基于MRI的放射学与自动细分和手动精细化,用于预测不可切除的肝细胞癌的治疗反应:一个多中心研究
Yanyan Yu1, XingQing Qin1, Yuanfang Tao1
1First Affiliated Hospital of GuangXi Medical University, Nanning, China.
Abdominal radiology (New York)
|January 20, 2026
概括
结合多参数MRI放射学模型的自动和手动细分的混合方法显著改善了不可切除的肝细胞癌 (HCC) 治疗反应的预测. 这种混合方法提供了准确性和效率之间的平衡.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医疗成像医学成像
背景情况:
- 肝细胞癌 (HCC) 是一个主要的全球健康问题.
- 在不可切除的HCC中预测治疗反应对于患者管理至关重要.
- 基于多参数MRI的放射学为非侵入性治疗反应预测提供了潜力.
研究的目的:
- 评估不同细分策略对放射学模型性能的影响,以预测不可切割的HCC的治疗反应.
- 为了比较全自动,手动和混合细分方法.
主要方法:
- 一项多中心回顾性研究,涉及334名无法切除的HCC患者.
- 有趣的瘤区域 (ROI) 用自动深度学习 (DeepLabV3-ResNet50) 进行细分,并以手动方式进行细化.
- 开发了三种放射学模型:全自动 (Auto),手动 (Manual) 和混合 (Auto + Refine).
- 模型在多家医院接受了培训和验证.
主要成果:
- 混合 (Auto + Refine) 细分模型实现了预测治疗反应的曲线下面积 (AUC) 值的最高值.
- 混合模型的AUC为0.885 (测试组) 和0.893 (外部验证).
- 手动细分也表现良好,超过了完全自动化的模型.
结论:
- 混合细分策略 (Auto + Refine) 与纯手动或完全自动化的方法相比,在不可切割的HCC中预测治疗反应方面表现优越.
- 这种混合方法代表了首选的工作流程,平衡了注释负担和预测准确性.
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