, TLG) derived by pre-therapeutic ¹⁸F-FDG PET.

METHODS

Eighteen patients with progressive MTC underwent baseline ¹⁸F-FDG PET/CT prior to and 3 months after vandetanib initiation. By manual segmentation of the tumor burden at baseline and follow-up PET, intratumoral TF and TLG were computed. The ability of TLG, imaging-based TF, and clinical parameters (including age, tumor marker doubling times, prior therapies and RET (rearranged during transfection) mutational status) for prediction of both PFS and OS were evaluated.

RESULTS

The TF Complexity and the volumetric parameter TLG obtained at baseline prior to TKI initiation successfully differentiated between low- and high-risk patients. Complexity allocated 10/18 patients to the high-risk group with an OS of 3.3 y (vs. low-risk group, OS = 5.3 y, 8/18, AUC = 0.78, P = 0.03). Baseline TLG designated 11/18 patients to the high-risk group (OS = 3.5 y vs. low-risk group, OS = 5 y, 7/18, AUC = 0.83, P = 0.005). The Hazard Ratio for cancer-related death was 6.1 for Complexity (TLG, 9.5). Among investigated clinical parameters, the age at initiation of TKI treatment reached significance for PFS prediction (P = 0.02, OS, n.s.).

CONCLUSIONS

The TF Complexity and the volumetric parameter TLG are both independent parameters for OS prediction.

Can the Efficacy of [18F]FDG-PET/CT in Clinical Oncology Be Enhanced by Screening Biomolecular Profiles?

Positron Emission Tomography (PET) is a functional imaging modality widely used in clinical oncology. Over the years the sensitivity and specificity of PET has improved with the advent of specific radiotracers, increased technical accuracy of PET scanners and incremental experience of Radiologists. However, significant limitations exist—most notably false positives and false negatives. Additionally, the accuracy of PET varies between cancer types and in some cancers, is no longer considered a standard imaging modality. This review considers the relative influence of macroscopic tumour features such as size and morphology on 2-Deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG) uptake by tumours which, though well described in the literature, lacks a comprehensive assessment of biomolecular features which may influence [¹⁸F]FDG uptake. The review aims to discuss the potential influence of individual molecular markers of glucose transport, glycolysis, hypoxia and angiogenesis in addition to the relationships between these key cellular processes and their influence on [¹⁸F]FDG uptake. Finally, the potential role for biomolecular profiling of individual tumours to predict positivity on PET imaging is discussed to enhance accuracy and clinical utility.

Update 2018: 18F-FDG PET/CT and PET/MRI in Head and Neck Cancer

There are recent advances, namely, a standardized method for reporting therapy response (Hopkins criteria), a multicenter prospective cohort study with excellent negative predictive value of F-FDG PET/CT for N0 clinical neck, a phase III multicenter randomized controlled study establishing the value of a negative posttherapy F-FDG PET/CT for patient management, a phase II randomized controlled study demonstrating radiation dose reduction strategies for human papilloma virus-related disease, and Food and Drug Administration approval of nivolumab for treatment of recurrent head and neck squamous cell carcinoma.

Lactic Acid Accumulation in the Tumor Microenvironment Suppresses 18F-FDG Uptake

The process by which tumor cells take up 2-[¹⁸F]fluoro-2-deoxy-D-glucose (FDG) is heterogeneous and influenced by a multitude of factors. In mouse tumor grafts, the core of the tumor often presents lower FDG uptake than the periphery. Whether this pattern is caused by the intrinsic avidity of individual cells for FDG, the density of viable cells in the tumor, or the perfusion of the radiotracer remains unknown. In this study, we used radioluminescence microscopy to measure FDG uptake in single cells isolated from the core and periphery of the tumor and found that differences in FDG uptake persist on the level of single cells. Single cells from the core of 4T1 and MDA-MB-231 tumors grafts took up 26% to 84% less FDG than those from the periphery. These differences were observed in mice with large tumors (>8 mm diameter) but not in those with smaller tumors. To explain the origin of these differences, we examined the influence of three microenvironmental factors on FDG uptake. Hypoxia was ruled out as a possible explanation because its presence in the core would increase and not decrease FDG uptake. Higher cell proliferation in the periphery was consistent with higher FDG uptake, but there was no evidence of a causal relationship. Finally, lactate was higher in the core of the tumor, and it suppressed FDG uptake in a dose-dependent fashion. We therefore conclude that lactic acidosis-the combination of lactate ion buildup and acidic pH-can increase the heterogeneity of FDG uptake in MDA-MB-231 and 4T1 tumor grafts. SIGNIFICANCE: Analysis of single cells from heterogeneous tumors reveals the role played by the tumor microenvironment, lactic acidosis in particular, on the uptake by tumor cells of ¹⁸F-FDG, a PET imaging agent.

