Skip to main content
SpringerLink
Log in

Advances in Machine Learning Approaches in Cancer Prognosis

  • Chapter
  • First Online:
Advanced Machine Learning Approaches in Cancer Prognosis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 204))

  • 657 Accesses

Abstract

Machine Learning (ML) methods have numerous promising applications in medicine, including cancer risk assessment, lesion detection using biomarkers and image segmentation, prediction of disease grading, staging, prognosis and therapy response and so on. The ML methods have the potential to improve analysis of various medical data, such as multidimensional numerical, visual and text data, compared to conventional statistical analysis. A brief overview presented in this chapter discusses the trends in predicting some types of cancer, including the application of Deep Learning (DL) methods.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288, 318–328 (2018)

    Article  Google Scholar 

  2. Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl. Based Syst. 80, 14–23 (2015)

    Article  Google Scholar 

  3. Wang, G., Zhang, G., Choi, K.-S., Lam, K.-M., Lu, J.: Output based transfer learning with least squares support vector machine and its application in bladder cancer prognosis. Neurocomputing 387, 279–292 (2020)

    Article  Google Scholar 

  4. Cuocolo, R., Caruso, M., Perillo, T., Ugga, L., Petretta, M.: Machine learning in oncology: a clinical appraisal. Cancer Lett. 481, 55–62 (2020)

    Article  Google Scholar 

  5. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Computation. and Structural Biotechnology J. 13, 8–17 (2015)

    Article  Google Scholar 

  6. Liu, S., Wu, J., Xia, Q., Liu, H., Li, W., Xia, X., Wang, J.: Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning. Computational and Structural Biotechnology J. 18, 1891–1903 (2020)

    Article  Google Scholar 

  7. Savareh, B.A., Aghdaie, H.A., Behmanesh, A., Bashiri, A., Sadeghi, A., Zali, M., Shams, R.: A machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA signatures. Pancreatology 20, 1195–1204 (2020)

    Article  Google Scholar 

  8. He, B., Dai, C., Lang, J., Bing, P., Tian, G., Wang, B., Yang, J.: A Machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation. BBA—Molecular Basis Disease 1866, 165916.1–165916.7 (2020)

    Google Scholar 

  9. Shao, W., Wang, T., Sun, L., Dong, T., Han, Z., Huang, Z., Zhang, J., Zhang, D., Huang, K.: Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers. Med. Image Anal. 65, 101795.1–101795.10 (2020)

    Google Scholar 

  10. Houssein, E.H., et al.: Deep and machine learning techniques for medical imaging-based breast cancer: a comprehensive review. Exp. Syst. Appl. 114161 (2020)

    Google Scholar 

  11. Wang, X., Yang, W., Weinreb, J., Han, J., Li, Q., Kong, X., Yan, Y., Ke, Z., Luo, B., Liu, T., Wang, L.: Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci. Rep. 7, 15415 (2017)

    Article  Google Scholar 

  12. Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T., Franco, I., Mak, R.H., Aerts, H.: Deep learning predicts lung cancer treatment response from serial medical imaging. Clin. Cancer Res. 25, 3266–3275 (2019)

    Article  Google Scholar 

  13. Patil, R., Bellary, S.: Machine learning approach in melanoma cancer stage detection. J. King Saud Univ. Comput. Inform. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.09.002

  14. Abreu, P.H., Santos, M.S., Abreu, M.H., Andrade, B., Silva, D.C.: Predicting breast cancer recurrence using machine learning techniques: a systematic review. ACM Comput. Surv. 49, 52.1–52.40 (2016)

    Google Scholar 

  15. Colleoni, M., Sun, Z., Price, K.N., Karlsson, P., Forbes, J.F., Thürlimann, B., Gianni, L., Castiglione, M., Gelber, R.D., Coates, A.S., Goldhirsch, A.: Annual Hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the international breast cancer study group trials I to V. J. Clin. Oncol. 34, 927–935 (2016)

    Article  Google Scholar 

  16. Wang, C., Cicek, M.S., Charbonneau, B., Kalli, K.R., Armasu, S.M., Larson, M.C., Konecny, G.E., Winterhoff, B., Fan, J.-B., Bibikova, M., Chien, J., Shridhar, V., Block, M.S., Hartmann, L.C., Visscher, D.W., Cunningham, J.M., Knutson, K.L., Fridley, B.L., Goode, E.L.: Tumor Hypomethylation at 6p21.3 associates with longer time to recurrence of high-grade serous epithelial Ovarian cancer. Cancer Res. 74, 3084–3091 (2014)

