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基于变压器的深度学习用于使用磁共振成像来预测脑瘤复发

Qiuyu Zhou¹, Xuwei Tian², Meiling Feng³

¹School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Medical physics

|September 25, 2025

概括

一个基于变压器的新型深度学习模型使用MRI和放射治疗数据准确地预测大脑瘤复发. 这种人工智能工具的性能优于现有的模型,为个性化放射治疗治疗策略提供了潜力.

科学领域:

人工智能的人工智能
医疗成像医学成像
在瘤学瘤学.

背景情况:

深度学习 (DL) 模型,特别是变形机,在分析复杂的医学成像数据以预测脑瘤时显示出有前途.
然而,它们在预测治疗后复发的有效性仍未得到充分证明.

研究的目的:

使用多模式数据 (治疗前MRI和放射治疗剂量) 开发和验证基于变压器的DL模型.
预测治疗后脑瘤复发,以支持个性化放射治疗决策.

主要方法:

训练并验证了基于变压器的DL模型,用于接受玛刀放射手术的脑转移患者的MRI数据.
将该模型与九个已建立的预后模型进行比较,并验证了其在临床子组中的概括性.
利用后勤回归和统计分析来确认预测的独立性.

主要成果:

变压器模型实现了0.817的AUROC,超过了所有其他模型.
在各个年龄和性别子组中表现出强烈的概括性.
后勤回归证实模型的预测是独立的和高度显著的 (p < 0.001).

结论:

开发的基于变压器的DL模型是预测放射治疗后脑瘤复发的可靠预后工具.
它显著优于现有模型,表明了指导个性化治疗策略的潜力.
这种人工智能方法可以增强接受放射治疗的大脑瘤患者的决策能力.

关键词:

大脑瘤大脑瘤深度学习是一种深度学习.预后预后预后

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