956
Views
3
CrossRef citations to date
0
Altmetric
Research Article

An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial

, , , , &

References

  • Beaulac, C., J. S. Rosenthal, and D. Hodgson. 2018. A deep latent-variable model application to select treatment intensity in survival analysis. Proceedings of the Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 2018. https://arxiv.org/search/?searchtype=report_num&query=ML4H/2018/53
  • Bou-Hamad, I., D. Larocque, and H. Ben-Ameur. 2011, January. A review of survival trees. Statistics Surveys 5 :44–71. doi:10.1214/09-SS047.
  • Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655.
  • Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Breiman, L., J. Friedman, R. Olshen, and C. Stone. 1984. Classification and regression trees. Monterey, CA: Wadsworth and Brooks.
  • Chen, H.-C., R. L. Kodell, K. F. Cheng, and J. J. Chen. 2012, July 23. Assessment of performance of survival prediction models for cancer prognosis. BMC Medical Research Methodology 12 (1):102. doi:10.1186/1471-2288-12-102.
  • Christodoulou, E., J. Ma, G. S. Collins, E. W. Steyerberg, J. Y. Verbakel, and B. V. Calster. 2019. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology 110:12–22. doi:10.1016/j.jclinepi.2019.02.004.
  • Cox, D. R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological) 34 (2):187–220. http://www.jstor.org/stable/2985181.
  • Cox, D. R. 1975, August. Partial likelihood. Biometrika 62 (2):269–76. doi:10.1093/biomet/62.2.269.
  • Fotso, S. 2018, January. Deep neural networks for survival analysis based on a multi-task framework. arXiv E-prints:arXiv:1801.05512.
  • Fotso, S., et al. 2019. PySurvival: Open source package for survival analysis modeling. https://www.pysurvival.io/
  • Friedman, D. L., L. Chen, S. Wolden, A. Buxton, K. McCarten, T. J. FitzGerald, and C. L. Schwartz. 2014. Dose-intensive response-based chemotherapy and radiation therapy for children and adolescents with newly diagnosed intermediate-risk hodgkin lymphoma: A report from the children’s oncology group study ahod0031. Journal of Clinical Oncology 32 (32):3651–58. (PMID: 25311218). doi:10.1200/JCO.2013.52.5410.
  • Friedman, J., T. Hastie, and R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1):1–22. http://www.jstatsoft.org/v33/i01/.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT Press. http://www.deeplearningbook.org.
  • Graf, E., C. Schmoor, W. Sauerbrei, and M. Schumacher. 1999. Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 18 (September):2529–45. doi:10.1002/(SICI)1097-0258(19990915/30)18:17/183.0.CO;2-5.
  • Haider, H. 2019. MTLR: Survival prediction with multi-task logistic regression [Computer software manual]. (R package version 0.2.1). https://CRAN.R-project.org/package=MTLR.
  • Hand, D. J. 2006, February. Classifier technology and the illusion of progress. Statistical Science 21 (1):1–14. doi:10.1214/088342306000000060.
  • Harrell, F. E., K. L. Lee, and D. B. Mark. 1996. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15 (4):361–87. doi:10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.
  • Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning. 2nd ed. New York: Springer.
  • Hothorn, T., B. Lausen, A. Benner, and M. Radespiel-Tröger. 2004. Bagging survival trees. Statistics in Medicine 23 (1):77–91. doi:10.1002/sim.1593.
  • Hothorn, T., K. Hornik, and A. Zeileis. 2006. Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15 (3):651–74. doi:10.1198/106186006X133933.
  • Hothorn, T., K. Hornik, C. Strobl, and A. Zeileis. 2019. Party: A laboratory for recursive partytioning [Computer software manual]. (R package version 1.3-3). https://cran.r-project.org/web/packages/party/index.html.
  • Hothorn, T., P. Bühlmann, S. Dudoit, A. Molinaro, and M. J. Van Der Laan. 2005, December. Survival ensembles. Biostatistics 7 (3):355–73. doi:10.1093/biostatistics/kxj011.
  • Ishwaran, H., and U. Kogalur. 2019. Fast unified random forests for survival, regression, and classification (rf-src) [Computer software manual]. manual. (R package version 2.9.1). https://cran.r-project.org/package=randomForestSRC.
  • Ishwaran, H., and U. B. Kogalur. 2010. Consistency of random survival forests. Statistics & Probability Letters 80 (13):1056–64. http://www.sciencedirect.com/science/article/pii/S0167715210000672.
  • Ishwaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer. 2008, September. Random survival forests. The Annals of Applied Statistics 2 (3):841–60. doi:10.1214/08-AOAS169.
