Predicting outcomes of nonsmall cell lung cancer using CT image features

IEEE Access

Volumn 2, Issue , 2014, Pages 1418-1426

  Hawkins, Samuel H ^a   Korecki, John N ^a   Balagurunathan, Yoganand ^b   Gu, Yuhua ^b   Kumar, Virendra ^b   Basu, Satrajit ^a   Hall, Lawrence O ^a   Goldgof, Dmitry B ^a,b   Gatenby, Robert A ^b   Gillies, Robert J ^b  

^a University of South Florida   (United States)

^b H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

Computed tomography; CT 3D texture features; support vector machine

Indexed keywords

BIOLOGICAL ORGANS; CLASSIFICATION (OF INFORMATION); DECISION TREES; DISEASES; FEATURE EXTRACTION; FORECASTING; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; TOMOGRAPHY;

3D TEXTURES; COMPUTED TOMOGRAPHY SCAN; DATA SET; LEAVE-ONE-OUT CROSS VALIDATIONS; LUNG CANCER; NON SMALL CELL LUNG CANCER; PREDICTING OUTCOMES; SURVIVAL TIME;

COMPUTERIZED TOMOGRAPHY;

EID: 84923319145     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2014.2373335     Document Type: Article

References (27)

1. V. Kumar et al., Radiomics: The process and the challenges," Magn. Reson. Imag., vol. 30, no. 9, pp. 1234-1248, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0730725X12002202
2. B. Ganeshan, S. Abaleke, R. C. D. Young, C. R. Chatwin, and K. A. Miles, Texture analysis of non-small cell lung cancer on unenhanced computed tomography: Initial evidence for a relationship with tumour glucose metabolism and stage," Cancer Imag., vol. 10, no. 1, pp. 137-143, Jul. 2010.
3. R. Samala, W. Moreno, Y. You, and W. Qian, A novel approach to nodule feature optimization on thin section thoracic CT," Acad. Radiol., vol. 16, no. 4, pp. 418-427, 2009.
4. T.W.Way et al., Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classi-cation using 3D active contours," Med. Phys., vol. 33, no. 7, pp. 2323-2337, 2006. [Online]. Available: http://dx.doi.org/10.1118/1.2207129
5. M. C. Lee et al., Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classi-er ensemble construction," Artif. Intell. Med., vol. 50, no. 1, pp. 43-53, 2010.
6. Y. Zhu, Y. Tan, Y. Hua, M. Wang, G. Zhang, and J. Zhang, Feature selection and performance evaluation of support vector machine (SVM)-based classi-er for differentiating benign and malignant pulmonary nodules by computed tomography," J. Digit. Imag., vol. 23, no. 1, pp. 51-65, 2010.
7. O. S. Al-Kadi and D. Watson, Texture analysis of aggressive and nonaggressive lung tumor CE CT images," IEEE Trans. Biomed. Eng., vol. 55, no. 7, pp. 1822-1830, Jul. 2008.
8. S. Kido, K. Kuriyama, M. Higashiyama, T. Kasugai, and C. Kuroda, 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. Tomograph., vol. 27, no. 1, pp. 56-61, 2003.
9. E. Segal et al., Decoding global gene expression programs in liver cancer by noninvasive imaging," Nature Biotechnol., vol. 25, pp. 675-680, May 2007. [Online]. Available: http://www.stanford.edu/group/OTL/lagan/06343/Kuoetal2007Article.pdf
10. De-niens Developer XD 2.0.4 User Guide, De-niens AG, München, Germany, 2009.
11. Y. Gu et al., Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach," Pattern Recognit., vol. 46, no. 3, pp. 692-702, Mar. 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0031320312004384
12. S. Basu et al., Developing a classi-er model for lung tumors in CT-scan images," in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 2011, pp. 1306-1312.
13. Y. Balagurunathan et al., Reproducibility and prognosis of quantitative features extracted from CT images," Translational Oncol., vol. 7, no. 1, pp. 72-87, Feb . 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1936523314800107
14. J. R. Quinlan, Decision trees and decision-making," IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 339-346, Mar./Apr. 1990.
15. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, The WEKA data mining software: An update," ACM SIGKDD Explorations Newslett., vol. 11, no. 1, pp. 10-18, Nov. 2009. [Online]. Available: http://doi.acm.org/10.1145/1656274.1656278
16. J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann, 1993.
17. W. W. Cohen, Fast effective rule induction," in Proc. 12th Int. Conf. Mach. Learn., 1995, pp. 115-123.
18. D. D. Lewis, Naive (Bayes) at forty: The independence assumption in information retrieval," in Machine Learning: ECML (Lecture Notes in Computer Science), vol. 1398, C. Nédellec and C. Rouveirol, Eds. Berlin, Germany: Springer-Verlag, 1998, pp. 4-15. nline]. Available: http://dx.doi.org/10.1007/BFb0026666
19. G. H. John and P. Langley, Estimating continuous distributions in Bayesian classi-ers," in Proc. 11th Conf. Uncertainty Artif. Intell., 1995, pp. 338-345.
20. C. Cortes and V. Vapnik, Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
21. K. A. Kramer, L. O. Hall, D. B. Goldgof, A. Remsen, and T. Luo, Fast support vector machines for continuous data," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 4, pp. 989-1001, 2009.
22. K. Bache and M. Lichman. (2013). UCI Machine Learning Repository. [Online]. Available: http://archive.ics.uci.edu/ml
23. J. Dehmeshki, J. Chen, M. V. Casique, and M. Karakoy, Classi-cation of lung data by sampling and support vector machine," in Proc. 26th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (IEMBS), Sep. 2004, pp. 3194-3197.
24. C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, 2011. [Online]. Available: http://www.csie.ntu. edu.tw/-cjlin/libsvm
25. K. Kira and L. A. Rendell, A practical approach to feature selection," in Proc. 9th Int. Workshop Mach. Learn., 1992, pp. 249-256.
26. I. Kononenko, Estimating attributes: Analysis and extensions of RELIEF," in Proc. Eur. Conf. Mach. Learn., 1994, pp. 171-182.
27. M. Robnik-Sikonja and I. Kononenko, An adaptation of Relief for attribute estimation in regression," in Proc. 14th Int. Conf. Mach. Learn., 1997, pp. 296-304.