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一种多模态深度学习方法,用于预测鼻癌患者适应性辐射治疗的资格

Zhichun Li¹, Zihan Li¹, Sai Kit Lam^2,3

¹Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.

|July 29, 2025

概括

这项研究开发了一种深度学习模型,用于预测鼻癌 (NPC) 患者适应性辐射治疗 (ART) 的资格. 人工智能模型准确地识别候选人,提高治疗效率和个性化.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

适应性辐射疗法 (ART) 可以改善鼻癌 (NPC) 的预后.
由于解剖学变异性,患者选择ART具有挑战性,影响治疗时间和放射科医生的工作量.

研究的目的:

使用分类神经网络,在放射治疗 (RT) 之前预测NPC患者的符合条件的ART候选人.
利用合并的医学成像和临床数据来有效地选择ART候选人.

主要方法:

分析了305名接受RT的NPC患者的回顾性数据.
开发了一个多模分类神经网络,结合ResNet-50,交叉注意力,多尺度特征和临床数据.
患者根据ART重新规划状态进行分类.

主要成果:

拟议的多模式深度预测模型实现了0.9070.0的AUC.
该模型在预测ART资格方面,与其他深度学习网络相比,表现优越.
实现了对NPC患者ART资格的准确分类和预测.

结论:

开发的方法在预测NPC患者的ART资格方面表现强.
这种方法有可能提高临床决策和优化治疗效率.
这些发现支持通过人工智能驱动的预测来推进个性化癌症护理.

关键词:

适应性辐射疗法适应性辐射疗法卷积神经网络是一种卷积神经网络.医学图像分类医学图像分类鼻癌 (nasopharyngeal carcinoma) 是一种发生在鼻的癌症.视觉变压器视觉变压器

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