Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas

PurposeAmino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas.MethodsOne hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival.ResultsAll FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not hold in multivariate analysis.ConclusionsDetermination of uptake heterogeneity in pre-therapeutic FET-PET using textural features proved valuable for the (sub-)grading of high-grade glioma as well as prediction of tumor progression and patient survival, and showed improved performance compared to standard parameters such as TBR and tumor volume. Our results underscore the importance of intratumoral heterogeneity in the biology of high-grade glial cell tumors and may contribute to individual therapy planning in the future, although they must be confirmed in prospective studies before incorporation into clinical routine.

[1]  R. Sevick,et al.  How often are nonenhancing supratentorial gliomas malignant? A population study , 2002, Neurology.

[2]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[3]  Maximilian Niyazi,et al.  Prognostic Significance of Dynamic 18F-FET PET in Newly Diagnosed Astrocytic High-Grade Glioma , 2015, The Journal of Nuclear Medicine.

[4]  Irène Buvat,et al.  Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis , 2014, The Journal of Nuclear Medicine.

[5]  Jochen Herms,et al.  FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading , 2007, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  Chun-Ta Liao,et al.  Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma , 2013, The Journal of Nuclear Medicine.

[7]  S. Nekolla,et al.  Prediction of Glioma Recurrence Using Dynamic 18F-Fluoroethyltyrosine PET , 2014, American Journal of Neuroradiology.

[8]  John O. Prior,et al.  Performance of 18F-Fluoro-Ethyl-Tyrosine (18F-FET) PET for the Differential Diagnosis of Primary Brain Tumor: A Systematic Review and Metaanalysis , 2012, The Journal of Nuclear Medicine.

[9]  J. Tonn,et al.  MRI-suspected low-grade glioma: is there a need to perform dynamic FET PET? , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[10]  D. Louis WHO classification of tumours of the central nervous system , 2007 .

[11]  W. Wick,et al.  Neuroradiological Response Criteria for High-grade Gliomas , 2011, Clinical Neuroradiology.

[12]  F. Ducray,et al.  Chemotherapy in low-grade gliomas , 2012, Current opinion in oncology.

[13]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[14]  M. Hatt,et al.  Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET , 2012, The Journal of Nuclear Medicine.

[15]  Ronald Boellaard,et al.  Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[16]  W. Wick,et al.  Molecular predictors of outcome in low-grade glioma. , 2012, Current opinion in neurology.

[17]  William D. Dunn,et al.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. , 2013, Radiology.

[18]  Vicky Goh,et al.  Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? , 2013, The Journal of Nuclear Medicine.

[19]  K. Hamacher,et al.  O-(2-[18F]fluorethyl)-L-tyrosine PET in the clinical evaluation of primary brain tumours , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[20]  W. Koch,et al.  Analysis of 18F-FET PET for grading of recurrent gliomas: is evaluation of uptake kinetics superior to standard methods? , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[21]  S. Al-Sarraj,et al.  Receptor tyrosine kinase genes amplified in glioblastoma exhibit a mutual exclusivity in variable proportions reflective of individual tumor heterogeneity. , 2012, Cancer research.

[22]  R. Komotar,et al.  Predictors of Long-Term Survival in Patients With Glioblastoma Multiforme: Advancements From the Last Quarter Century , 2013, Cancer investigation.

[23]  Carsten Denkert,et al.  Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization , 2012, PloS one.

[24]  S. Lange,et al.  Adjusting for multiple testing--when and how? , 2001, Journal of clinical epidemiology.

[25]  Florent Tixier,et al.  Visual Versus Quantitative Assessment of Intratumor 18F-FDG PET Uptake Heterogeneity: Prognostic Value in Non–Small Cell Lung Cancer , 2014, The Journal of Nuclear Medicine.

[26]  Martin J. van den Bent,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[27]  V. Goh,et al.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. , 2013, Radiology.

[28]  F. Brooks,et al.  The Effect of Small Tumor Volumes on Studies of Intratumoral Heterogeneity of Tracer Uptake , 2014, The Journal of Nuclear Medicine.

[29]  T. Cloughesy The impact of recent data on the optimization of standards of care in newly diagnosed glioblastoma. , 2011, Seminars in oncology.

[30]  M. Hatt,et al.  Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.