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预测复发使用一个堆叠的denoising自编码器和多方面特征分析的预治疗MRI在患有鼻癌的患者的复发

Yibin Liu¹, Xianwen Wang², Jiongyi Li³

¹Department of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.

Current radiopharmaceuticals

|April 16, 2025

概括

预测鼻癌 (NPC) 复发是非常重要的. 一个新的模型将放射学,自编码器和临床数据与SVM融合在一起,在NPC患者中显示出高准确度的复发预测.

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) 复发预测需要改进的策略.
预处理CE-T1WMRI特征是开发预测模型的关键.

研究的目的:

开发和验证一个多omics特征融合模型,用于预测治疗后NPC复发.
整合深度学习和放射学功能,以提高预测准确度.

主要方法:

用于特征提取,使用了一个深层无监督堆叠无噪声自编码器 (SDAE).
多种omics的特征 (radiomics,自编码器衍生,临床) 被融合在一起.
支持矢量机 (SVM),多层感知子 (MLP),逻辑回归 (LR) 和随机森林 (RF) 用于模型构建.

主要成果:

结合Radiomics,AutoEncoder和Clinical功能的SVM模型实现了最高的性能.
该模型的平均AUC为0.89,准确度为81.5%,灵敏度为67.3%,特异性为97.9%.
其他几种机器学习模型也显示出有希望的预测能力.

结论:

使用CE-T1W放射学,自编码器和临床数据的基于SVM的融合模型是预测NPC复发的可靠工具.
这个模型可以帮助临床医生做出有关NPC患者诊断,治疗和干预措施的明智决策.

关键词:

鼻癌是指鼻癌.磁共振成像技术磁共振成像技术磁共振成像技术预测模型预测模型复发性复发性复发性堆叠的无噪声自动编码器支持矢量机器的支持矢量机器.

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