Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Machine Learning Techniques in Cancer Prognostic Modeling and Performance Assessment

  • Chapter
  • First Online:
Frontiers of Biostatistical Methods and Applications in Clinical Oncology

Abstract

Prognostic models for disease occurrence, tumor progression and survival are abundant for most types of cancers. Physicians and cancer patients are utilizing these models to make informed treatment decisions and corresponding arrangements. However, not all cancer prognostic models are built and validated rigorously. Some are more useful and reliable than others. In this chapter, we briefly introduce some popular machine learning methods for constructing cancer prognostic models, and discuss pros and cons of each. We also introduce the commonly used discrimination and calibration metrics for assessing predictive performance and validating the prognostic models. In the end, we outline several challenges of using prognostic models in the real world for clinical decision-making support, and propose related suggestions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmad A. Pathways to breast cancer recurrence. ISRN Oncol. 2013;2013:290568. doi:10.1155/2013/290568.

    Google Scholar 

  2. Ahmad LG, Eshlaghy AT, Poorebrahimi A, et al. Using three machine learning techniques for predicting breast cancer recurrence. J Heal Med Inform. 2013;4:1000124. doi:10.4172/2157-7420.1000124.

    Google Scholar 

  3. Altman DG, Royston P. What do we mean by validating a prognistic model? Stat Med. 2000;19:453–73.

    Article  Google Scholar 

  4. Ankerst DP, Hoefler J, Bock S, et al. Prostate cancer prevention trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer. Urology. 2014;83:1362–7. doi:10.1016/j.urology.2014.02.035.

    Article  Google Scholar 

  5. Bellaachia A, Guven E. Predicting breast cancer survivability using data mining techniques. SIAM Int Conf Data Min. 2006;6:1–4. doi:10.1109/ICSTE.2010.5608818.

    Google Scholar 

  6. Bharathi A, Natarajan AM. Cancer classification using support vector machines and relevance vector machine based on analysis of variance features. J Comput Sci. 2011;7:1393–9.

    Article  Google Scholar 

  7. De Bin R, Sauerbrei W, Boulesteix A-L. Investigating the prediction ability of survival models based on both clinical and omics data: Two case studies. Stat Med. 2014;33:5310–29. doi:10.1002/sim.6246.

    Article  MathSciNet  Google Scholar 

  8. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual ACM workshop on computational learning theory. New York: ACM Press; 1992. p. 144–152.

    Google Scholar 

  9. Bottaci L, Drew PJ, Hartley JE, et al. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet. 1997;350:469–72. doi:10.1016/S0140-6736(96)11196-X.

    Article  Google Scholar 

  10. Bou-Hamd I, Larocque D, Ben-Ameur H. A review of survival trees. Stat Surv. 2011;5:44–71. doi:10.1214/09-SS047.

    Article  MathSciNet  MATH  Google Scholar 

  11. Boulesteix A, Sauerbrei W. Added predictive value of high-throughput molecular data to clinical data and its validation. Brief Bioinform. 2011;12:215–29. doi:10.1093/bib/bbq085.

    Article  Google Scholar 

  12. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2:121–67.

    Article  Google Scholar 

  13. Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–62.

    Article  Google Scholar 

  14. Chow E, Abdolell M, Panzarella T, et al. Predictive model for survival in patients with advanced cancer. J Clin Oncol. 2008;26:5863–9. doi:10.1200/JCO.2008.17.1363.

    Article  Google Scholar 

  15. Chow E, James JL, Hartsell W, et al. Validation of a predictive model for survival in patients with advanced cancer: Secondary analysis of RTOG 9714. World J Oncol. 2011;2:181–90. doi:10.4021/wjon325w.

    Google Scholar 

  16. Clark GM. Prognostic factors versus predictive factors: Examples from a clinical trial of erlotinib. Mol Oncol. 2008;1:406–12. doi:10.1016/j.molonc.2007.12.001.

    Article  Google Scholar 

  17. Craven MW, Shavlik JW. Extracting tree-structured representations of trained networks. In: Advances in neural information processing systems. Denver: MIT Press; 1996. p. 24–30.

    Google Scholar 

  18. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: A comparison of three data mining methods. Artif Intell Med. 2005;34:113–27. doi:10.1016/j.artmed.2004.07.002.