18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters

OBJECTIVE:

This retrospective study examined whether the primary tumour 18F-FDG uptake features could predict the high-risk of recurrence in differentiated thyroid cancer (DTC) patients.

METHODS:

The enrolled 114 DTC patients underwent preoperative ¹⁸F-FDG-PET/CT. SUVmax, SUVmean, metabolic tumour volume (MTV), total lesion glycolysis (TLG) and 6 texture parameters were obtained. Because the texture features can be confounded by the tumour volume effects, ¹⁸F-FDG-avid tumour patients were divided into two groups (tumours with MTV ≤ 10.0 cm³ and >10.0 cm³). Diagnostic performance for predicting the high-risk was evaluated by the area under the curve (AUC) by the ROC curve analysis.

RESULTS:

Eighty eight ¹⁸F-FDG-avid tumours revealed more advanced-risk classification (p = 0.015 → 0.02) than 26 ¹⁸F-FDG-nonavid tumours, which yielded no high-risk patients. In the 44 MTV > 10.0 cm^{3 18}F-FDG-avid tumour patients, 8 high-risk patients revealed significantly higher SUVmax, SUVmean, MTV, TLG, intensity variability and size-zone variability, and lower zone percentage than 36 non-high-risk patients (p < 0.001-0.016). Their AUC (diagnostic accuracy) ranged between 0.77 (66%) and 0.92 (91%). When each parameter was scored as 0 (negative for high-risk) or 1 (positive for high-risk) according to each threshold criterion, and the 7 parameter summed score ≥5 was defined as high-risk, the accuracy was 93.2% (AUC: 0.98) in the MTV > 10.0 cm^{3 18}F-FDG-avid tumour patients.

CONCLUSION:

For primary MTV > 10.0 cm^{3 18}F-FDG-avid DTCs, the combined use of SUV-related, volumetric, and texture parameters may be more useful to identify high-risk patients than the individual parameters.

ADVANCES IN KNOWLEDGE:

Combined use of SUV-related, volumetric, and texture parameters may be useful to identify high-risk DTC patients.

Adaptive radiotherapy for head and neck cancer

INTRODUCTION

Large anatomical variations can be observed during the treatment course intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC), leading to potential dose variations. Adaptive radiotherapy (ART) uses one or several replanning sessions to correct these variations and thus optimize the delivered dose distribution to the daily anatomy of the patient. This review, which is focused on ART in the HNC, aims to identify the various strategies of ART and to estimate the dosimetric and clinical benefits of these strategies.

MATERIAL AND METHODS

We performed an electronic search of articles published in PubMed/MEDLINE and Science Direct from January 2005 to December 2016. Among a total of 134 articles assessed for eligibility, 29 articles were ultimately retained for the review. Eighteen studies evaluated dosimetric variations without ART, and 11 studies reported the benefits of ART.

RESULTS

Eight in silico studies tested a number of replanning sessions, ranging from 1 to 6, aiming primarily to reduce the dose to the parotid glands. The optimal timing for replanning appears to be early during the first two weeks of treatment. Compared to standard IMRT, ART decreases the mean dose to the parotid gland from 0.6 to 6 Gy and the maximum dose to the spinal cord from 0.1 to 4 Gy while improving target coverage and homogeneity in most studies. Only five studies reported the clinical results of ART, and three of those studies included a non-randomized comparison with standard IMRT. These studies suggest a benefit of ART in regard to decreasing xerostomia, increasing quality of life, and increasing local control. Patients with the largest early anatomical and dose variations are the best candidates for ART.

CONCLUSION

ART may decrease toxicity and improve local control for locally advanced HNC. However, randomized trials are necessary to demonstrate the benefit of ART before using the technique in routine practice.

Metabolic heterogeneity on baseline 18FDG-PET/CT scan is a predictor of outcome in primary mediastinal B-cell lymphoma

