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利用深度学习优化诱导化疗在鼻癌的选择

Zi-Hang Chen¹, Xu Han², Li Lin¹

¹Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|July 30, 2025

概括

这项研究开发了深度学习模型,以预测局部区域高级鼻癌 (LA-NPC) 的诱导化疗 (IC) 反应. 人工智能工具使用MRI数据指导个性化化疗选择,改善治疗选择.

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:

局部先进的鼻癌 (LA-NPC) 缺乏个性化诱导化疗 (IC) 治疗方案指导.
目前的IC疗法包括TPF (多克塞尔+西斯普拉丁+5-Fu) 和GP (凝丁+西斯普拉丁).

研究的目的:

开发深度学习模型来预测LA-NPC患者的IC反应.
创建一个人工智能驱动的工具,用于个性化的IC疗程选择.

主要方法:

利用两个中心的1438名LA-NPC患者的预治疗MRI扫描.
开发了放射学和图形卷积网络 (GCN) 模型,使用MRI数据来预测完整的生物反应 (cBR).
将临床参数与MRI数据集成,以构建增强的MRI-临床模型.

主要成果:

磁力共振成像和磁力共振成像-临床模型在TPF和GP队列中都显示出cBR预测的高分辨准确性 (AUC从0.764到0.838).
核磁共振-临床模型有效地分层患者风险,与高敏感性组中改善的3年无病生存率相关.
基于MRI-Clinical模型开发了一个在线工具,以指导个性化的IC疗程选择.

结论:

基于放射学和GCN的AI模型为LA-NPC中的IC响应提供了强大的预测性能.
开发的工具显示了在LA-NPC中指导个性化治疗决策的潜力.

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鼻癌是指鼻癌.个性化的精密处理.

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