    Article  Google Scholar 

  19. Dettling M, Bühlmann P. Boosting for tumor classification with gene expression data. Bioinformatics. 2003;19:1061–9. doi:10.1093/bioinformatics/btf867.

    Article  Google Scholar 

  20. Faraggi D, LeBlanc M, Crowley J. Understanding neural networks using regression trees: an application to multiple myeloma survival data. Stat Med. 2001;20:2965–76. doi:10.1002/sim.912.

    Article  Google Scholar 

  21. Freund Y, Schapire RE. A desicion-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55:119–39. doi:10.1006/jcss.1997.1504.

    Article  MATH  Google Scholar 

  22. Friedman JH, Meulman JJ. Multiple additive regression trees with application in epidemiology. Stat Med. 2003;22:1365–81. doi:10.1002/sim.1501.

    Article  Google Scholar 

  23. Furey TS, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16:906–14.

    Article  Google Scholar 

  24. Ganesan N, Vankatesh K, Rama MA, Palani AM. Application of neural networks in diagnosing cancer disease using demographic data. Int J Comput Appl. 2010;1:76–85. doi:10.5120/476-783.

    Google Scholar 

  25. Garson DG. Interpreting neural-network connection weights. Artif Intell Expert. 1991;6:46–51.

    Article  Google Scholar 

  26. Ge G, Wong GW. Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles. BMC Bioinform. 2008;9:275. doi:10.1186/1471-2105-9-275.

    Article  Google Scholar 

  27. Glare P. Clinical predictors of survival in advanced cancer. J Support Oncol. 2005;3:331–9.

    Google Scholar 

  28. Goh ATC. Back-propagation neural networks for modeling complex systems. Artif Intell Eng. 1995;9:143–51. doi:10.1016/0954-1810(94)00011-S.

    Article  Google Scholar 

  29. Goldberg Y, Kosorok MR. Support vector regression for right censored data. 2012. arXiv 1202.5130v2.

    Google Scholar 

  30. Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18:2529–45. doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529:AID-SIM274>3.0.CO;2-5.

    Article  Google Scholar 

  31. Gupta S, Tran T, Luo W, et al. Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry. BMJ Open. 2014;4:e004007. doi:10.1136/bmjopen-2013-004007.

    Article  Google Scholar 

  32. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422.

    Article  MATH  Google Scholar 

  33. Halabi S, Lin C-Y, Kelly WK, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol. 2014;32:671–7. doi:10.1200/JCO.2013.52.3696.

    Article  Google Scholar 

  34. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.

    Article  Google Scholar 

  35. Henderson R, Jones M, Stare J. Accuracy of point predictions in survival analysis. Stat Med. 2001;20:3083–96. doi:10.1002/sim.913.

    Article  Google Scholar 

  36. Henderson R, Keiding N. Individual survival time prediction using statistical models. J Med Ethics. 2005;31:703–6. doi:10.1136/jme.2005.012427.

    Article  Google Scholar 

  37. Hofner B, Boccuto L, Göker M. Controlling false discoveries in high-dimensional situations: boosting with stability selection. BMC Bioinform. 2015;16:144. doi:10.1186/s12859-015-0575-3.

    Article  Google Scholar 

  38. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. New York: Wiley Interscience; 2013.

    Book  MATH  Google Scholar 

  39. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2:841–60. doi:10.1214/08-AOAS169.

    Article  MathSciNet  MATH  Google Scholar 

  40. Jonsdottir T, Hvannberg ET, Sigurdsson H, Sigurdsson S. The feasibility of constructing a predictive outcome model for breast cancer using the tools of data mining. Expert Syst Appl. 2008;34:108–18. doi:10.1016/j.eswa.2006.08.029.

    Article  Google Scholar 

  41. Kass GV. An exploratory technique for investigating large quantities of categorical data. Appl Stat. 1980;29:119–27. doi:10.2307/2986296.

    Article  Google Scholar 

  42. Katz MHG, Hu C-Y, Fleming JB, et al. A clinical calculator of conditional survival estimates for resected and unresected pancreatic cancer survivors. Arch Surg. 2012;147:513–9. doi:10.1001/archsurg.2011.2281.

    Article  Google Scholar 

  43. Khan FM, Zubek VB. Support vector regression for censored data (SVRc): a novel tool for survival analysis. In: Eighth IEEE international conference on data mining. New York: IEEE; 2008. p. 863–868.