  • Ishwaran, H., U. B. Kogalur, E. Z. Gorodeski, A. J. Minn, and M. S. Lauer. 2010. High-dimensional variable selection for survival data. Journal of the American Statistical Association 105 (489):205–17. doi:10.1198/jasa.2009.tm08622.
  • Jinga, B., T. Zhangh, Z. Wanga, Y. Jina, K. Liua, W. Qiua, … C. Lia. 2019. A deep survival analysis method based on ranking. Artificial Intelligence in Medicine 98:1–9. doi:10.1016/j.artmed.2019.06.001.
  • Katzman, J. (2017). Deepsurv: Personalized treatment recommender system using a cox proportional hazards deep neural network. https://github.com/jaredleekatzman/DeepSurv
  • Katzman, J., U. Shaham, A. Cloninger, J. Bates, T. Jiang, and Y. Kluger. 2018, December. Deepsurv: Personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Medical Research Methodology. 18 (1). doi:10.1186/s12874-018-0482-1.
  • Kingma, D. P. 2017. Variational inference & deep learning: A new synthesis. Unpublished doctoral dissertation, Universiteit van Armsterdam.
  • Kingma D.P. and Welling M. 2014.Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR)
  • LeBlanc, M., and J. Crowley. 1995. A review of tree-based prognostic models. Recent Advances in Clinical Trial Design and Analysis 75:113–24.
  • Leblanc, M. E., and J. P. Crowley. 1992. Relative risk trees for censored survival data. Biometrics 48 (2):411–25. doi:10.2307/2532300.
  • Liu, Y., K. Gadepalli, M. Norouzi, G. E. Dahl, T. Kohlberger, A. Boyko, and M. C. Stumpe. 2017, March. Detecting cancer metastases on gigapixel pathology images. arXiv E-prints :arXiv:1703.02442.
  • Louizos, C., U. Shalit, J. Mooij, D. Sontag, R. Zemel, and M. Welling. 2017, May. Causal effect inference with deep latent-variable models. . In Advances in Neural Information Processing Systems (pp. 6446-6456).
  • Luck, M., T. Sylvain, H. Cardinal, A. Lodi, and Y. Bengio. 2017. Deep learning for patient-specific kidney graft survival analysis. CoRR, abs/1705.10245. http://arxiv.org/abs/1705.10245
  • Nazabal,A., Olmos, P. M., Ghahramani, Z., & Valera. (2020). Handling incomplete heterogeneous data using vaes. Pattern Recognition, 107501.
  • Peters, A., and T. Hothorn. 2019. ipred: Improved predictors [Computer software manual]. (R package version 0.9-9). https://CRAN.R-project.org/package=ipred.
  • R Core Team. 2013. R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. http://www.R-project.org/.
  • Rodríguez-Ruiz, A., E. Krupinski, -J.-J. Mordang, K. Schilling, S. H. Heywang-Köbrunner, I. Sechopoulos, and R. M. Mann. 2019. Detection of breast cancer with mammography: Effect of an artificial intelligence support system. Radiology 290 (2):305–14. (PMID: 30457482). doi:10.1148/radiol.2018181371.
  • Rodriguez-Ruiz, A., K. Lång, A. Gubern-Merida, M. Broeders, G. Gennaro, P. Clauser, and I. Sechopoulos. 2019, March. Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute 111 (9):916–22. doi:10.1093/jnci/djy222.
  • Sidiropoulos, N., S. H. Sohi, N. Rapin, and F. O. Bagger (2017). sinaplot: An enhanced chart for simple and truthful representation of single observations over multiple classes. https://cran.r-project.org/web/packages/sinaplot/vignettes/SinaPlot.html
  • Simon, N., J. Friedman, T. Hastie, and R. Tibshirani. 2011. Regularization paths for cox’s proportional hazards model via coordinate descent. Journal of Statistical Software, Articles 39 (5):1–13. doi:10.18637/jss.v039.i05.
  • Steck, H., Krishnapuram, B., Dehing-Oberije, C., Lambin, P., & Raykar, V. C. (2008). On ranking in survival analysis: Bounds on the concordance index. In Advances in neural information processing systems (pp. 1209–1216)
  • Strobl, C., A.-L. Boulesteix, A. Zeileis, and T. Hothorn. 2007. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8 (1):25. doi:10.1186/1471-2105-8-25.
  • Therneau, T., B. Atkinson, and B. Ripley (2017). rpart: Recursive partitioning and regression trees [Computer software manual]. (R package version 4.1-11). https://CRAN.R-project.org/package=rpart
  • Van Rossum, G., and F. L. Drake Jr. 1995. Python tutorial. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica.
  • Yu, C.-N., R. Greiner, H.-C. Lin, and V. Baracos. 2011. Learning patient-specific cancer survival distributions as a sequence of dependent regressors. In Advances in neural information processing systems 24, ed. J. Shawe Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, 1845–53. Curran Associates, Inc. http://papers.nips.cc/paper/4210-learning-patient-specific-cancer-survival-distributions-as-a-sequence-of-dependent-regressors.pdf.
  • Zhao, L., and D. Feng. 2019, August. DNNSurv: Deep neural networks for survival analysis using pseudo values. arXiv E-prints :arXiv:1908.02337.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.