Background

Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care.

Objectives

This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice.

Data sources

We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC.

Eligibility criteria

Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered.

Data extraction

Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies.

Results

A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations.

Conclusion

Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.

中文翻译：

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口腔鳞状细胞癌的机器学习：现状、临床问题和未来前景——系统评价

背景

口腔癌可以表现出不同的行为模式。为了正确有效地管理口腔癌，早期诊断和准确预测预后很重要。为了实现这一目标，人工智能 (AI) 或其子领域机器学习因其通过提高诊断精度和结果预测来彻底改变癌症管理的潜力而受到吹捧。然而，迄今为止，它对实际医疗实践或患者护理的贡献很少。

目标

本研究系统回顾了机器学习在口腔鳞状细胞癌 (OSCC) 中的诊断和预后应用，并强调了临床医生在日常临床实践中实施基于机器学习的模型的一些局限性和担忧。

数据来源

我们在 OvidMedline、PubMed、Scopus、Web of Science 和电气和电子工程师协会 (IEEE) 数据库中搜索了从成立到 2020 年 2 月的将机器学习用于 OSCC 诊断或预后目的的文章。

资格标准

只考虑了检查机器学习模型在预后和/或诊断目的上的应用的原始研究。

数据提取

由两名研究人员（AR 和 OY）使用预定义的研究选择标准独立提取文章。我们在搜索和筛选过程中使用了系统评价和元分析首选报告项目 (PRISMA)。我们还使用了偏倚风险评估工具 (PROBAST) 的预测模型来评估偏倚风险 (ROB) 和纳入研究的质量。

结果

总共发表了 41 项研究，使用机器学习来帮助 OSCC 的诊断/或预后。大多数这些研究使用支持向量机 (SVM) 和人工神经网络 (ANN) 算法作为机器学习技术。在这些研究中，它们的特异性为 0.57 至 1.00，灵敏度为 0.70 至 1.00，准确度为 63.4 % 至 100.0 %。主要的限制和问题可以分为机器学习科学固有的挑战或与临床实施相关的挑战。

结论

据报道，机器学习模型在口腔癌研究中的诊断和预后分析方面显示出有希望的性能。应开发这些模型以进一步增强可解释性、可解释性和外部验证的普遍性，以便安全地集成到日常临床实践中。此外，在临床实践中采用这些模型的监管框架是必要的。