一个创新和高效的头癌诊断预测流:基于文本和多中心PET/CT图像的多模式生存分析预测的深度学习方法

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Updated: Jul 2, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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Zhaonian Wang1,2,3, Chundan Zheng1,2,3,4, Xu Han1,2,3,4

  • 1School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.

Diagnostics (Basel, Switzerland)
|February 24, 2024
PubMed
概括

这项研究引入了一种新的多模式图像-文本融合策略,用于头癌 (HNC) 的预后. 深度学习模型整合了PET,CT和临床文本数据,显著改善了生存预测.

关键词:
聚乙烯/聚乙烯/聚乙烯计算机辅助诊断是指计算机辅助的诊断.头部和部癌症是一种癌症.多模式融合的多模式融合.生存分析,生存分析.

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