Tumor Delineation and Quantitative Assessment of Glucose Metabolic Rate within Histologic Subtypes of Non-Small Cell Lung Cancer by Using Dynamic 18F Fluorodeoxyglucose PET

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

OBJECTIVES

18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.

METHODS

We included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.

RESULTS

In the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523-0.802].

CONCLUSION

Although both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Dynamic FDG-PET imaging for differentiating metastatic from non-metastatic lymph nodes of lung cancer

OBJECTIVES

18F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer.

METHODS

One hundred and eight patients with pulmonary nodules were enrolled to perform 18F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K1, k2, k3 and Ki and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was computed subsequently as a separate marker. We further divided the LNs into mediastinal LNs (N=82) and pulmonary hilar LNs (N=53). Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analysis was performed on each parameter to compare the diagnostic efficacy in differentiating lymph node metastases from inflammatory uptake. P<0.05 were considered statistically significant.

RESULTS

Among the 135 FDG-avid LNs confirmed by pathology, 49 LNs were non-metastatic, and 86 LNs were metastatic. LN-SUVmax, MBP-SUVmax, LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter couldn't well differentiate metastatic from non-metastatic LNs (P>0.05). However, LN-SUVmax/PT-SUVmax have good performance in the differential diagnosis of non-metastatic and metastatic LNs (P=0.039). Dynamic metabolic parameters in addition to k3, the parameters including K1, k2, Ki, and Ki/K1, on the other hand, have good performance in the differential diagnosis of metastatic and non-metastatic LNs (P=0.045, P=0.001, P=0.001, P=0.001, respectively). For ROC analysis, the metabolic parameters Ki (AUC of 0.672 [0.579-0.765], sensitivity 0.395, specificity 0.918) and Ki/K1 (AUC of 0.673 [0.580-0.767], sensitivity 0.570, specificity 0.776) have good performance in the differential diagnosis of metastatic from non-metastatic LNs than SUVmax (AUC of 0.596 [0.498-0.696], sensitivity 0.826, specificity 0.388), included the mediastinal region and pulmonary hilar region.

CONCLUSION

Compared with SUVmax, quantitative parameters such as K1, k2, Ki and Ki/K1 showed promising results for differentiation of metastatic and non-metastatic LNs with high uptake. The Ki and Ki/K1 had a high differential diagnostic value both in the mediastinal region and pulmonary hilar region.

Parametric Imaging With Dynamic PET for Oncological Applications: Protocols, Interpretation, Current Applications and Limitations for Clinical Use

Nuclear medicine imaging modalities, and in particular positron emission tomography (PET), provide functional images that demonstrate the mean radioactivity distribution at a defined point in time. With the help of mathematical model's, it is possible to depict isolated parameters of the radiotracers' pharmacokinetics and to visualize them. These so called parametric images add a new dimension to the existing conventional PET images and provide more detailed information about the tracer distribution over time and space. Prerequisite for the calculation of parametric images, which reflect specific pharmacokinetic parameters, is the dynamic PET (dPET) data acquisition. Hitherto, PET parametric imaging has mainly found use for research purposes. However, it has not been yet implemented into clinical routine, since it is more time-consuming, it requires a complicated analysis and still lacks a clear benefit over conventional PET imaging. However, the recent introduction of new PET-CT scanners with an ultralong field of view, which allow a faster data acquisition and are associated with higher sensitivity, as well as the development of more sophisticated evaluation software packages will probably lead to a renaissance of dPET and parametric maps even of the whole body. The implementation of dPET imaging in daily routine with appropriate acquisition protocols, as well as the calculation, interpretation and potential clinical applications of parametric images will be discussed in this review article.

