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Classification of Malignant and Benign Tumors

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Machine Learning in Radiation Oncology

Abstract

Machine learning has a long-standing history of application for computer-aided diagnosis (CADx) purposes and discriminating between different types of benign and malignant lesions. In this chapter, we explain the application of machine learning algorithms for development of classifiers of tumors using features extracted from diagnostic imaging. Examples from our work on mammography using conventional classification approaches and more advanced methods based on content-based image retrieval will be presented and discussed.

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References

  1. Horeweg N, Scholten ET, de Jong PA, van der Aalst CM, Weenink C, Lammers J-WJ, et al. Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers. Lancet Oncol. 2014;15:1342–50. doi:10.1016/S1470-2045(14)70387-0.

    Article  PubMed  Google Scholar 

  2. Agarwal V, Branstetter BF 4th, Johnson JT. Indications for PET/CT in the head and neck. Otolaryngol Clin North Am. 2008;41:23–49. doi:http://dx.doi.org/10.1016/j.otc.2007.10.005.

  3. Thompson J, Lawrentschuk N, Frydenberg M, Thompson L, Stricker P, USANZ. The role of magnetic resonance imaging in the diagnosis and management of prostate cancer. BJU Int. 2013;112 Suppl 2:6–20. doi:10.1111/bju.12381.

    Article  PubMed  Google Scholar 

  4. Leung D, Han X, Mikkelsen T, Nabors LB. Role of MRI in primary brain tumor evaluation. J Natl Compr Canc Netw. 2014;12:1561–8.

    PubMed  Google Scholar 

  5. Young RJ, Knopp EA. Brain MRI: tumor evaluation. J Magn Reson Imaging. 2006;24:709–24. doi:10.1002/jmri.20704.

    Article  PubMed  Google Scholar 

  6. Pilewskie M, King TA. Magnetic resonance imaging in patients with newly diagnosed breast cancer: a review of the literature. Cancer. 2014;120:2080–9. doi:10.1002/cncr.28700.

    Article  PubMed  Google Scholar 

  7. Wu Y, Giger M, Doi K, Vyborny C, Schmidt R, Metz C. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology. 1993;187(1):81–7.

    Article  CAS  PubMed  Google Scholar 

  8. Andreadis II, Spyrou GM, Nikita KS. A comparative study of image features for classification of breast microcalcifications. Meas Sci Tech. 2011;22(11):114005.

    Article  Google Scholar 

  9. Cheng HD, Cai X, Chen X, Hu L, Lou X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognit. 2003;36:2967–91.

    Article  Google Scholar 

  10. Jiang Y, Nishikawa RM, Wolverton EE, Metz CE, Giger ML, Schmidt RA, Vyborny CJ. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology. 1996;198:671–8.

    Article  CAS  PubMed  Google Scholar 

  11. Sakka E, Prentza A, Koutsouris D. Classification algorithms for microcalcifications in mammograms (review). Oncol Rep. 2006;15(4):1049–55.

    PubMed  Google Scholar 

  12. Wei L, Yang Y, Nishikawa RM, Wenick MN, Edwards A. Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans Med Imaging. 2005;24(10):1278–85.

    Article  PubMed  Google Scholar 

  13. Wei L, Yang Y, Nishikawa RM, Jiang Y. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging. 2005;24(3):371–80.

    Article  PubMed  Google Scholar 

  14. Bozek J, Mustra M, Delac K, Grgic M. A survey of image processing algorithms in digital mammography. Recent Adv Multimedia Signal Process Commun. 2009;231:631–57.

    Google Scholar 

  15. Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN. Approaches for automated detection and classification of masses in mammograms. Pattern Recognit. 2006;39(4):646–68.

    Article  Google Scholar 

  16. Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: a review. Med Phys. 2009;36:2052–68.

    Article  PubMed  Google Scholar 

  17. Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Metz CE. Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. Acad Radiol. 2000;7(12):1077–84.

