基于多模式的渐进融合变压器模型,用于预测肝细胞癌患者的免疫治疗反应
Lushan Xiao1, Jiaren Wang1, Hao Cui1
1Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Journal of advanced research
|February 11, 2026
概括
这项研究开发了一种基于变压器的模型 (GIFT-CIP),用于预测肝细胞癌 (HCC) 患者的免疫治疗反应. 该GIFT-CIP模型准确预测患者的结果,有助于个性化治疗策略的HCC.
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:
- 免疫疗法显著改善肝细胞癌 (HCC) 患者的生存率.
- 预测免疫疗法反应对于优化HCC治疗策略至关重要.
- 这项研究的重点是开发HCC免疫治疗反应的预测模型.
研究的目的:
- 开发和验证基于多模式变压器的模型,用于预测HCC的免疫治疗反应.
- 评估这些模型在不同数据模式中的性能和概括能力.
主要方法:
- 五个医疗中心的HCC患者的回顾性纳入.
- 使用最小绝对收缩和选择操作员 (LASSO) 进行临床特征选择.
- 使用临床数据和CT成像特征 (内和周贴片) 训练了基于多模渐进融合变压器 (GIFT-CIP) 的模型.
- 在内部和独立的外部测试队列上验证的模型.
主要成果:
- 结合临床,内和周成像数据的GIFT-CIP模型实现了高预测性能,AUC值在0.883到0.926之间.
- 该模型有效地将患者分为低风险和高风险组,在外部队列中显示出无进展和整体存活率 (p < 0.01) 的显著差异.
- 在不同的模式和队伍中表现出强烈的概括性.
结论:
- GIFT-CIP模型提供了一种非侵入性方法,用于预测HCC患者的免疫治疗反应.
- 这个模型可以帮助临床医生指导监测和选择最佳的免疫疗法策略.
- 突出了多模式人工智能模型在精密瘤学的潜力.
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