Cancer as a Model System for Testing Metabolic Scaling Theory

Biological allometries, such as the scaling of metabolism to mass, are hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to blood flow. Metabolic scaling theory argues two guiding principles—conservation of fluid flow and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Yet, mostly absent from past efforts are studies that directly, and independently, measure metabolic rate from respiration and vascular architecture for the same organ, organism, or tissue. Lack of these measures may lead to inconsistent results and conclusions about metabolism, growth, and allometric scaling. We present simultaneous and consistent measurements of metabolic scaling exponents from clinical images of lung cancer, serving as a first-of-its-kind test of metabolic scaling theory, and identifying potential quantitative imaging biomarkers indicative of tumor growth. We analyze data for 535 clinical PET-CT scans of patients with non-small cell lung carcinoma to establish the presence of metabolic scaling between tumor metabolism and tumor volume. Furthermore, we use computer vision and mathematical modeling to examine predictions of metabolic scaling based on the branching geometry of the tumor-supplying blood vessel networks in a subset of 56 patients diagnosed with stage II-IV lung cancer. Examination of the scaling of maximum standard uptake value with metabolic tumor volume, and metabolic tumor volume with gross tumor volume, yield metabolic scaling exponents of 0.64 (0.20) and 0.70 (0.17), respectively. We compare these to the value of 0.85 (0.06) derived from the geometric scaling of the tumor-supplying vasculature. These results: (1) inform energetic models of growth and development for tumor forecasting; (2) identify imaging biomarkers in vascular geometry related to blood volume and flow; and (3) highlight unique opportunities to develop and test the metabolic scaling theory of ecology in tumors transitioning from avascular to vascular geometries.

The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis

Background: Central necrosis can be detected on [¹⁸F]FDG PET/CT as a region with little to no tracer uptake. Currently, there is no consensus regarding the inclusion of regions of central necrosis during volume of interest (VOI) delineation for radiomic analysis. The aim of this study was to assess how central necrosis affects radiomic analysis in PET. Methods: Forty-three patients, either with non-small cell lung carcinomas (NSCLC, n = 12) or with pheochromocytomas or paragangliomas (PPGL, n = 31), were included retrospectively. VOIs were delineated with and without central necrosis. From all VOIs, 105 radiomic features were extracted. Differences in radiomic features between delineation methods were assessed using a paired t-test with Benjamini–Hochberg multiple testing correction. In the PPGL cohort, performances of the radiomic models to predict the noradrenergic biochemical profile were assessed by comparing the areas under the receiver operating characteristic curve (AUC) for both delineation methods. Results: At least 65% of the features showed significant differences between VOI_vital-tumour and VOI_gross-tumour (65%, 79% and 82% for the NSCLC, PPGL and combined cohort, respectively). The AUCs of the radiomic models were not significantly different between delineation methods. Conclusion: In both tumour types, almost two-third of the features were affected, demonstrating that the impact of whether or not to include central necrosis in the VOI on the radiomic feature values is significant. Nevertheless, predictive performances of both delineation methods were comparable. We recommend that radiomic studies should report whether or not central necrosis was included during delineation.

The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis

BACKGROUND

Central necrosis can be detected on [¹⁸F]FDG PET/CT as a region with little to no tracer uptake. Currently, there is no consensus regarding the inclusion of regions of central necrosis during volume of interest (VOI) delineation for radiomic analysis. The aim of this study was to assess how central necrosis affects radiomic analysis in PET.

METHODS

Forty-three patients, either with non-small cell lung carcinomas (NSCLC, n = 12) or with pheochromocytomas or paragangliomas (PPGL, n = 31), were included retrospectively. VOIs were delineated with and without central necrosis. From all VOIs, 105 radiomic features were extracted. Differences in radiomic features between delineation methods were assessed using a paired t-test with Benjamini-Hochberg multiple testing correction. In the PPGL cohort, performances of the radiomic models to predict the noradrenergic biochemical profile were assessed by comparing the areas under the receiver operating characteristic curve (AUC) for both delineation methods.

RESULTS

At least 65% of the features showed significant differences between VOI_vital-tumour and VOI_gross-tumour (65%, 79% and 82% for the NSCLC, PPGL and combined cohort, respectively). The AUCs of the radiomic models were not significantly different between delineation methods.

CONCLUSION

In both tumour types, almost two-third of the features were affected, demonstrating that the impact of whether or not to include central necrosis in the VOI on the radiomic feature values is significant. Nevertheless, predictive performances of both delineation methods were comparable. We recommend that radiomic studies should report whether or not central necrosis was included during delineation.

Adding the temporal domain to PET radiomic features

Background

Radiomic features, extracted from positron emission tomography, aim to characterize tumour biology based on tracer intensity, tumour geometry and/or tracer uptake heterogeneity. Currently, radiomic features are derived from static images. However, temporal changes in tracer uptake might reveal new aspects of tumour biology. This study aims to explore additional information of these novel dynamic radiomic features compared to those derived from static or metabolic rate images.