    Article  CAS  PubMed  Google Scholar 

  18. Chan H, Sahiner B, Helvie MA, Petrick N, Roubidoux MA, Wilson TE, Adler DD, Paramagul C, Newman JS, Sanjay-Gopal S. Improvement of radiologists’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology. 1999;212:817–27.

    Article  CAS  PubMed  Google Scholar 

  19. Horsch K, Giger ML, Vyborny CJ, Lan L, Mendelson EB, Hendrick RE. Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology. 2006;240:357–68.

    Article  PubMed  Google Scholar 

  20. Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Schimidt RA, Doi K. Automated computerized classification of malignant and benign masses on digitized mammograms. Acad Radiol. 1998;5(3):155–68.

    Article  CAS  PubMed  Google Scholar 

  21. Jiang Y, Nishikawa RM, Schmidt RA, Toledano AY, Doi K. Potential of computer-aided diagnosis to reduce variability in radiologists’ interpretations of mammograms depicting microcalcifications. Radiology. 2001;220:787–94.

    Article  CAS  PubMed  Google Scholar 

  22. Müller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medical applications–clinical benefits and future directions. Int J Med Inform. 2004;73:1–23.

    Article  PubMed  Google Scholar 

  23. Bustos B, Keim D, Saupe D, Schreck T. Content-based 3D object retrieval. IEEE Comput Graph Appl. 2007;27:22–7.

    Article  PubMed  Google Scholar 

  24. Bhanu B, Peng J, Qing S. Learning feature relevance and similarity metrics in image database. In: IEEE workshop proceedings on content-based access of image and video libraries. Washington, DC; 1998. p. 14–8.

    Google Scholar 

  25. El-Naqa I, Yang Y, Galasanos NP, Nishikawa RM, Wernick MN. A similarity learning approach to content based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233–44. doi:10.1109/TMI.2004.834601.

    Article  PubMed  Google Scholar 

  26. Vapnik V. Statistical learning theory. New York: Wiley; 1998.

    Google Scholar 

  27. Jiang Y, Nishikawa RM, Giger ML, Doi K, Schmidt R, Vyborny C. Method of extracting signal area and signal thickness of microcalcifications from digital mammograms. Proc SPIE. 1992;1778:28–36.

    Google Scholar 

  28. Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK. Segmentation, feature extraction, and multiclass brain tumor classification. J Digit Imaging. 2013;26(6):1141–50.

    Article  PubMed Central  PubMed  Google Scholar 

  29. Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad S. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognit. 2004;37:1973–86.

    Article  Google Scholar 

  30. Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–21.

    Article  Google Scholar 

  31. Gonzalez RC, Woods RE. Digital image processing. Upper Saddle River: Prentice Hall; 2002.

    Google Scholar 

  32. Yu L, Liu H. Feature selection for high-dimensional data: a fast correlation-based filter solution. ICML. 2003;3:856–63.

    Google Scholar 

  33. Kohavi R, John GH. Wrappers for feature subset selection. Artif Int. 1997;97(1):273–324.

    Article  Google Scholar 

  34. Perkins S, Lacker K, Theiler J. Grafting: fast, incremental feature selection by gradient descent in function space. J Mach Learn Res. 2003;3:1333–56.

    Google Scholar 

  35. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.

    Google Scholar 

  36. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York: Springer; 2009.

    Google Scholar 

  37. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

    Google Scholar 

  38. Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge/New York: Cambridge University Press; 2000.

    Book  Google Scholar 

  39. Tipping ME. Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res. 2001;1:211–44.

    Google Scholar 

  40. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119–39.

    Article  Google Scholar 

  41. Dietterich TG. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn. 2000;40(2):139–57.

    Article  Google Scholar 

  42. Breiman L, Spector P. Submodel selection and evaluation in regression. The x-random case. Int Stat Rev. 1992;60:291–319.

    Article  Google Scholar 

  43. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI. 1995;14(2):1137–45.

    Google Scholar 

  44. Mertens BJ, de Noo ME, Tollenaar RAEM, Dcclder AM. Mass spectrometry proteomic diagnosis: enacting the double cross-validatory paradigm. J Comput Biol. 2006;13(9):1591–605.

