Test–Retest Reproducibility Analysis of Lung CT Image Features

Yoganand Balagurunathan1, Virendra Kumar1, Yi Gu1, Jongphil Kim2, Hua Wang1, Ying Liu1, Dmitry B. Goldgof3, Lawrence Hall3, R Korn4, Binsheng Zhao5, Lawrence H. Schwartz5, Shibani Basu3, Steven Eschrich2, Robert A. Gatenby6, Robert J. Gillies6
1Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
2Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
3Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA
4Definiens AG, Bernhard-Wicki-Straße 5, 80636, Munchen, Germany
5Department of Radiology, Columbia University, New York, NY, USA
6Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

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Nguyen T, Rangayyan R: Shape analysis of breast masses in mammograms via the fractal dimension. Conf Proc IEEE Eng Med Biol Soc 3:3210–3213, 2005

Schuster DP: The opportunities and challenges of developing imaging biomarkers to study lung function and disease. Am J Respir Crit Care Med 176(3):224–230, 2007

Suzuki C, Jacobsson H, Hatschek T, et al: Radiologic measurements of tumor response to treatment: practical approaches and limitations. Radiographics 28(2):329–344, 2008

Tuma RS: Sometimes size doesn't matter: reevaluating RECIST and tumor response rate endpoints. J Natl Cancer Inst 98(18):1272–1274, 2006

Ganeshan B, Abaleke S, Young RC, 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. Cancer Imaging 10:137–143, 2010

Way TW, Sahiner B, Chan HP, et al: Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys 36(7):3086–3098, 2009

Samala R, Moreno W, You Y, et al: A novel approach to nodule feature optimization on thin section thoracic CT. Acad Radiol 16(4):418–427, 2009

Lee MC, Boroczky L, Sungur-Stasik K, et al: Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artif Intell Med 50(1):43–53, 2010

Zhu Y, Tan Y, Hua Y, et al: Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. J Digit Imaging 23(1):51–65, 2010

Al-Kadi O, Watson D: Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Trans Biomed Eng 55(7):1822–1830, 2008

Kido S, Kuriyama K, Higashiyama M, 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. J Comput Assist Tomogr 27(1):56–61, 2003

Segal E, Sirlin CB, Ooi C, et al: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25(6):675–680, 2007

Buckler AJ, Mozley PD, Schwartz L, et al: Volumetric CT in lung cancer: an example for the qualification of imaging as a biomarker. Acad Radiol 17(1):107–115, 2010

America RSoN: Quantitative imaging biomarker alliance for volumetric CT image analysis: roadmap for a staged validation plan, 2010

Zhao B, James LP, Moskowitz CS, et al: Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 252(1):263–272, 2009

RIDER. The Reference Image Database to Evaluate Therapy Response. Available at: https://wiki.cancerimagingarchive.net/display/Public/RIDER+Collections;jsessionid=C78203F71E49C7EA3A43E0D213CE5555 . Accessed 24 Jun 2014

Gu Y, Kumar V, Hall LO, et al: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn 46(3):692–702, 2013

NBIA. National Biomedical Imaging Archive. Available at: https://imaging.nci.nih.gov/ncia . Accessed 30 June 2014

Definiens. Definiens AG, Munchen, Germany. Available at: http://www.definiens.com/product-services/definiens-xd-product-suite.html . Accessed 30 June 2014

Athelogou M, Schmidt G, Schaepe A, et al: Cognition network technology—a novel multimodal image analysis technique for automatic identification and quantification of biological image contents. In: Shorte SL, Frischknecht F Eds. Book cognition network technology—a novel multimodal image analysis technique for automatic identification and quantification of biological image contents. Springer-Verlag, New York City, 2007, pp 407–422

Baatz M, Zimmermann J, Blackmore CG: Automated analysis and detailed quantification of biomedical images using Definiens Congnition Network Technology. Comb Chem High Throughput Screen 12(9):908–916, 2009

Bendtsen C, Kietzmann M, Korn R, Mozley P, Schmidt G, Binnig G: X-ray computed tomography: semiautomated volumetric analysis of late-stage lung tumors as a basis for response assessments. Int J Biomed Imaging, vol 2011, 2011

Basu S, Hall LO, Goldgof DB, et al: Developing a classifier model for lung tumors in ct-scan images. IEEE Intl Conf on Systems, Man and Cybernetics, (SMC 2011), Anchorage, Alaska, 2011

Lin LI-K: A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:13, 1989

RGD Steel JT: Principles and procedures of statistics. McGraw-Hill, New York, 1960

Colin C, Frank AW, Gramaji H, et al: An R-square measured of goodness of fit for some common nonlinear regression models. J Econ 77(2):1790–1792, 1997

Aoki T, Tomoda Y, Watanabe H, et al: Peripheral lung adenocarcinoma: correlation of thin-section CT findings with histologic prognostic factors and survival. Radiology 220(3):803–809, 2001

Takashima S MY, Hasegawa M, Saito A, Haniuda M, Kadoya M. High-resolution CT features: prognostic significance in peripheral lung adenocarcinoma with bronchioloalveolar carcinoma components. Respiration: Int Rev Thorac Dis 70(1), 2003

Subramanian J, Simon R: Gene exression-based signature in lung cancer: ready for clinical use? JNCI 102(7):464–474, 2010

Jain AK, Zongker D: Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal 19(2):153–158, 1997

Pudil P, Novovičová J, Kittler J: Floating search methods in feature selection. Pattern Recogn Lett 15:1119–1125, 1994

Saeys Y, Inza I: A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517, 2007

Landis JR, Koch G: The measurement of observer agreement for categorical data. Biometrics 33:159–174, 1977

Ganeshan B, Panayiotou E, Burnand K, et al: Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22(4):796–802, 2012

Yanagawa M, Tanaka Y, Kusumoto M, et al: Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors. Lung Cancer 70(3):286–294, 2010

John S: A direct approach to false discovery rate. J R Stat Soc B 64(3):479–498, 2002

Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300, 1995

Zhao B, Oxnard G, Moskowitz CS, et al: A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development. Clin Cancer Res 16(18):4647–4653, 2010