An important unmet need in the management of primary mediastinal B-cell lymphoma (PMBCL) is to identify the patients for whom first-line therapy will fail to intervene before the lymphoma becomes refractory. High heterogeneity of intratumoral 18F-fluorodeoxyglucose (18FDG) uptake distribution on positron emission tomography/computed tomography (PET/CT) scans has been suggested as a possible marker of chemoresistance in solid tumors. In the present study, we investigated the prognostic value of metabolic heterogeneity (MH) in 103 patients with PMBCL prospectively enrolled in the International Extranodal Lymphoma Study Group (IELSG) 26 study, aimed at clarifying the role of PET in this lymphoma subtype. MH was estimated using the area under curve of cumulative standardized uptake value-volume histogram (AUC-CSH) method. Progression-free survival at 5 years was 94% vs 73% in low- and high-MH groups, respectively (P = .0001). In a Cox model of progression-free survival including dichotomized MH, metabolic tumor volume, total lesion glycolysis (TLG), international prognostic index, and tumor bulk (mediastinal mass > 10 cm), as well as age as a continuous variable, only TLG (P < .001) and MH (P < .001) retained statistical significance. Using these 2 features to construct a simple prognostic model resulted in early and accurate (positive predictive value, 89%; negative predictive value, ≥90%) identification of patients at high risk for progression at a point that would allow the use of risk-adapted treatments. This may provide an important opportunity for the design of future trials aimed at helping the minority of patients who harbor chemorefractory PMBCL. The study is registered at ClinicalTrials.gov as NCT00944567.

Revisit 18F-fluorodeoxyglucose oncology positron emission tomography: "systems molecular imaging" of glucose metabolism

¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography has become an important tool for detection, staging and management of many types of cancer. Oncology application of ¹⁸F-FDG bases on the knowledge that increase in glucose demand and utilization is a fundamental features of cancer. Pasteur effect, Warburg effect and reverse Warburg effect have been used to explain glucose metabolism in cancer. ¹⁸F-FDG accumulation in cancer is reportedly microenvironment-dependent, ¹⁸F-FDG avidly accumulates in poorly proliferating and hypoxic cancer cells, but low in well perfused (and proliferating) cancer cells. Cancer is a heterogeneous and complex “organ” containing multiple components, therefore, cancer needs to be investigated from systems biology point of view, we proposed the concept of “systems molecular imaging” for much better understanding systems biology of cancer.

This article revisits ¹⁸F-FDG uptake mechanisms, its oncology applications and the role of ¹⁸F-FDG PET for “systems molecular imaging”.

18F-deoxyglucose positron emission tomography/computed tomography to predict local failure in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) patients are at risk for local failure (LF) following treatment. Predicting tumor regions at high risk for local failure before radiotherapy may increase treatment efficacy by permitting an escalated radiation dose specifically to those regions critical for tumor control. Forty-one patients with non-resectable locally advanced ESCC underwent ¹⁸F-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT) imaging before concurrent chemoradiotherapy (CCRT). After CCRT, a second (failure) FDG PET/CT was performed in cases of relapse. Failure FDG PET/CT scans were fused to pre-treatment scans to identify tumor regions at high risk for LF. Within a median follow-up time of 26 months, 20 patients (48.8%) had LF. In 19 patients, the failure occurred within a pre-treatment high FDG uptake region; the failure occurred outside these regions in only one patient. Pre-treatment metabolic tumor volume (MTV) was independently associated with LF (P<0.001, HR 1.128, 95% CI: 1.061–1.198). LF was more likely in patients with MTVs ≥27 cm³. In initial PET/CT images, when 50% maximum standardized uptake value (SUV_max) was used as the threshold, delineated subvolumes overlapped LF regions. These results confirm that LF occurs most commonly within pre-treatment high FDG uptake regions.

In vivo evaluation of the effects of simultaneous inhibition of GLUT-1 and HIF-1α by antisense oligodeoxynucleotides on the radiosensitivity of laryngeal carcinoma using micro 18F-FDG PET/CT

Purpose

Hypoxia-inducible factor 1α (HIF-1α) and glucose transporter-1 (GLUT-1) are two important hypoxic markers associated with the radioresistance of cancers including laryngeal carcinoma. We evaluated whether the simultaneous inhibition of GLUT-1 and HIF-1α expression improved the radiosensitivity of laryngeal carcinoma. We explored whether the expression of HIF-1α and GLUT-1 was correlated with 2′-deoxy-2’-[18F]fluoro-D-glucose (¹⁸F-FDG) uptake and whether ¹⁸F-FDG positron emission tomography-computed tomography (PET/CT) was appropriate for early evaluation of the response of laryngeal carcinoma to targeted treatment in vivo.

Materials and Methods

To verify the above hypotheses, an in vivo model was applied by subcutaneously injecting Hep-2 (2 × 107/mL × 0.2 mL) and Tu212 cells (2 × 107/mL × 0.2 mL) into nude mice. The effects of HIF-1α antisense oligodeoxynucleotides (AS-ODNs) (100 μg) and GLUT-1 AS-ODNs (100 μg) on the radiosensitivity of laryngeal carcinoma were assessed by tumor volume and weight, microvessel density (MVD), apoptosis index (AI) and necrosis in vivo based on a full factorial (2³) design. ¹⁸F-FDG-PET/CT was taken before and after the treatment of xenografts. The relationships between HIF-1α and GLUT-1 expression and ¹⁸F-FDG uptake in xenografts were estimated and the value of ¹⁸F-FDG-PET/CT was assessed after treating the xenografts.

