Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors

PURPOSE To assess the uncertainty of quantitative imaging features extracted from contrast-enhanced computed tomography (CT) scans of lung cancer patients in terms of the dependency on the time after contrast injection and the feature reproducibility between scans. METHODS Eight patients underwent contrast-enhanced CT scans of lung tumors on two sessions 2-7 days apart. Each session included 6 CT scans of the same anatomy taken every 15s, starting 50s after contrast injection. Image features based on intensity histogram, co-occurrence matrix, neighborhood gray-tone difference matrix, run-length matrix, and geometric shape were extracted from the tumor for each scan. Spearman's correlation was used to examine the dependency of features on the time after contrast injection, with values over 0.50 considered time-dependent. Concordance correlation coefficients were calculated to examine the reproducibility of each feature between times of scans after contrast injection and between scanning sessions, with values greater than 0.90 considered reproducible. RESULTS The features were found to have little dependency on the time between the contrast injection and the CT scan. Most features were reproducible between times of scans after contrast injection and between scanning sessions. Some features were more reproducible when they were extracted from a CT scan performed at a longer time after contrast injection. CONCLUSION The quantitative imaging features tested here are mostly reproducible and show little dependency on the time after contrast injection.

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

[2]  M. Kendall.  Rank Correlation Methods , 1949 .

[3]  Robert B. McBride,et al.  A proposal for strength-of-agreement criteria for Lins Concordance Correlation Coefficient , 2005 .

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

[5]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[6]  P. Lambin,et al.  Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability , 2013, Acta oncologica.

[7]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

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

[9]  M. Martel,et al.  High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images. , 2013, Medical physics.

[10]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[11]  O. Mawlawi,et al.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. , 2014, International journal of radiation oncology, biology, physics.

[12]  J. M. Bevan,et al.  Rank Correlation Methods , 1949 .

[13]  Zhengyang Sun,et al.  Peripheral lung cancer: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and cyclin D1 expression. , 2007, Clinical imaging.

[14]  COMPUTATION OF MINKOWSKI MEASURES ON 2 D AND 3 D BINARY IMAGES D , 2007 .

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

[16]  Jinzhong Yang,et al.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. , 2015, Medical physics.

[17]  Robert J. Gillies,et al.  Developing a classifier model for lung tumors in CT-scan images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[18]  I. Jolliffe.  Principal Component Analysis , 2002 .

[19]  Satrajit Basu.  Developing Predictive Models for Lung Tumor Analysis , 2012 .

[20]  Ning Wu,et al.  Tumor response in patients with advanced non-small cell lung cancer: perfusion CT evaluation of chemotherapy and radiation therapy. , 2009, AJR. American journal of roentgenology.

[21]  Wei Wei,et al.  Reproducibility of perfusion parameters obtained from perfusion CT in lung tumors. , 2011, AJR. American journal of roentgenology.

[22]  P. Lambin,et al.  Predicting outcomes in radiation oncology—multifactorial decision support systems , 2013, Nature Reviews Clinical Oncology.

[23]  A. Chandler,et al.  Effect of duration of scan acquisition on CT perfusion parameter values in primary and metastatic tumors in the lung. , 2013, European journal of radiology.

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

[25]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  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.

[27]  Simon Wan,et al.  Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[28]  Jinzhong Yang,et al.  Preliminary investigation into sources of uncertainty in quantitative imaging features , 2015, Comput. Medical Imaging Graph..

[29]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[30]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

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

[32]  G. Mahon.  A Proposal for Strength-of-Agreement Criteria for Lin’s Concordance Correlation Coefficient , 2005 .

[33]  Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. , 2013, Medical physics.

[34]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[35]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.