Heterogeneity in [18F]Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography of Non–Small Cell Lung Carcinoma and Its Relationship to Metabolic Parameters and Pathologic Staging

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose–positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.

[1]  Habib Zaidi,et al.  A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. , 2010, Medical physics.

[2]  Peter Smeets,et al.  Predictive and prognostic value of metabolic tumour volume and total lesion glycolysis in solid tumours , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[3]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[4]  Z. Fu,et al.  FDG PET-CT in non-small cell lung cancer: relationship between primary tumor FDG uptake and extensional or metastatic potential. , 2013, Asian Pacific journal of cancer prevention : APJCP.

[5]  E. Santoni-Rugiu,et al.  Intratumour variation of biomarker expression by immunohistochemistry in resectable non-small cell lung cancer. , 2013, European journal of cancer.

[6]  K. Miles,et al.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.

[7]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[8]  A. Fischman,et al.  FDG-PET and CT features of non-small cell lung cancer based on tumor type. , 2007, International journal of molecular medicine.

[9]  Jianyi Li,et al.  Effects of differential distribution of microvessel density, possibly regulated by miR-374a, on breast cancer prognosis. , 2013, Asian Pacific journal of cancer prevention : APJCP.

[10]  R. Jeraj,et al.  Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters , 2010, Acta oncologica.

[11]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[12]  Dimitris Visvikis,et al.  Impact of Tumor Size and Tracer Uptake Heterogeneity in 18F-FDG PET and CT Non–Small Cell Lung Cancer Tumor Delineation , 2011, The Journal of Nuclear Medicine.

[13]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[14]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[15]  Kyoungjune Pak,et al.  Prognostic value of volumetric parameters measured by F-18 FDG PET/CT in surgically resected non-small-cell lung cancer , 2012, Nuclear medicine communications.

[16]  M. Soussan,et al.  Prognostic implications of volume-based measurements on FDG PET/CT in stage III non-small-cell lung cancer after induction chemotherapy , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.

[18]  C. Compton,et al.  AJCC Cancer Staging Manual , 2002, Springer New York.

[19]  A. M. G. Vicente,et al.  18F-FDG PET/CT and respiratory synchronization: effect in the detection and classification of pulmonary lesions , 2009 .

[20]  J. R. G. Garzón,et al.  Tomografía por emisión de positrones de cuerpo completo (PET/TAC) con 18F-fluorodesoxiglucosa , 2009 .

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

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

[23]  Steven E Schild,et al.  Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. , 2008, Mayo Clinic proceedings.

[24]  A. M. G. Vicente,et al.  18F-FDG PET-CT respiratory gating in characterization of pulmonary lesions: approximation towards clinical indications , 2010 .

[25]  V. Treyer,et al.  Spatial Heterogeneity of Low-Grade Gliomas at the Capillary Level: A PET Study on Tumor Blood Flow and Amino Acid Uptake , 2007, Journal of Nuclear Medicine.

[26]  Habib Zaidi,et al.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[27]  You Han Bae,et al.  Tumor heterogeneity and its implication for drug delivery. , 2012, Journal of controlled release : official journal of the Controlled Release Society.

[28]  M. Hatt,et al.  Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[29]  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.

[30]  J. Bradley,et al.  Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[31]  B. Djulbegovic,et al.  Survival of patients with non-small cell lung cancer without treatment: a systematic review and meta-analysis , 2013, Systematic Reviews.

[32]  Vicky Goh,et al.  Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? , 2013, European journal of radiology.

[33]  R. Minchin,et al.  Role of intratumoural heterogeneity in cancer drug resistance: molecular and clinical perspectives , 2012, EMBO molecular medicine.

[34]  A. Yıldız,et al.  Measurements of tumor size using CT and PET compared to histopathological size in non-small cell lung cancer. , 2013, Diagnostic and interventional radiology.

[35]  Vicky Goh,et al.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[36]  S. Geest,et al.  Weight gain, overweight and obesity in solid organ transplantation—a study protocol for a systematic literature review , 2015, Systematic Reviews.

[37]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[38]  V. Grégoire,et al.  Is (18)F-FDG a surrogate tracer to measure tumor hypoxia? Comparison with the hypoxic tracer (14)C-EF3 in animal tumor models. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[39]  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.

[40]  Yong Yin,et al.  Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage , 2013, Nuclear medicine communications.

[41]  D. Mollura,et al.  Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images , 2013, PloS one.

[42]  T. Atasever,et al.  Relationship between primary lesion metabolic parameters and clinical stage in lung cancer. , 2013, Revista espanola de medicina nuclear e imagen molecular.

[43]  A. G. Vicente,et al.  18F-FDG PET-TAC y sincronización respiratoria: efecto en la detección y catalogación de lesiones pulmonares , 2009 .

[44]  G Brix,et al.  Texture analysis of differently reconstructed PET images. , 1996, Physics in medicine and biology.

[45]  K. Miles,et al.  Multiparametric PET/CT in oncology , 2012, Cancer imaging : the official publication of the International Cancer Imaging Society.

[46]  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.

[47]  Chronic Disease Division.  Cancer facts and figures , 2010 .

[48]  Balaji Ganeshan,et al.  Quantifying tumour heterogeneity with CT , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[49]  C. Van de Wiele,et al.  FDG uptake, a surrogate of tumour hypoxia? , 2008, European Journal of Nuclear Medicine and Molecular Imaging.