Results

10 Gy X-ray irradiation decreased the weight of Hep-2 xenografts 8 and 12 days after treatment, and the weights of Tu212 xenografts 8 days after treatment. GLUT-1 AS-ODNs decreased the weight of Tu212 xenografts 12 days after treatment. There was a synergistic interaction among the three treatments (GLUT-1 AS-ODNs, HIF-1α AS-ODNs and 10Gy X-ray irradiation) in increasing apoptosis, decreasing MVD, and increasing necrosis in Hep-2 xenografts 8 days after treatment (p < 0.05) and in Tu212 xenografts 12 days after treatment (p < 0.001). Standardized uptake value (tumor/normal tissue)( SUVmaxT/N) did not show a statistically significant correlation with GLUT1 and HIF-1α expression and therapeutic effect (necrosis, apoptosis).

Conclusions

Simultaneous inhibition of HIF-1α and GLUT-1 expression might increase the radiosensitivity of laryngeal carcinoma, decreasing MVD, and promoting apoptosis and necrosis. ¹⁸F-FDG-PET/CT wasn't useful in evaluating the therapeutic effect on laryngeal cancer in this animal study.

Texture analysis of 18F-FDG PET/CT for grading thymic epithelial tumours: usefulness of combining SUV and texture parameters

OBJECTIVE

To retrospectively investigate the standardized uptake value (SUV)-related and heterogeneous texture parameters individually and in combination for differentiating between low- and high-risk ¹⁸Fluorone-fludeoxyglucose (¹⁸F-FDG)-avid thymic epithelial tumours (TETs) with positron emission tomography (PET)/CT.

METHODS

SUV-related and 6 texture parameters (entropy, homogeneity, dissimilarity, intensity variability, size-zone variability and zone percentage) were compared between 11 low-risk and 23 high-risk TETs (metabolic tumour volume >10.0 cm³ and SUV ≥2.5). Diagnostic performance was evaluated by receiver operating characteristic analysis. The diagnostic value of combining SUV and texture parameters was examined by a scoring system.

RESULTS

High-risk TETs were significantly higher in SUVmax (p = 0.022), entropy (p = 0.038), intensity variability (p = 0.041) and size-zone variability (p = 0.045) than low-risk TETs. Diagnostic accuracies of these 4 parameters, dissimilarity and zone percentage which also showed significance in receiver operating characteristic analysis ranged between 64.7 and 73.5% without significant differences in AUC (range; 0.71 to 0.75) (p ≥ 0.05 each). Each parameter was scored as 0 (negative for high-risk) or 1 (positive for high-risk) according to each threshold criterion, then scores were summed [0 or 1 for low-risk TETs (median; 1); ≥2 for high-risk TETs (median; 4)]. The sensitivity, specificity and accuracy of detecting high-risk TETs were 100, 81.8 and 94.1%, respectively, with an AUC of 0.99.

CONCLUSION

The diagnostic performances of individual SUVmax and texture parameters were relatively low. However, combining these parameters can significantly increase diagnostic performance when differentiating between relatively large low- and high-risk ¹⁸F-FDG-avid TETs. Advances in knowledge: Combined use of SUVmax and texture parameters can significantly increase the diagnostic performance when differentiating between low- and high-risk TETs.

Texture analysis of FDG PET/CT for differentiating between FDG-avid benign and metastatic adrenal tumors: efficacy of combining SUV and texture parameters

PURPOSE

To retrospectively investigate the SUV-related and texture parameters individually and in combination for differentiating between F-18-fluorodeoxyglucose (FDG)-avid benign and metastatic adrenal tumors with PET/CT.

METHODS

Thirteen benign adrenal tumors (BATs) and 22 metastatic adrenal tumors (MATs) with a metabolic tumor volume (MTV) > 10.0 cm³ and SUV ≥ 2.5 were included. SUVmax, MTV, total lesion glycolysis, and four textural parameters [entropy, homogeneity, intensity variability (IV), and size-zone variability] were obtained. These parameters were compared between BATs and MATs using Mann-Whitney U test, and the diagnostic performance was evaluated by the area under the curve (AUC) values derived from the receiver operating characteristic analysis. The diagnostic value of combining SUV and texture parameters was examined using a scoring system.

