Introduction

Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients’ relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use.

Methodologies

We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry.

Results

The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients’ survival periods are segmented into classes of ‘<=6 months’,’ 0.5 – 2 years’ and ‘>2 years’ and Root Mean Squared Error (RMSE) of 13.5 % and$R^2$ value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression.

Conclusions

This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.

中文翻译：

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肺癌生存期预测和理解：深度学习方法

介绍

通过癌症的早期诊断来预测生存期有很多好处。它使患者和护理人员都能计划资源，时间和护理强度，从而为患者提供最佳的治疗途径。在本文中，通过关注肺癌患者，我们使用深度学习技术构建了多个生存预测模型，以解决癌症生存分类和回归问题。我们还进行功能重要性分析，以了解肺癌患者的相关因素如何影响其生存期。我们致力于确定一种通常和实际适用于医疗用途的估计生存能力的方法。

方法论

我们比较了三种最流行的深度学习架构的性能-人工神经网络（ANN），卷积神经网络（CNN）和递归神经网络（RNN），同时将深度学习模型与传统机器学习模型的性能进行了比较。数据从监测，流行病学和最终结果（SEER）癌症注册表的肺癌部分获得。

结果

在分类和回归方法上，深度学习模型都优于传统的机器学习模型。当患者的生存期分为“ <= 6个月”，“ 0.5 – 2年”和“> 2年”以及“均方根误差”（RMSE）时，我们的分类方法获得了71.18％的最佳准确性。 13.5％和${\mathrm{[R}}^2$ 深度学习模型的回归方法值为0.5，而传统机器学习模型的回归分类饱和度为61.12％分类精度和14.87％RMSE。

结论

这种方法可以作为早期预测的基准，而预测可以通过从接受治疗的患者那里收集的更多临时治疗信息来进一步改善。此外，我们评估了功能重要性，以研究模型的可解释性，从而获得了对生存分析模型以及对癌症生存期预测至关重要的因素的进一步了解。