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多参数机器学习算法用于人类乳头瘤病毒状态和口腔癌患者的生存预测

Sherwin Fazelpour¹, Maryam Vejdani-Jahromi², Artem Kaliaev²

¹Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA.

|September 23, 2023

概括

机器学习模型可以预测人类乳头瘤病毒 (HPV) 状态和口腔癌 (OPC) 患者的存活率. 这些人工智能工具为改善临床指导和患者护理提供了潜力.

科学领域:

在瘤学瘤学.
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

人类乳头瘤病毒 (HPV) 状态是口腔癌 (OPC) 的关键预后因素.
准确识别高风险OPC患者对于优化治疗策略至关重要.
需要使用非侵入性方法来管理OPC患者并预测结果.

研究的目的:

开发和评估机器学习 (ML) 模型,用于在OPC中非侵入性地表征HPV状态.
根据HPV状态和临床数据,确定最佳的ML模型来预测患者的生存率.
探索AI在改善OPC临床指导和患者护理方面的潜力.

主要方法:

使用492名OPC患者的数据库开发了多参数算法.
模型包含临床数据,包括年龄,性别,吸烟/饮酒习惯,癌症亚位点和分期 (TNM,AJCC第7版).
算法性能被评估使用准确度和接收机操作员特征曲线 (AUC) 下的面积在 4:1 的训练:测试分割.

主要成果:

一个整体模型在HPV状态预测方面实现了0.83和78.7%的AUC准确度.
对于生存预测,组合模型的AUC值为0.91,准确率为87.7%.
这些结果表明ML在预测HPV状况和生存率方面的有效性.

结论:

人工智能显示出使用瘤成像和患者数据预测HPV状况和OPC结果的巨大潜力.
开发的算法可以非侵入性地优化临床指导,并增强口腔大癌患者的护理.
基于ML的洞察力可以导致对OPC患者的更个性化和更有效的管理.

关键词:

癌症成像档案馆 (TCIA) 提供癌症成像档案.人类乳头瘤病毒人类乳头瘤病毒机器学习是机器学习.这是口腔喉癌.生存预测的预测.

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