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基于DeepSurv的深度学习模型,用于预测舌头状细胞癌的生存率和个性化治疗建议

Weiyi Lin¹, Dalong Shu¹, Ningbo Geng¹

¹Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery

|June 3, 2025

概括

一个新的深度神经网络改善了舌头状细胞癌 (TSCC) 的生存预测和治疗建议. 这种AI模型准确地识别了高风险患者和辅助疗法的潜在益处.

科学领域:

在瘤学瘤学.
人工智能的人工智能
生物信息学是一种生物信息学.

背景情况:

舌头状细胞癌 (TSCC) 在头癌中构成重大挑战.
准确的生存预测和个性化治疗策略对于改善患者的治疗结果至关重要.
现有的模型可能无法完全捕捉TSCC中复杂的预后因素.

研究的目的:

开发和验证一个深度学习模型,以提高TSCC的生存预测.
利用该模型为TSCC患者推最佳辅助疗法.
确定影响TSCC患者存活率的关键预后因素.

主要方法:

开发基于DeepSurv的深度神经网络,结合双余块和批量规范化.
从SEER数据库 (2000-2021) 中对大型队列 (2,015名患者) 进行模型训练.
使用内部 (504名患者) 和外部 (249名患者) 队列的验证,采用复合Cox排名损失函数.

主要成果:

该模型实现了高预测准确度,一致性指数为0.781 (内部) 和0.744 (外部).
时间依赖的AUC表现出1至5年生存预测的强表现.
SHAP分析确定了年龄,瘤大小和组织学等级作为关键预后指标.

结论:

开发的深度神经网络显示出在TSCC中准确预测生存率和风险分层的巨大潜力.
该模型可以帮助识别可能受益于特定辅助疗法的TSCC患者.
建议对这种人工智能驱动方法的临床实施进行前性验证.

关键词:

深度学习是一种深度学习.对生存分析的分析.舌头状细胞癌瘤治疗建议治疗建议

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