RESULTS

MATs showed significantly higher SUV_max (p = 0.004), entropy (p = 0.013), IV (p = 0.006), and lower homogeneity (p = 0.019) than BATs. The accuracies for diagnosing MATs were 82.9, 82.9, 85.7, and 71.4% for SUV_max, entropy, IV, and homogeneity, respectively. No significant differences in AUC were found among these parameters (p > 0.05 each). When each parameter was scored as 0 (negative for malignancy) and 1 (positive for malignancy) according to each threshold criterion and the four parameter summed scores 0, 1, and 2 were defined as benignity and 3 and 4 as malignancy, the sensitivity and specificity and accuracy to predict MATs were 100% (22/22), 84.6% (11/13), and 94.3% (33/35), respectively, with 0.97 of the AUC.

CONCLUSION

The combined use of SUV_max and texture parameters has a potential to significantly increase the diagnostic performance to differentiate between large FDG-avid BATs and MATs.

Prognostic value of intratumoral metabolic heterogeneity on F-18 fluorodeoxyglucose positron emission tomography/computed tomography in locally advanced cervical cancer patients treated with concurrent chemoradiotherapy

OBJECTIVE

To evaluate the prognostic value for predicting tumor recurrence of intratumoral metabolic heterogeneity and traditional quantitative metabolic parameters on pre-treatment F-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

Ninety-three patients with biopsy-proven cervical cancer and treated with CCRT (FIGO stage IIB-IV) were enrolled in this study. The traditional metabolic parameters of the primary tumor, regional lymph node, and whole body (maximum standardized uptake value [SUVmax], metabolic tumor volume [MTV], and total lesion glycolysis), and intratumoral heterogeneity factor (HF) were measured on pre-treatment 18F-FDG PET/CT images. Univariate and multivariate analyses for disease-free survival (DFS) were performed using clinical and metabolic parameters. The additional HF prognostic value was evaluated by means of time-dependent receiver operating characteristic curve, integrated discrimination improvement, and net reclassification improvement.

RESULTS

On multivariate analysis, nodal SUVmax (hazard ratio 3.60; 95% CI, 1.66-7.85; p = 0.0012) and whole body MTV (WBMTV; hazard ratio 3.15; 95% CI, 1.17-8.53; p = 0.0236) were significant prognostic factors for DFS. When HF was combined with nodal SUVmax and WBMTV, a significant improvement in discrimination for recurrence was observed compared with nodal SUVmax alone (area under curve 0.817 vs. 0.732; p = 0.0028).

CONCLUSIONS

HF did not show superiority over traditional metabolic parameters. However, when HF was combined with nodal SUVmax and WBMTV, the predictive value for tumor recurrence improved. Therefore, HF may be a useful additional prognostic biomarker to improve the prognostic value of traditional metabolic parameters on 18F-FDG PET/CT.

Mismatched intratumoral distribution of [18F] fluorodeoxyglucose and 3'-deoxy-3'-[18F] fluorothymidine in patients with lung cancer

In a mouse model of human lung cancer, intratumoral distribution between 3′-deoxy-3′-[¹⁸F] fluorothymidine (¹⁸F-FLT) and [¹⁸F] fluorodeoxyglucose (¹⁸F-FDG) was mutually exclusive. ¹⁸F-FLT primarily accumulated in proliferating cancer cells, whereas ¹⁸F-FDG accumulated in hypoxic cancer cells. The aim of the present study was to evaluate these preclinical findings in patients with lung cancer. A total of 55 patients with solitary pulmonary lesion were included in the present study. Patients underwent ¹⁸F-FLT positron emission tomography-computed tomography (PET/CT) and ¹⁸F-FDG PET/CT scan with a 3-day interval. The final diagnosis was based on histological examination. Among the 55 cases, a total of 24 cases were confirmed as malignant lesions. Mismatched ¹⁸F-FLT- and ¹⁸F-FDG-accumulated regions were observed in 19 cases (79%) and matched in 5 (21%). Among the 31 benign lesions, ¹⁸F-FLT and ¹⁸F-FDG were mismatched in 12 cases (39%) and matched in 19 (61%). The difference in intratumoral distribution of ¹⁸F-FLT and ¹⁸F-FDG between malignant and benign lesions was statistically significant (P<0.05). The results of the present study indicate that a mismatch in intratumoral distribution of ¹⁸F-FLT and ¹⁸F-FDG may be a feature of patients with lung cancer. Increased ¹⁸F-FDG accumulation may serve as an indicator of tumor hypoxia, whereas regions with increased ¹⁸F-FLT uptake may be associated with an increased rate of cancer cell proliferation in patients with lung cancer.