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预治疗多序对比增强型MRI用于预测无法切除的肝细胞癌中对免疫治疗的反应,使用变压器:一个多中心研究

Jialin Chen^1,2, Juan Chen², Yamei Ye³

¹Department of Oncology, Fuzhou General Teaching Hospital of Fujian University of Traditional Chinese Medicine, 350001, Fuzhou, Fujian, PR China.

Journal of Cancer

|June 19, 2025

概括

一个新的基于变压器的放射学模型准确地预测了在不可切除的肝细胞癌 (uHCC) 中针对性联合免疫疗法的反应. 这种人工智能工具有助于选择可能受益于治疗的患者,改善临床决策和患者的治疗结果.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

向组合免疫疗法 (TCI) 在不可切除的肝细胞癌 (uHCC) 中显示出有限的疗效.
预测患者对TCI的反应对于优化治疗策略至关重要.

研究的目的:

开发和验证基于变压器的放射学模型,用于预测HHCC患者对TCI的客观反应率 (ORR).
评估预测模型的临床适用性和生存影响.

主要方法:

这是一项多中心的回顾性研究,对264名HuhCC患者进行了多中心的回顾性研究,这些患者在TCI之前接受了对比增强型MRI.
在多序MRI上使用ResNet50进行特征提取,然后进行ORR预测的变压器模型训练.
使用ROC/DCA曲线的模型性能评估和通过卡普兰-梅尔曲线的生存分析;用Grad-CAM和SHAP可视化.

主要成果:

基于变压器的放射学模型实现了高预测准确性 (AUC=1000在训练中,0.929在验证中).
客观响应者在培训和验证队伍中表现出明显更好的整体存活率 (OS).
该模型证实了临床适用性,有助于在接受TCI的HHCC患者的治疗决策.

结论:

结合ResNet50和Transformer的深度学习框架有效地预测了HhCC中TCI的有效性.
这种由人工智能驱动的方法为HhCC的个性化治疗策略提供了有价值的指导.
该模型能够预测反应和生存影响的能力增强了TCI的临床决策.

关键词:

这就是为什么MRI是MRI.深度学习是一种深度学习.肝细胞癌是肝细胞癌.免疫疗法免疫疗法预测模型是一个预测模型.

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