    Article  CAS  PubMed  Google Scholar 

  45. Mushlin AI, Kouides RW, Shapiro DE. Estimating the accuracy of screening mammography: a meta-analysis. Am J Prev Med. 1998;14:143–53.

    Article  CAS  PubMed  Google Scholar 

  46. Urbain JL. Breast cancer screening, diagnostic accuracy and health care policies. CMAJ. 2005;172:210–1.

    Article  PubMed Central  PubMed  Google Scholar 

  47. Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002;225:165–75.

    Article  PubMed  Google Scholar 

  48. Elmore JG, Barton MB, Moceri VM, Polk S, Arena PJ, Fletcher SW. Ten-year risk of false positive screening mammograms and clinical breast examinations. N Engl J Med. 1998;338(16):1089–96.

    Article  CAS  PubMed  Google Scholar 

  49. Tan A, Freeman Jr DH, Goodwin JS, Freeman JL. Variation in false-positive rates of mammography reading among 1067 radiologists: a population-based assessment. Breast Cancer Res Treat. 2006;100:309–18.

    Article  PubMed  Google Scholar 

  50. Sickles EA, Miglioretti DL, Ballard-Barbash R, Geller BM, Leung JWT, Rosenberg RD, Smith-Bindman R, Yankaskas BC. Performance benchmarks for diagnostic mammography. Radiology. 2005;235:775–90.

    Article  PubMed  Google Scholar 

  51. Chan H, Wei D, Lam K, Lo S, Sahiner B, Helvie M, Adler D. Computerized detection and classification of microcalcifications on mammograms. SPIE. 1995;2434:612–20.

    Google Scholar 

  52. Chan H, Sahiner B, Lam KL, Petrick N, Helvie MA, Goodsitt MM, Adler DD. Computerized analysis of mammographic microcalcifications in morphological and texture feature space. Med Phys. 1998;25:2007–19.

    Article  CAS  PubMed  Google Scholar 

  53. Markopoulos C, Kouskos E, Koufopoulos K, Kyriakou V, Gogas J. Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. Eur J Radiol. 2001;39(1):60–5.

    Article  CAS  PubMed  Google Scholar 

  54. Jiang Y, Nishikawa RM, Schmidt RA, Metz CE, Giger ML, Doi K. Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol. 1999;6:22–33.

    Article  CAS  PubMed  Google Scholar 

  55. Nishikawa RM. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph. 2007;31:224–35.

    Article  PubMed  Google Scholar 

  56. Rangayyan RM, Fabio JA, Desautels JL. A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Franklin Inst. 2007;344:312–48.

    Article  Google Scholar 

  57. Sampat MP, Markey MK, Bovik AC. Computer-aided detection and diagnosis in mammography. Chap. 10.4. In: Handbook of image & video processing. 2nd ed. Amsterdam/Boston: Elsevier Academic Press; 2005.

    Google Scholar 

  58. Wang J, Yang Y. Spatial density modeling for discriminating between benign and malignant microcalcification lesions. In: IEEE international conference on image processing. San Francisco. 2013;133–6.

    Google Scholar 

  59. Muller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval system in medical applications-clinical benefits and future directions. Int J Med Info. 2004;73:1–23.

    Article  Google Scholar 

  60. Rahman M, Want T, Desai B. Medical image retrieval and registration: towards computer assisted diagnostic approach. In: Proceedings of IDEAS workshop on medical information systems: the Digital Hospital. Canada; 2004. p. 78–89.

    Google Scholar 

  61. Holt A, Bichindaritz I, Schmidt R, Perner P. Medical applications in case-based reasoning. Knowl Eng Rev. 2005;20:289–92.

    Article  Google Scholar 

  62. Bilska-Wolak A, Floyd E. Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADSâ„¢ lexicon. Med Phys. 2002;29:2090.

    Article  PubMed  Google Scholar 

  63. Floyd CE, Lo J, Tourassi GD. Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. Am J Roentgenol. 2000;175(5):1347–52.

    Article  Google Scholar 

  64. Zheng B, Lu A, Hardesty LA, Sumkin JH, Hakim CM, Ganott MA, Gur D. A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. Med Phys. 2006;33:111–7.

    Article  PubMed  Google Scholar 

  65. Jing H, Yang Y. Case-adaptive classification based on image retrieval for computer-aided diagnosis. In: IEEE international conference on image processing. Hong Kong; 2010. p. 4333–6.

    Google Scholar 

  66. Wei L, Yang Y, Nishikawa RM, Jiang Y. Learning of perceptual similarity from expert readers for mammogram retrieval. In: IEEE international symposium on biomedical imaging. Arlington; 2006. p. 1356–9.

    Google Scholar 

  67. Wei L, Yang Y, Nishikawa RM. Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recognit. 2009;42:1126–32.

    Article  PubMed Central  PubMed  Google Scholar 

  68. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP. The digital database for screening mammography. The fifth international workshop on digital mammography. Toronto; 2001. p. 212–8.

    Google Scholar 

  69. Mcloughlin KJ, Bones PJ, Karssemeijer N. Noise equalization for detection of microcalcification clusters in direct digital mammogram images. IEEE Trans Med Imaging. 2004;23(3):313–20.

    Article  PubMed  Google Scholar 

  70. Aisen A, Broderick L, Winer-Muram H, Brodley C, Kak A, Pavlopoulou C, Dy J, Shyu C, Marchiori A. Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. Radiology. 2003;228:265–70.

    Article  PubMed  Google Scholar 

  71. Muramatsu C, Nishimura K, Endo T, Oiwa M, Shiraiwa M, Doi K, Fujita H. Representation of lesion similarity by use of multidimensional scaling for breast masses on mammograms. J Digit Imaging. 2013;26(4):740–7.

    Article  PubMed Central  PubMed  Google Scholar 

  72. Tourassi GD, Harrawood B, Singh S, Lo JY, Floyd CE. Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. Med Phys. 2007;34:140–50.

    Article  PubMed  Google Scholar 

  73. Yang L, Jin R, Mummert L, Sukthankar R, Goode A, Zheng B, Hoi SCH, Satyanarayanan M. A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE Trans Pattern Aana Mach Intell. 2010;32(1):30–44.

    Article  Google Scholar 

  74. Wang J, Jing H, Wernick MN, Nishikawa RM, Yang Y. Analysis of perceived similarity between pairs of microcalcification clusters in mammograms. Med Phys. 2014;41(5):051904.

    Article  PubMed Central  PubMed  Google Scholar 

  75. Borg I, Groenen PJF. Modern multidimensional scaling: theory and application. New York: Springer; 2005.

    Google Scholar 

  76. Karahahiou AN, Boniatis IS, Skiadopoulos SG, Sakellaropoulos FN, Arikidis NS, Likaki EA, Panayiotakis GS, Costaridou LI. Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans Inf Technol Biomed. 2008;12:731–8.

    Article  Google Scholar 

  77. Hadjiiski L, Chan HP, Sahiner B, Helvie MA, Roubidoux MA, Blane C, Paramagul C, Petrick N, Bailey J, Klein K, Foster M, Patterson S, Alder D, Nees A, Shen J. Improvement in radiologists’ characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study. Radiology. 2004;233:255–65.

    Article  PubMed  Google Scholar 

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Acknowledgement

This work was supported in part by NIH grant EB009905.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn, Chicago, IL, 60616, USA

    Juan Wang & Yongyi Yang

  2. Department of Oncology, McGill University, Montreal, QC, Canada

    Issam El Naqa

  3. Department of Radiation Oncology, University of Michigan, Ann Arbor, USA

    Issam El Naqa

Authors
  1. Juan Wang
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  2. Issam El Naqa
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Correspondence to Yongyi Yang .

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Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

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Wang, J., El Naqa, I., Yang, Y. (2015). Classification of Malignant and Benign Tumors. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-18305-3_8

  • Publisher Name: Springer, Cham

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