Methods

Thirty-five patients with non-small cell lung carcinoma underwent dynamic [¹⁸F]FDG PET/CT scans. Spatial intensity, shape and texture radiomic features were derived from volumes of interest delineated on static PET and parametric metabolic rate PET. Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features, assessing the temporal domain unidirectionally, were calculated on eight and sixteen time frames of equal length. Spearman’s rank correlations of parametric and dynamic features with static features were calculated to identify features with potential additional information. Survival analysis was performed for the non-redundant temporal features and a selection of static features using Kaplan-Meier analysis.

Results

Three out of 90 parametric features showed moderate correlations with corresponding static features (ρ≥0.61), all other features showed high correlations (ρ>0.7). Dynamic features are robust independent of frame duration. Five out of 22 dynamic GLCM features showed a negligible to moderate correlation with any static feature, suggesting additional information. All sixteen dynamic GLRLM features showed high correlations with static features, implying redundancy. Log-rank analyses of Kaplan-Meier survival curves for all features dichotomised at the median were insignificant.

Conclusion

This study suggests that, compared to static features, some dynamic GLCM radiomic features show different information, whereas parametric features provide minimal additional information. Future studies should be conducted in larger populations to assess whether there is a clinical benefit of radiomics using the temporal domain over traditional radiomics.

Glucose and glutamine metabolism in relation to mutational status in NSCLC histological subtypes

BACKGROUND

Both hypoxia and oncogenic mutations rewire tumor metabolism. In this study, glucose and glutamine metabolism-related markers were examined in stage I - resectable stage IIIA non-small cell lung cancer (NSCLC). Furthermore, expression of metabolism-related markers was correlated with mutational status to examine mutations associated with rewired tumor metabolism.

METHODS

Mutation analysis was performed for 97 tumors. Glucose and glutamine metabolism-related marker expression was measured by immunofluorescent staining (protein) and qPCR (mRNA) (n = 81).

RESULTS

Glutamine metabolism-related markers were significantly higher in adeno- than squamous cell NSCLCs. Glucose transporter 1 (GLUT1) protein expression was higher in solid compared to lepidic adenocarcinomas (P < 0.01). In adenocarcinomas, mRNA expression of glutamine transporter SLC1A5 correlated with tumor size (r(p) = 0.41, P = 0.005). Furthermore, SLC1A5 protein expression was significantly higher in adenocarcinomas with worse pTNM stage (r(s) = 0.39, P = 0.009). EGFR-mutated tumors showed lower GLUT1 protein (P = 0.017), higher glutaminase 2 (GLS2) protein (P = 0.025) and higher GLS2 mRNA expression (P = 0.004), compared to EGFR wild-type tumors. GLS mRNA expression was higher in KRAS-mutated tumors (P = 0.019). TP53-mutated tumors showed higher GLUT1 expression (P = 0.009).

CONCLUSIONS

NSCLC is a heterogeneous disease, with differences in mutational status and metabolism-related marker expression between adeno- and squamous cell NSCLCs, and also within adenocarcinoma subtypes. GLUT1 and SLC1A5 expression correlate with aggressive tumor behavior in adenocarcinomas but not in squamous cell NSCLCs. Therefore, these markers could steer treatment modification for subgroups of adenocarcinoma patients. TP53, EGFR and KRAS mutations are associated with expression of glucose and glutamine metabolism-related markers in NSCLC.

Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC

Radiomics has become an area of interest for tumor characterization in ¹⁸F-Fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.

Metabolic Subtyping of Pheochromocytoma and Paraganglioma by 18F-FDG Pharmacokinetics Using Dynamic PET/CT Scanning

