Background

The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling.

Objectives

This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population.

Methods

The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver-operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients.

Results

The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model’s performance to predict overall survival.

Conclusion

The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients’ outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram – machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.

中文翻译：

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列线图与机器学习技术在预测舌癌患者总体生存率方面的比较

背景

舌癌总体生存的预测对于个性化护理和患者咨询的计划很重要。

目标

这项研究将诺模图与机器学习模型的性能进行了比较，以预测舌癌的总体存活率。诺模图和机器学习模型是使用来自监视，流行病学和最终结果（SEER）程序数据库的大量数据构建的。进行比较是必要的，以便为临床医生提供一个全面，实用和最准确的辅助系统，以预测该患者群体的总体存活率。

方法

使用的数据集包括7596例舌癌患者的记录。所考虑的机器学习算法是逻辑回归，支持向量机，贝叶斯点数机，增强型决策树，决策森林和决策丛林。这些算法主要根据接收器工作特性（ROC）曲线（AUC）和精度值下的面积进行评估。将产生最佳结果的算法的性能与诺模图进行比较，以预测舌癌患者的总体生存率。

结果

增强型决策树算法优于其他算法。与使用外部验证数据的列线图相比，增强型决策树的准确度为88.7％，而列线图的准确度为60.4％。此外，还发现患者的年龄，T分期，放疗和手术切除是最突出的特征，对机器学习模型预测整体生存的性能具有重大影响。

结论

与诺模图相比，机器学习模型提供了更多个性化和可靠的舌癌预后信息。然而，由诺模图提供的用于估计患者结果的透明度似乎更加自信，并加强了患者与临床医生之间共同决策的原则。因此，诺模图–机器学习（NomoML）预测模型的组合可能有助于改善护理，向患者提供信息，并有助于临床医生做出与舌癌管理相关的决策。