Modeling and Predicting the Survival of Breast Cancer Patients via Deep Neural Networks and Bayesian Algorithm

Document Type : Original Paper

Authors

1 Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

2 Department of Biomedical Engineering, Meybod University, Meybod, Iran

Abstract

Introduction: Due to breast cancer's prevalence as the second leading cause of cancer death worldwide, accurate survival prediction models are crucial. This study aimed to use an optimized deep neural network to predict breast cancer patient survival.
Material and Methods: The present study is an analytical study. The information utilized in this research is derived from the METABRIC database, associated with the molecular categorization of breast cancer patients, from the International Federation of Breast Cancer's Molecular Taxonomy Data. The total number of patients studied is 1981. Of these, 888 patients were under care until their death, and the remaining patients withdrew from the study during its course. In this database, 22 clinical features of patients have been considered, which includes a total of 6 quantitative features and 16 qualitative features. A deep neural network model called the optimized DeepHit is used to predict survival. The optimal parameters for specific variables of the neural network are obtained by the Bayesian algorithm.
Results: The optimized model has achieved the criterion of c_index = 0.748, which is a criterion for measuring the capability of survival analysis models.
Conclusion: The proposed model was compared with previous models using real and synthetic datasets. The results show that the optimized DeepHit achieved significantly better performance and statistically significant improvements over previous methods.

Keywords

Main Subjects

References

  1. El-Bendary N, Belal NA. A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification. Applied Soft Computing. 2020 Jun 1;91:106238.
  2. Singh D, Singh AK. Role of image thermography in early breast cancer detection-Past, present and future. Computer methods and programs in biomedicine. 2020 Jan 1;183:105074.
  3. McPherson K, Steel C, Dixon JM. Breast cancer-epidemiology, risk factors, and genetics. BMJ. 2000;321(7261):624-8.
  4. Forouzanfar MH, Foreman KJ, Delossantos AM, Lozano R, Lopez AD, Murray CJ, et al. Breast and cervical cancer in 187 countries between 1980 and 2010: a systematic analysis. The lancet. 2011; 378(9801):1461-84.
  5. Delen, Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine. 2005;34(2):113-27
  6. Campone M, Fumoleau P, Bourbouloux E, Kerbrat P, Roché H. Taxanes in adjuvant breast cancer setting: which standard in Europe? Critical reviews in oncology/hematology. 2005; 55(3):167-75.
  7. hammadpour A, Jahangirian E, Moharrami T, Rad GG, Sheikhani LJ, Taghizadeh S. Breast Cancer, Genetic Factors and Methods of Diagnosis. Sarem Journal of Reproductive Medicine. 2020;4(4):198-207.
  8. Mukherjee A, Russell R, Chin SF, Liu B, Rueda OM, Ali HR, et al. Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. NPJ breast cancer. 2018;4(1):1-9.
  9. Rakha EA, Green AR. Molecular classification of breast cancer: what the pathologist needs to know. Pathology. 2017;49(2):111-9.
  10. Hao J, Kim Y, Mallavarapu T, Oh JH, Kang M. Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. BMC medical genomics. 2019; 12(10):1-13.
  11. Collett D. Modelling survival data in medical research. 2015: CRC press.
  12. Stevenson M, EpiCentre IV. An introduction to survival analysis. EpiCentre, IVABS, Massey University. 2009 Jun 4.
  13. Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. International journal of Ayurveda research. 2010;1(4):274.
  14. Fisher LD, Lin DY. Time-dependent covariates in the Cox proportional-hazards regression model. Annual review of public health. 1999; 20(1):145-57.
  15. Therneau TM, Grambsch PM, Therneau TM, Grambsch PM. The cox model. Springer New York; 2000.
  16. O’Brien RC, Ishwaran H, Szczotka-Flynn LB, Lass JH. Random survival forests analysis of intraoperative complications as predictors of descemet stripping automated endothelial keratoplasty graft failure in the cornea preservation time study. JAMA ophthalmology. 2021 Feb 1;139(2):191-7.
  17. Pickett KL, Suresh K, Campbell KR, Davis S, Juarez-Colunga E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC medical research methodology. 2021 Dec;21:1-4.
  18. Liestbl K, Andersen PK, Andersen U.Survival analysis and neural nets. Statistics in medicine. 1994; 13(12):1189-200.
  19. Faraggi D, Simon R. A neural network model for survival data. Statistics in medicine. 1995; 14(1): 73-82.
  20. Street WN. A Neural Network Model for Prognostic Prediction. InICML.1998; 540-6.
  21. Yu CN, Greiner R, Lin HC, Baracos V. Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems. 2011.;24:1845-53.
  22. Fotso S. Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512, 2018.
  23. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology. 2018;18(1):1-12.
  24. Harrell Jr FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine. 1996;(4)15:361-87.
  25. Lee C, Zame W, Yoon J, Van Der Schaar M. Deephit: A deep learning approach to survival analysis with competing risks. in Thirty-second AAAI conference on artificial intelligence. 2018.
  26. A Tour of Survival Analysis, from Classical to Modern, Some Software Packages. Available from:: https://sites.google.com/view/chil-survival.
  27. Nagpal C, Li X, Dubrawski A. Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks. IEEE Journal of Biomedical and Health Informatics. 2021;25(8):3163-75.
  28. Cuevas-Delgado P, Dudzik D, Miguel V, Lamas S, Barbas C. Data-dependent normalization strategies for untargeted metabolomics—A case study. Analytical and Bioanalytical Chemistry. 2020 Sep;412:6391-405.
  29. Frazier PI. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811. 2018.
  30. Lee ML, Whitmore GA. Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Statistical Science. 2006; 21(4):501-13.

 

 

 

 

 

Iranian Journal of Medical Physics
Volume 21, Issue 3 - Serial Number 3
May and June 2024
Pages 203-210

Files

History

  • Receive Date: 29 November 2022
  • Revise Date: 10 May 2023
  • Accept Date: 13 May 2023

Share

How to cite

Statistics