Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review.

IF 1.3
Carlos M Chiesa-Estomba, Manuel Graña, Alfonso Medela, Jon A Sistiaga-Suarez, Jerome R Lechien, Christian Calvo-Henriquez, Miguel Mayo-Yanez, Luigi Angelo Vaira, Alberto Grammatica, Giovanni Cammaroto, Tareck Ayad, Johannes J Fagan
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引用次数: 7

Abstract

Introduction: Despite multiple prognostic indicators described for oral cavity squamous cell carcinoma (OCSCC), its management still continues to be a matter of debate. Machine learning is a subset of artificial intelligence that enables computers to learn from historical data, gather insights, and make predictions about new data using the model learned. Therefore, it can be a potential tool in the field of head and neck cancer.

Methods: We conducted a systematic review.

Results: A total of 81 manuscripts were revised, and 46 studies met the inclusion criteria. Of these, 38 were excluded for the following reasons: use of a classical statistical method (N = 16), nonspecific for OCSCC (N = 15), and not being related to OCSCC survival (N = 7). In total, 8 studies were included in the final analysis.

Conclusions: ML has the potential to significantly advance research in the field of OCSCC. Advantages are related to the use and training of ML models because of their capability to continue training continuously when more data become available. Future ML research will allow us to improve and democratize the application of algorithms to improve the prediction of cancer prognosis and its management worldwide.

机器学习算法作为口腔癌预后和管理决策的计算机辅助决策工具:系统综述。
简介:尽管口腔鳞状细胞癌(OCSCC)有多种预后指标,但其治疗仍然是一个有争议的问题。机器学习是人工智能的一个子集,它使计算机能够从历史数据中学习,收集见解,并使用所学的模型对新数据进行预测。因此,它可以成为头颈癌领域的潜在工具。方法:我们进行了系统综述。结果:共修改81篇文献,46篇研究符合纳入标准。其中,38项研究因以下原因被排除:使用经典统计学方法(N = 16),非OCSCC特异性(N = 15),与OCSCC生存无关(N = 7)。最终分析共纳入8项研究。结论:ML具有显著推进OCSCC领域研究的潜力。机器学习模型的使用和训练的优势在于,当有更多的数据可用时,机器学习模型能够继续训练。未来的机器学习研究将使我们能够改进和民主化算法的应用,以改善全球癌症预后的预测及其管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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