将机器学习与基于Web的工具集成在一起,以在口腔腺囊性癌症中提供个性化的预后
Sakhr Alshwayyat1, Mesk Alkhatib2, Hebah Almahariq2
1King Hussein Cancer Center, Amman, Jordan; Princess Basma Teaching Hospital, Irbid, Jordan; Applied Science Research Center, Applied Science Private University, Amman, Jordan.
Journal of stomatology, oral and maxillofacial surgery
|November 10, 2024
概括
机器学习模型将年龄和转移确定为口腔腺状囊性癌 (ACC) 的关键预后因素,有助于为这种罕见的癌症制定个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 癌症研究 癌症研究
背景情况:
- 口腔腺性囊性癌 (ACC) 是一种罕见的恶性瘤.
- 有限的研究使基于证据的ACC治疗策略变得复杂.
- 这项研究利用机器学习 (ML) 来分析ACC生存结果.
研究的目的:
- 用ML分析口腔ACC的生存结果.
- 为了优化ACC的治疗方法.
- 为了确定ACC的关键预后因素.
主要方法:
- 使用了SEER数据库 (2000-2020年).
- 采用考克斯回归和5个ML算法来预测5年生存率.
- 使用ROC曲线AUC和执行卡普兰-梅尔分析的验证模型.
主要成果:
- 分析了645名ACC患者;硬口腔和脸粘膜是常见的主要部位.
- 仅仅手术就显示了最高的生存率.
- ML模型将年龄,转移和手术确定为显著的预后因素.
结论:
- 研究提供了有限的ACC文献的证据,强调了辅助放射治疗的重要性.
- 转移和年龄是ACC的关键预后因素.
- 开发的ML工具为罕见癌症 (如ACC) 提供了个性化的预后.
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