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

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.

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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

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Acknowledgments

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

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Authors

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.

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

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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.

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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

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Keywords

  • Artificial intelligence
  • Decision support techniques
  • Prostate cancer
  • Survival