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基于MRI的深度学习签名可能为鼻癌提供风险分层策略

Chen Yang¹, Yuan Chen², Luchao Zhu¹

¹Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

|June 26, 2023

概括

使用MRI扫描的深度学习模型准确地预测了鼻癌 (NPC) 的预后. 这种人工智能工具有助于医生分层患者的风险,并制定量身定制的治疗策略,以获得更好的结果.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

鼻癌 (NPC) 的预后是复杂的,因为多因素的影响.
准确预测NPC患者的结果对临床医生来说是具有挑战性的.

研究的目的:

开发一种深度学习 (DL) 签名,用于NPC患者的风险分层.
利用MRI成像和临床数据来改善预后预测.

主要方法:

一组293名NPC患者被分为培训,验证和测试组.
使用Res-Net18算法创建基于MRI的DL模型和临床数据模型.
使用曲线下的面积 (AUC) 和一致性指数 (C指数) 评估模型性能.

主要成果:

基于MRI的DL模型表现出比临床数据模型更好的性能 (AUC:0.8861比0.745;C指数:0.865比0.727).
卡普兰-梅尔生存分析证实了基于MRI模型确定的风险组之间的显著生存差异.

结论:

对MRI扫描的深度学习分析显示,对预测NPC预后有希望.
这种人工智能驱动的方法可以成为NPC风险分层和治疗规划的新工具.

关键词:

深度学习是一种深度学习.这就是为什么MRI是MRI.鼻癌是指鼻癌.预测预后预测预测风险分层是指风险的分层.

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