Static single-time-frame ¹⁸F-FDG PET/CT is useful for the localization and functional characterization of pheochromocytomas and paragangliomas (PPGLs). ¹⁸F-FDG uptake varies between PPGLs with different genotypes, and the highest SUVs are observed in cases of succinate dehydrogenase (SDH) mutations, possibly related to enhanced aerobic glycolysis in tumor cells. The exact determinants of ¹⁸F-FDG accumulation in PPGLs are unknown. We performed dynamic PET/CT scanning to assess whether in vivo ¹⁸F-FDG pharmacokinetics has added value over static PET to distinguish different genotypes. Methods: Dynamic ¹⁸F-FDG PET/CT was performed on 13 sporadic PPGLs and 13 PPGLs from 11 patients with mutations in SDH complex subunits B and D, von Hippel-Lindau (VHL), RET, and neurofibromin 1 (NF1). Pharmacokinetic analysis was performed using a 2-tissue-compartment tracer kinetic model. The derived transfer rate-constants for transmembranous glucose flux (K ₁ [in], k ₂ [out]) and intracellular phosphorylation (k ₃), along with the vascular blood fraction (V_b), were analyzed using nonlinear regression analysis. Glucose metabolic rate (MR_glc) was calculated using Patlak linear regression analysis. The SUV_max of the lesions was determined on additional static PET/CT images. Results: Both MR_glc and SUV_max were significantly higher for hereditary cluster 1 (SDHx, VHL) tumors than for hereditary cluster 2 (RET, NF1) and sporadic tumors (P < 0.01 and P < 0.05, respectively). Median k ₃ was significantly higher for cluster 1 than for sporadic tumors (P < 0.01). Median V_b was significantly higher for cluster 1 than for cluster 2 tumors (P < 0.01). No statistically significant differences in K ₁ and k ₂ were found between the groups. Cutoffs for k ₃ to distinguish between cluster 1 and other tumors were established at 0.015 min^-1 (100% sensitivity, 15.8% specificity) and 0.636 min^-1 (100% specificity, 85.7% sensitivity). MR_glc significantly correlated with SUV_max (P = 0.001) and k ₃ (P = 0.002). Conclusion: In vivo metabolic tumor profiling in patients with PPGL can be achieved by assessing ¹⁸F-FDG pharmacokinetics using dynamic PET/CT scanning. Cluster 1 PPGLs can be reliably identified by a high ¹⁸F-FDG phosphorylation rate.

CD147-mediated glucose metabolic regulation contributes to the predictive role of 18 F-FDG PET/CT imaging for EGFR-TKI treatment sensitivity in NSCLC

The aim of this study is to investigate the role of CD147 in glucose metabolic regulation and its association with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment sensitivity prediction using ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG) PET/CT imaging in non-small cell lung cancer (NSCLC). In this study, four human NSCLC cell lines with different EGFR-TKI responses were used to detect p-EGFR/EGFR and CD147 expression via Western blotting and flow cytometric analyses. Radioactive uptake of ¹⁸ F-FDG by established stable NSCLC cell lines (HCC827, H1975) with different levels of CD147 expression and the corresponding xenografts was assessed through γ-radioimmunoassays in vitro and micro-PET/CT imaging in vivo to study the role of CD147 in glucose metabolic reprogramming. Correlation analyses were performed to investigate the association between CD147 expression and PD-L1 expression in stable NSCLC cell lines. Higher CD147 expression was found in EGFR-TKI-sensitive NSCLC cell lines than in relatively resistant NSCLC cell lines (HCC827>PC9>A549>H1975). CD147 could promote ¹⁸ F-FDG uptake by HCC827 and H1975 cells in vitro and in vivo through an EGFR-initiated Akt/mTOR-dependent signaling pathway. Programmed cell death-ligand 1 (PD-L1) expression was positively correlated with CD147 expression in human NSCLC cell lines. EGFR-TKI treatment sensitivity prediction in NSCLC using ¹⁸ F-FDG PET/CT imaging significantly correlated with CD147-mediated glucose metabolic regulation via the Akt/mTOR-dependent pathway. Moreover, PD-L1 expression in NSCLC cell lines could be regulated by CD147, suggesting a potential immunosuppression induced by the upregulation of tumor glucose metabolism.

Spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography and magnetic resonance diffusion imaging metrics in cervical cancer

Background

This study investigated the spatial relationship of 2-deoxy-2-[¹⁸F]-fluoro-D-glucose positron emission tomography ([¹⁸F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data.

We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [¹⁸F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUV_max), mean ADC (ADC_mean), and mean tumor-to-muscle ADC ratio (ADC_T/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis.

Results

Seventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUV_max and ADC_mean, and SUV_max and ADC_T/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS).

Conclusions

In this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0403-7) contains supplementary material, which is available to authorized users.