BACKGROUND The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary centers. OBJECTIVES We compared the performance of four machine learning (ML) algorithms for predicting the risk of locoregional recurrences in patients with OTSCC. These algorithms were Support Vector Machine (SVM), Naive Bayes (NB), Boosted Decision Tree (BDT), and Decision Forest (DF). MATERIALS AND METHODS The study cohort comprised 311 cases from the five University Hospitals in Finland and A.C. Camargo Cancer Center, São Paulo, Brazil. For comparison of the algorithms, we used the harmonic mean of precision and recall called F1 score, specificity, and accuracy values. These algorithms and their corresponding permutation feature importance (PFI) with the input parameters were externally tested on 59 new cases. Furthermore, we compared the performance of the algorithm that showed the highest prediction accuracy with the prognostic significance of depth of invasion (DOI). RESULTS The results showed that the average specificity of all the algorithms was 71% . The SVM showed an accuracy of 68% and F1 score of 0.63, NB an accuracy of 70% and F1 score of 0.64, BDT an accuracy of 81% and F1 score of 0.78, and DF an accuracy of 78% and F1 score of 0.70. Additionally, these algorithms outperformed the DOI-based approach, which gave an accuracy of 63%. With PFI-analysis, there was no significant difference in the overall accuracies of three of the algorithms; PFI-BDT accuracy increased to 83.1%, PFI-DF increased to 80%, PFI-SVM decreased to 64.4%, while PFI-NB accuracy increased significantly to 81.4%. CONCLUSIONS Our findings show that the best classification accuracy was achieved with the boosted decision tree algorithm. Additionally, these algorithms outperformed the DOI-based approach. Furthermore, with few parameters identified in the PFI analysis, ML technique still showed the ability to predict locoregional recurrence. The application of boosted decision tree machine learning algorithm can stratify OTSCC patients and thus aid in their individual treatment planning.

中文翻译：

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有监督的机器学习分类技术在预测早期口腔癌局部复发中的比较。

背景技术对于早期口腔舌鳞状细胞癌（OTSCC）复发风险的正确估计对于做出个体治疗决策至关重要。但是，即使对于经验丰富的多学科中心而言，这仍然是一个挑战。目的我们比较了四种机器学习（ML）算法在预测OTSCC患者局部复发风险中的性能。这些算法是支持向量机（SVM），朴素贝叶斯（NB），增强决策树（BDT）和决策森林（DF）。材料与方法该研究队列包括来自芬兰五所大学医院和巴西圣保罗AC Camargo癌症中心的311例病例。为了比较算法，我们使用了精度和召回率的谐波平均值，称为F1得分，特异性和准确性值。这些算法及其与输入参数对应的置换特征重要性（PFI）在59个新案例上进行了外部测试。此外，我们比较了算法的性能，该算法显示出最高的预测准确度和入侵深度（DOI）的预后意义。结果结果表明，所有算法的平均特异性为71％。SVM的准确性为68％，F1评分为0.63，NB的准确性为70％，F1评分为0.64，BDT的准确性为81％，F1评分为0.78，DF的准确性为78％，F1评分为0.70 。此外，这些算法的性能优于基于DOI的方法，该方法的准确性为63％。通过PFI分析，三种算法的整体精度没有显着差异。PFI-BDT准确度提高到83.1％，PFI-DF提高到80％，PFI-SVM降低到64.4％，而PFI-NB准确度显着提高到81.4％。结论我们的发现表明，使用增强决策树算法可以实现最佳分类精度。此外，这些算法的性能优于基于DOI的方法。此外，由于在PFI分析中确定的参数很少，因此ML技术仍显示出预测局部复发的能力。增强决策树机器学习算法的应用可以对OTSCC患者进行分层，从而有助于他们的个性化治疗计划。由于在PFI分析中确定的参数很少，因此ML技术仍具有预测局部复发的能力。增强决策树机器学习算法的应用可以对OTSCC患者进行分层，从而有助于他们的个性化治疗计划。由于在PFI分析中确定的参数很少，因此ML技术仍具有预测局部复发的能力。增强决策树机器学习算法的应用可以对OTSCC患者进行分层，从而有助于他们的个性化治疗计划。