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Updated: Jul 13, 2025

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

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

Published on: October 10, 2018

通过深度学习预测脑膜瘤等级和病理标记物的表达.

Jiawei Chen¹, Yanping Xue¹, Leihao Ren¹

¹Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.

European radiology

|October 18, 2023

概括

这项研究开发了一个深度学习 (DL) 模型来预测脑膜瘤瘤等级和病理标记物的表达. DL模型对改善术前诊断和指导脑膜瘤患者的治疗决策充满希望.

关键词:

深度学习是一种深度学习.磁共振成像技术磁共振成像技术阴道瘤是发生在阴道上的人.无线电学 (Radiomics) 是一种放射学.

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科学领域:

神经瘤学神经瘤学
医疗成像医学成像
人工智能的人工智能

背景情况:

脑膜瘤是最常见的初级内瘤.
准确的手术前分类和病理标志物的预测对于治疗规划和患者管理至关重要.

研究的目的:

建立一个深度学习 (DL) 模型来预测瘤等级和脑膜瘤病理标志物的表达.
在内部和外部验证队列中评估DL模型的预测性能.

主要方法:

对来自两个机构的1192名脑膜瘤患者的回顾性分析.
利用基于转移学习的微调ResNet50模型进行分类和预测.
外部验证是使用来自另一个机构的数据进行的.

主要成果:

在内部测试组中,DL模型实现了WHO等级和病理标记 (Ki-67指数,H3K27me3,PR状态) 的高预测性能.
该模型在外部验证队列中显示了中等的性能,表明了概括的潜力.
世卫组织等级预测曲线下的面积 (AUC) 为0.966 (内部) 和0.669 (外部).

结论:

深度学习模型可以有效地预测脑膜瘤等级和病理标志物表达在手术前.
这种方法可以帮助识别高风险患者,并为手术和后续策略提供信息.
使用DL进行术前预测,有利于在脑膜瘤管理中的临床决策.