    Google Scholar 

  17. Macías-García, L., Martínez-Ballesteros, M., Luna-Romera, J.M., García-Heredia, J.M., García-Gutierrez, J., Riquelme-Santos, J.C.: Machine learning models and gene-weight significance. Artif. Intell. Med. 110, 101976.1–101976.16 (2020)

    Google Scholar 

  18. Reyes, O., Perez, E., Luque, R.M., Castano, J., Ventura, S.: A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: an application in cancer diagnosis. Artif. Intell. Med. 108, 101950.1–101950.13 (2020)

    Google Scholar 

  19. Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, pp. 4765–4774 (2017)

    Google Scholar 

  20. Koikea, Y., Aokage, K., Ikeda, K., Nakai, T., Tane, K., Miyoshi, T., Sugano, M., Kojima, M., Fujii, S., Kuwata, T., Ochiai, A., Tanaka, T., Suzuki, K., Tsuboi, M., Ishii, G.: Machine learning-based histological classification that predicts recurrence of peripheral lung squamous cell carcinoma. Lung Cancer 147, 252–258 (2020)

    Article  Google Scholar 

  21. Faraggi, D., Simon, R.: A neural network model for survival data. Stat. Med. 14(1), 73–82 (1995)

    Article  Google Scholar 

  22. Ching, T., Zhu, X., Garmire, L.X.: Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput. Biol. 14, (2018)

    Article  Google Scholar 

  23. Zhu, W., Xie, L., Han, J., Guo, X.: The application of deep learning in cancer prognosis prediction. Cancers 12, 603.1–603.19 (2020)

    Google Scholar 

  24. Khan, U., Shin, H., Choi, J.P., Kim, M.: wFDT—Weighted fuzzy decision trees for prognosis of breast cancer survivability. In: Roddick, J.F., Li, J., Christen, P., Kennedy, P.J. (eds.) Proceedings of the 7th Australasian Data Mining Conference, Australian Computer Society, Glenelg, South Australia, pp. 141–152 (2008)

    Google Scholar 

  25. Sun, D., Wang, M., Li, A.: A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE ACM Trans. Comput. Biol. Bioinform. 16(3), 841–850 (2018)

    Article  Google Scholar 

  26. Oh, S.E., Choi, M.-G., Seo, S.W.: ASO author reflections: use of the survival recurrent network for prediction of overall survival in patients with gastric cancer. Ann. Surg. Oncol. 25, 1153–1159 (2018)

    Article  Google Scholar 

  27. Samala, R.K., Chan, H.-P., Hadjiiski, L., Helvie, M.A., Richter, C.D., Cha, K.H.: Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans. Med. Imaging 38, 686–696 (2019)

    Article  Google Scholar 

  28. Ciompi, F., de Hoop, B., van Riel, S.J., Chung, K., Scholten, E.T., Oudkerk, M., de Jong, P.A., Prokop, M., van Ginneken, B.: Automatic classification of pulmonary perifissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal. 26, 195–202 (2015)

    Article  Google Scholar 

  29. Gao, H.X., Huang, S.G., Du, J.F., Zhang, X.C., Jiang, N., Kang, W.X., Mao, J., Zhao, Q.: Comparison of prognostic indices in NSCLC patients with brain metastases after radiosurgery. Int. J. Biol. Sci. 14, 2065–2072 (2018)

    Article  Google Scholar 

  30. Alabi, R.O., Makitie, A.A., Pirinen, M., Elmusrati, M., Leivo, I., Almangush, A.: Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer. Int. J. Med. Inform. 145, 104313.1–104313.9 (2021)

    Google Scholar 

  31. Doppalapudi, S., Qiu, R.G., Badr, Y.: Lung cancer survival period prediction and understanding: deep learning approaches. Int. J. Med. Inform. 104371 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Reshetnev Siberian State University of Science and Technology, Institute of Informatics and Telecommunications, 31, Krasnoyarsky Rabochy ave, Krasnoyarsk, 660037, Russian Federation

    Margarita N. Favorskaya

Authors
  1. Margarita N. Favorskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Margarita N. Favorskaya .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  2. Department of Informatics and Computer Techniques, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  3. Elizabeth Grove Surgery, Elizabeth Grove, SA, Australia

    Seema Jain

  4. Department of Computer Application, Veer Surendra Sai University of Technology, Odisha, India

    Bighnaraj Naik

  5. Department of Electrical and Electronics Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Manohar Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Favorskaya, M.N. (2021). Advances in Machine Learning Approaches in Cancer Prognosis. In: Nayak, J., Favorskaya, M.N., Jain, S., Naik, B., Mishra, M. (eds) Advanced Machine Learning Approaches in Cancer Prognosis. Intelligent Systems Reference Library, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-030-71975-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation