Skip to main content

Advertisement

SpringerLink
Log in

Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system

  • Original Article
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system.

Methods

Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell’s C-index.

Results

The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10 years, according to the initial treatment strategy.

Conclusion

The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

AUC:

Area under the curve

CRPC:

Castration-resistant prostate cancer

CSS:

Cancer-specific survival

CV:

Cross-validation

LSTM:

Long short-term memory

MLP:

Multilayer perceptron

MLP-N:

MLP for N-year survival prediction

OS:

Overall survival

PCa:

Prostate cancer

References

  1. Koo KC, Cho JS, Bang WJ et al (2018) Cancer-specific mortality among Korean men with localized or locally advanced prostate cancer treated with radical prostatectomy versus radiotherapy: a multi-center study using propensity scoring and competing risk regression analyses. Cancer Res Treat 50:129–137

    Article  Google Scholar 

  2. Mohler JL, Armstrong AJ, Bahnson RR et al (2016) Prostate cancer, version 1.2016. J Natl Compr Canc Netw 14:19–30

    Article  Google Scholar 

  3. Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 71:618–629

    Article  Google Scholar 

  4. Sanda MG, Cadeddu JA, Kirkby E et al (2018) Clinically localized prostate cancer: AUA/ASTRO/SUO guideline. Part I: risk stratification, shared decision making, and care options. J Urol 199:683–690

    Article  Google Scholar 

  5. Cuypers M, Lamers RED, Cornel EB et al (2018) The impact of prostate cancer diagnosis and treatment decision-making on health-related quality of life before treatment onset. Support Care Cancer 26:1297–1304

    Article  Google Scholar 

  6. D'Amico AV, Cote K, Loffredo M et al (2002) Determinants of prostate cancer-specific survival after radiation therapy for patients with clinically localized prostate cancer. J Clin Oncol 20:4567–4573

    Article  Google Scholar 

  7. Cooperberg MR, Broering JM, Carroll PR (2009) Risk assessment for prostate cancer metastasis and mortality at the time of diagnosis. J Natl Cancer Inst 101:878–887

    Article  Google Scholar 

  8. Kourou K, Exarchos TP, Exarchos KP et al (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17

    Article  CAS  Google Scholar 

  9. Spelt L, Nilsson J, Andersson R et al (2013) Artificial neural networks–a method for prediction of survival following liver resection for colorectal cancer metastases. Eur J Surg Oncol 39:648–654

    Article  CAS  Google Scholar 

  10. Peng JH, Fang YJ, Li CX et al (2016) A scoring system based on artificial neural network for predicting 10-year survival in stage II a colon cancer patients after radical surgery. Oncotarget 7:22939–22947

    Article  Google Scholar 

  11. Litwin MS, Tan HJ (2017) The diagnosis and treatment of prostate cancer: a review. JAMA 317:2532–2542

    Article  Google Scholar 

  12. Campbell JM, Raymond E, O'Callaghan ME et al (2017) Optimum tools for predicting clinical outcomes in prostate cancer patients undergoing radical prostatectomy: systematic review of prognostic accuracy and validity. Clin Genitourin Cancer 15:e827–e834

    Article  Google Scholar 

  13. Chang SW, Abdul-Kareem S, Merican AF et al (2013) Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinform 14:170

    Article  Google Scholar 

  14. Chi CL, Street WN, Wolberg WH (2007) Application of artificial neural network-based survival analysis on two breast cancer datasets. AMIA Annu Symp Proc 130–134.

  15. Rumelhart D, Hinton G, Williams R et al (1986) Learning internal representations by error propagation. In: Rumelhart DE, MCclelland JL (eds) Parallel distributed processing 1986. MIT Press, Cambridge, pp 318–362

    Google Scholar 

  16. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  CAS  Google Scholar 

  17. Oh SE, Seo SW, Choi MG et al (2018) Prediction of overall survival and novel classification of patients with gastric cancer using the survival recurrent network. Ann Surg Oncol 25:1153–1159

    Article  Google Scholar 

  18. Obrzut B, Kusy M, Semczuk A et al (2017) Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods. BMC Cancer 17:840

    Article  Google Scholar 

  19. Qiao G, Li J, Huang A et al (2014) Artificial neural networking model for the prediction of post-hepatectomy survival of patients with early hepatocellular carcinoma. J Gastroenterol Hepatol 29:2014–2020

    Article  CAS  Google Scholar 

  20. Snow PB, Rodvold DM, Brandt JM (1999) Artificial neural networks in clinical urology. Urology 54:787–790

    Article  CAS  Google Scholar 

  21. Stephenson AJ, Kattan MW, Eastham JA et al (2009) Prostate cancer-specific mortality after radical prostatectomy for patients treated in the prostate-specific antigen era. J Clin Oncol 27:4300–4305

    Article  Google Scholar 

  22. Korets R, Motamedinia P, Yeshchina O et al (2011) Accuracy of the Kattan nomogram across prostate cancer risk-groups. BJU Int 108:56–60

    Article  Google Scholar 

  23. Listgarten J, Damaraju S, Poulin B et al (2004) Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clin Cancer Res 10:2725–2737

    Article  CAS  Google Scholar 

  24. van Stam MA, Pieterse AH, van der Poel HG et al (2018) Shared decision making in prostate cancer care-encouraging every patient to be actively involved in decision making or ensuring the patient preferred level of involvement? J Urol 200:582–589

    Article  Google Scholar 

  25. Stacey D, Legare F, Lewis KB (2017) Patient decision aids to engage adults in treatment or screening decisions. JAMA 318:657–658

    Article  Google Scholar 

  26. Feldman-Stewart D, Tong C, Brundage M et al (2018) Making their decisions for prostate cancer treatment: patients' experiences and preferences related to process. Can Urol Assoc J 12:337–343

    Article  Google Scholar 

  27. Harter M, Moumjid N, Cornuz J et al (2017) Shared decision making in 2017: International accomplishments in policy, research and implementation. Z Evid Fortbild Qual Gesundhwes 123–124:1–5

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported through a Young Researcher Program Grant from the National Research Foundation of Korea (NRF-2017R1C1B5017516).

Author information

Author notes

  1. Kyo Chul Koo and Kwang Suk Lee equally contributed to the study.

Authors and Affiliations

  1. Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea

    Kyo Chul Koo, Kwang Suk Lee, Gyu Rang Min, Young Hwa Lee, Woong Kyu Han, Koon Ho Rha, Sung Joon Hong, Seung Choul Yang & Byung Ha Chung

  2. Selvas AI, Seoul, Republic of Korea

    Suah Kim & Choongki Min

Authors
  1. Kyo Chul Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwang Suk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suah Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Choongki Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gyu Rang Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Young Hwa Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Woong Kyu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Koon Ho Rha
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sung Joon Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Seung Choul Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Byung Ha Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Protocol/project development: Koo, KS Lee, Han, Rha, Hong, Yang and Chung. Data collection and management: Koo, KS Lee, YH Lee, and GR Min. Data analysis: Kim and C Min. Manuscript writing/editing: Koo and Chung.

Corresponding author

Correspondence to Byung Ha Chung.

Ethics declarations

Conflict of interest

All of the authors declare that they have no conflicts of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was not required for the purposes of this study as it was based upon retrospective anonymous patient data and did not involve patient intervention or the use of human tissue samples.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koo, K.C., Lee, K.S., Kim, S. et al. Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. World J Urol 38, 2469–2476 (2020). https://doi.org/10.1007/s00345-020-03080-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-020-03080-8

Keywords

Search

Navigation