    Google Scholar 

  44. Kharya S. Using data mining techniques for diagnosis and prognosis of cancer disease. Int J Comput Sci Inf Technol. 2012;2:55–66. doi:10.5121/ijcseit.2012.2206.

    Google Scholar 

  45. Laber EB, Zhao YQ. Tree-based methods for individualized treatment regimes. Biometrika. 2015;102:501–14. doi:10.1093/biomet/asv028.

    Article  MathSciNet  MATH  Google Scholar 

  46. Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics—application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform. 2009;10:315–29. doi:10.1093/bib/bbp012.

    Article  Google Scholar 

  47. LeBlanc M, Crowley J. Relative risk tees for censored survival data. Biometrics. 1992;48:411–25.

    Article  Google Scholar 

  48. LeBlanc M, Kooperberg C. Boosting predictions of treatment success. Proc Natl Acad Sci USA. 2010;107:13559–60. doi:10.1073/pnas.1008052107.

    Article  Google Scholar 

  49. Lisboa PJ, Taktak AFG. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Netw. 2006;19:408–15. doi:10.1016/j.neunet.2005.10.007.

    Article  MATH  Google Scholar 

  50. Liu HX, Zhang RS, Luan F, et al. Diagnosing breast cancer based on support vector machines. J Chem Inf Comput Sci. 2003;43:900–7.

    Article  Google Scholar 

  51. Loh W-Y. Classification and regression trees. Wiley Interdiscip Rev Data Min Knowl Discov. 2011;1:14–23. doi:10.1002/widm.8.

    Article  Google Scholar 

  52. Louie KS, Seigneurin A, Cathcart P, Sasieni P. Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis. Ann Oncol. 2015;26:848–64. doi:10.1093/annonc/mdu525.

    Article  Google Scholar 

  53. Lowrance WT, Elkin EB, Jacks LM, et al. Comparative effectiveness of surgical treatments for prostate cancer: a population-based analysis of postoperative outcomes. J Urol. 2010;183:1366–72. doi:10.1016/j.juro.2009.12.021.Comparative.

    Article  Google Scholar 

  54. Lundin M, Lundin J, Burke HB, et al. Artificial neural networks applied to survival prediction in breast cancer. Oncology. 1999;57:281–6.

    Article  Google Scholar 

  55. Mayr A, Hofner B, Schmid M. Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection. BMC Bioinform. 2016;17:288. doi:10.1186/s12859-016-1149-8.

    Article  Google Scholar 

  56. Meads C, Ahmed I, Riley RD. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat. 2012;132:365–77. doi:10.1007/s10549-011-1818-2.

    Article  Google Scholar 

  57. Menéndez LÁ, de Cos Juez FJ, Lasheras SF, Riesgo JAÁ. Artificial neural networks applied to cancer detection in a breast screening programme. Math Comput Model. 2010;52:983–91. doi:10.1016/j.mcm.2010.03.019.

    Article  MathSciNet  MATH  Google Scholar 

  58. Morgan JN, Sonquist JA. Problems in the analysis of survey data, and a proposal. J Am Stat Assoc. 1963;58:415–34. doi:10.1080/01621459.1963.10500855.

    Article  MATH  Google Scholar 

  59. Oberije C, De Ruysscher D, Houben R, et al. A validated prediction model for overall survival from stage III non-small cell lung cancer: toward survival prediction for individual patients. Int J Radiat Oncol Biol Phys. 2015;92:935–44. doi:10.1016/j.ijrobp.2015.02.048.

    Article  Google Scholar 

  60. Parks CM. Prognoses should be based on proved indicators not intuition. BMJ. 2000;320:473. doi:10.1136/bmj.320.7233.469.

    Article  Google Scholar 

  61. Penciana MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23:2109–23. doi:10.1002/sim.1802.

    Article  Google Scholar 

  62. Pölsterl S, Conjeti S, Navab N, Katouzian A. Survival analysis for high-dimensional, heterogeneous medical data: exploring feature extraction as an alternative to feature selection. Artif Intell Med. 2016;72:1–11. doi:10.1016/j.artmed.2016.07.004.

    Article  Google Scholar 

  63. Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med. 2004;23:723–48. doi:10.1002/sim.1621.

    Article  Google Scholar 

  64. Saritas I. Prediction of breast cancer using artificial neural networks. J Med Syst. 2012;36:2901–7. doi:10.1007/s10916-011-9768-0.

    Article  Google Scholar 

  65. Sauerbrei W, Hübner K, Schmoor C, Schumacher M. Validation of existing and development of new prognostic classification schemes in node negative breast cancer. Breast Cancer Res Treat. 1997;42:149–63.

    Article  Google Scholar 

  66. Schapire RE, Freund Y. Boosting—foundations and algorithms. Cambridge: MIT Press; 2012.

    MATH  Google Scholar 

  67. Schoop R, Graf E, Schumacher M. Quantifying the predictive performance of prognostic models for censored survival data with time-dependent covariates. Biometrics. 2008;64:603–10. doi:10.1111/j.l541-0420.2007.00889.x.

    Article  MathSciNet  MATH  Google Scholar 

  68. Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med. 2000;19:541–61. doi:10.1002/(SICI)1097-0258(20000229)19:4<541:AID-SIM355>3.0.CO;2-V.

    Article  Google Scholar 

  69. Scutari M, Denis J-B. Bayesian networks: with examples in R. Boca Raton: CRC Press; 2014.

    MATH  Google Scholar 

  70. Sesen MB, Nicholson AE, Banares-Alcantara R, et al. Bayesian networks for clinical decision support in lung cancer care. PLoS ONE. 2013;8:e82349. doi:10.1371/journal.pone.0082349.

    Article  Google Scholar 

  71. Shivaswamy PK, Chu W, Jansche M. A support vector approach to censored targets. In: Seventh IEEE international conference on data mining. New York: IEEE; 2007. p. 655–660.

    Google Scholar 

  72. Steyerberg EW, Harrell FE, Borsboom GJJM, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54:774–81. doi:10.1016/S0895-4356(01)00341-9.

    Article  Google Scholar 

  73. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology. 2010;21:128–38. doi:10.1097/EDE.0b013e3181c30fb2.Assessing.

    Article  Google Scholar 

  74. Sweilam NH, Tharwat AA, Moniem NKA. Support vector machine for diagnosis cancer disease: a comparative study. Egypt Inform J. 2010;11:81–92. doi:10.1016/j.eij.2010.10.005.

    Article  Google Scholar 

  75. Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Support vector methods for survival analysis: A comparison between ranking and regression approaches. Artif Intell Med. 2011;53:107–18.

    Article  Google Scholar 

  76. van Gerven MAJ, Taal BG, Lucas PJF. Dynamic Bayesian networks as prognostic models for clinical patient management. J Biomed Inform. 2008;41:515–29. doi:10.1016/j.jbi.2008.01.006.

    Article  Google Scholar 

  77. van Stiphout RGPM, Postma EO, Valentini V, Lambin P. The contribution of machine learning to predicting cancer outcome. Artif Intell. 2010;350:400.

    Google Scholar 

  78. Vapnik VN. Statistical learning theory. New york: Wiley Interscience; 1998.

    MATH  Google Scholar 

  79. Wang SJ, Wissel AR, Luh JY, et al. An interactive tool for individualized estimation of conditional survival in rectal cancer. Ann Surg Oncol. 2011;18:1547–52. doi:10.1245/s10434-010-1512-3.

    Article  Google Scholar 

  80. Williams TGS, Cubiella J, Griffin SJ, et al. Risk prediction models for colorectal cancer in people with symptoms: a systematic review. BMC Gastroenterol. 2016;16:63. doi:10.1186/s12876-016-0475-7.

    Article  Google Scholar 

  81. Yosefian I, Mosa Farkhani E, Baneshi MR. Application of random forest survival models to increase generalizability of decision trees: a case study in acute myocardial infarction. Comput Math Methods Med. 2015;2015:576413. doi:10.1155/2015/576413.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. OHSU-PSU School of Public Health, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA

    Yiyi Chen

  2. Fariborz Maseeh Department of Mathematics and Statistics, Portland State University, Portland, OR, 97006, USA

    Jess A. Millar

Authors
  1. Yiyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jess A. Millar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiyi Chen .

Editor information

Editors and Affiliations

  1. Graduate School of Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Shigeyuki Matsui

  2. Cancer Research and Biostatistics, Seattle, Washington, USA

    John Crowley

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Chen, Y., Millar, J.A. (2017). Machine Learning Techniques in Cancer Prognostic Modeling and Performance Assessment. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_13

Download citation

Publish with us

Policies and ethics

Search

Navigation