Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis

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

[2]  Y. Ohno,et al.  Diffusion-weighted MRI versus 18F-FDG PET/CT: performance as predictors of tumor treatment response and patient survival in patients with non-small cell lung cancer receiving chemoradiotherapy. , 2012, AJR. American journal of roentgenology.

[3]  Bal Sanghera,et al.  Reproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer. , 2012, Radiology.

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

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

[6]  L. Xing,et al.  Prognostic significance of SUV on PET/CT in patients with localised oesophagogastric junction cancer receiving neoadjuvant chemotherapy/chemoradiation:a systematic review and meta-analysis. , 2012, The British journal of radiology.

[7]  K. Miles,et al.  Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.

[8]  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.

[9]  J. Decertaines Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma ? , 2012 .

[10]  O. Chinot,et al.  Independent prognostic value of pre-treatment 18-FDG-PET in high-grade gliomas , 2012, Journal of Neuro-Oncology.

[11]  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.

[12]  V. Goh,et al.  Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.

[13]  A. Hogg,et al.  Association between pulmonary uptake of fluorodeoxyglucose detected by positron emission tomography scanning after radiation therapy for non-small-cell lung cancer and radiation pneumonitis. , 2011, International journal of radiation oncology, biology, physics.

[14]  Brandon Whitcher,et al.  DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6 , 2011, British Journal of Cancer.

[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 J Niessen,et al.  Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response? , 2011, Physics in medicine and biology.

[17]  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.

[18]  A. Villers,et al.  Prostate cancer characterization on MR images using fractal features. , 2010, Medical physics.

[19]  J. Yue,et al.  Prognostic value of serial [18F]fluorodeoxyglucose PET-CT uptake in stage III patients with non-small cell lung cancer treated by concurrent chemoradiotherapy. , 2011, European journal of radiology.

[20]  Jinming Yu,et al.  18F-FDG PET or PET-CT to evaluate prognosis for head and neck cancer: a meta-analysis , 2011, Journal of Cancer Research and Clinical Oncology.

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

[22]  Omar Sultan Al-Kadi,et al.  Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images , 2010, Comput. Medical Imaging Graph..

[23]  Siegfried Trattnig,et al.  Texture‐based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas , 2010, Journal of magnetic resonance imaging : JMRI.

[24]  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.

[25]  P. Grigsby,et al.  Anal cancer maximum F-18 fluorodeoxyglucose uptake on positron emission tomography is correlated with prognosis. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  H. Eskola,et al.  Characterization of breast cancer types by texture analysis of magnetic resonance images. , 2010, Academic radiology.

[27]  B. Manaster Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome , 2010 .

[28]  Maximilien Vermandel,et al.  Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-Hodgkin lymphoma , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[29]  I. Poon,et al.  Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. , 2009, International journal of radiation oncology, biology, physics.

[30]  V. Rusch,et al.  Predictive Value of Initial PET-SUVmax in Patients with Locally Advanced Esophageal and Gastroesophageal Junction Adenocarcinoma , 2009, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[31]  H. Eskola,et al.  Non-Hodgkin lymphoma response evaluation with MRI texture classification , 2009, Journal of experimental & clinical cancer research : CR.

[32]  Issam El-Naqa,et al.  Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..

[33]  R. Wahl,et al.  From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors , 2009, Journal of Nuclear Medicine.

[34]  Huan Yu,et al.  Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning , 2009, IEEE Transactions on Medical Imaging.

[35]  Steve Halligan,et al.  Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis , 2009, European Radiology.

[36]  S. Ben-Haim,et al.  18F-FDG PET and PET/CT in the Evaluation of Cancer Treatment Response* , 2008, Journal of Nuclear Medicine.

[37]  Kathryn Trinkaus,et al.  PET-based estradiol challenge as a predictive biomarker of response to endocrine therapy in women with estrogen-receptor-positive breast cancer , 2009, Breast Cancer Research and Treatment.

[38]  Richard Frayne,et al.  A comparison of texture quantification techniques based on the Fourier and S transforms. , 2008, Medical physics.

[39]  Neeraj Sharma,et al.  Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network , 2008, Journal of medical physics.

[40]  Omar S. Al-Kadi,et al.  Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images , 2008, IEEE Transactions on Biomedical Engineering.

[41]  Barry A Siegel,et al.  Impact of positron emission tomography/computed tomography and positron emission tomography (PET) alone on expected management of patients with cancer: initial results from the National Oncologic PET Registry. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[42]  W. Oyen,et al.  Chemotherapy Response Evaluation with 18F-FDG PET in Patients with Non-Small Cell Lung Cancer , 2007, Journal of Nuclear Medicine.

[43]  M. Giger,et al.  Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.

[44]  C. Chatwin,et al.  Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. , 2007, Clinical radiology.

[45]  Johan Wennerberg,et al.  2-Deoxy-2-[18F] fluoro-D-glucose uptake and correlation to intratumoral heterogeneity. , 2007, Anticancer research.

[46]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[47]  Lucia Dettori,et al.  A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography , 2007, Comput. Biol. Medicine.

[48]  Tahsin Kurc,et al.  Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data , 2007, Journal of magnetic resonance imaging : JMRI.

[49]  Y. Bang,et al.  High Fluorodeoxyglucose Uptake on Positron Emission Tomography in Patients with Advanced Non–Small Cell Lung Cancer on Platinum-Based Combination Chemotherapy , 2006, Clinical Cancer Research.

[50]  B. Cheson,et al.  Positron-emission tomography and assessment of cancer therapy. , 2006, The New England journal of medicine.

[51]  Joos V Lebesque,et al.  Standardised FDG uptake: a prognostic factor for inoperable non-small cell lung cancer. , 2005, European journal of cancer.

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

[53]  Shoji Kido,et al.  Fractal Analysis of Internal and Peripheral Textures of Small Peripheral Bronchogenic Carcinomas in Thin-section Computed Tomography: Comparison of Bronchioloalveolar Cell Carcinomas With Nonbronchioloalveolar Cell Carcinomas , 2003, Journal of computer assisted tomography.

[54]  N. Sadato,et al.  FDG-PET for prediction of tumour aggressiveness and response to intra-arterial chemotherapy and radiotherapy in head and neck cancer , 2002, European Journal of Nuclear Medicine and Molecular Imaging.

[55]  S. Das,et al.  Dynamic contrast-enhanced MRI and fractal characteristics of percolation clusters in two-dimensional tumor blood perfusion. , 1999, Journal of Biomechanical Engineering.

[56]  P. Marsden,et al.  A PET study of 18FDG uptake in soft tissue masses , 1999, European Journal of Nuclear Medicine.

[57]  J L Grashuis,et al.  Texture analysis in radiographs: the influence of modulation transfer function and noise on the discriminative ability of texture features. , 1998, Medical physics.

[58]  O. Sabri,et al.  FDG PET for detection and therapy control of metastatic germ cell tumor. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[59]  Zhiling Wang,et al.  Comparison of several approaches for the segmentation of texture images , 1995, Electronic Imaging.

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

[61]  E. Lachmann,et al.  The Roentgen Diagnosis of Osteoporosis and Its Limitations1 , 1936 .