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.

中文翻译：

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长期短期记忆人工神经网络模型，可根据初始治疗策略预测前列腺癌的生存结果：在线决策支持系统的开发。

目的

精确医学的交付是临床和转化研究人员的主要目标。新诊断为前列腺癌（PCa）的患者面临着在多种初始治疗方式之间做出决定的挑战。这项研究的目的是利用人工神经网络（ANN）建模来根据初始治疗方式预测生存结果，并开发一个在线决策支持系统。

方法

回顾性收集1988年1月至2017年12月间7267例诊断为PCa的患者的数据。这些分析包括19个治疗前的临床病理协变量。多层感知器（MLP），用于N年生存预测的MLP（MLP-N）和长期短期记忆（LSTM）ANN模型用于分析无去势抵抗PCa（CRPC）生存，癌症特异性的进展生存期（CSS）和总体生存期（OS），具体取决于初始治疗方式。使用Harrell的C指数比较了ANN和Cox比例风险回归模型的性能。

结果

与Cox比例风险回归模型相比，ANN模型为无CRPC生存期，CSS和OS的5年和10年进展提供了更高的预测能力。LSTM模型具有最高的预测能力，其次是MLP-N和MLP模型。我们根据初始治疗策略，开发了基于LSTM模型的在线决策支持系统，以提供5年和10年的个性化生存结果。

结论

LSTM ANN模型可以根据初始治疗策略提供PCa的个体化生存结果。患者和医疗服务提供者可以使用我们的在线决策支持系统来确定最佳的初始治疗方式，并指导生存预测。