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从生物学上可解释的深度学习衍生的MRI表型揭示了淋巴结参与和新辅助疗法响应在肝脏内胆固醇癌

Wentao Wang^1,2, Siqi Yin^3,4, Qianhui Xu^5,6,7

¹Department of Radiology, Cancer center, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Hepatology (Baltimore, Md.)

|January 12, 2026

概括

一个新的AI模型SwinU-CliRad准确地预测了淋巴结参与肝脏内胆管癌 (iCCA) 的情况. 该工具有助于选择手术候选人,并确定可能从新辅助疗法 (NAT) 中受益的患者.

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:

准确的淋巴结 (LN) 阶段对肝内胆管癌 (iCCA) 是至关重要的,但具有挑战性的非侵入性方法.
目前的非侵入性方法往往难以精确地分层LN参与,影响治疗决策.

研究的目的:

开发和验证一种新型的计算模型,以提升淋巴结 (LN) 风险分层在肝脏内胆固醇癌 (iCCA) 中.
评估该模型的潜在治疗影响,特别是在接受新辅助疗法 (NAT) 的患者中.

主要方法:

开发了一个SwinU-CliRad框架,将MRI中的Swin UNEt TRansformers (SwinU) 与临床放射学数据用于LN评估.
该模型在发现,内部和外部多中心队列中进行了训练和验证,在新辅助疗法 (NAT) 队列中进行了进一步评估.
探索人工智能衍生的输出与瘤多组体特征之间的相关性,包括单细胞RNA测序.

主要成果:

在SwinU-CliRad模型中,在多个队列中的LN风险分层中,SwinU-CliRad模型表现出高准确性 (AUC 0.932-0.888).
该模型在纠正错误分类方面表现优于放射科医生的评估.
在NAT队列中,模型识别的高风险患者表现出改善的病理反应,包括更高的完全和主要病理反应率.
人工智能衍生的LN状态与特定的基因突变 (KRAS),蛋白质表达 (MUC5AC) 和组织学亚型相关,将LN参与与免疫抑制瘤微环境联系起来.

结论:

斯温U-CliRad模型为iCCA中的LN风险分层提供了一个生物学上可解释的工具.
它可以帮助识别可能受益于新辅助疗法 (NAT) 的手术候选人和患者,从而有可能改善治疗结果.

关键词:

生物标志物生物标志物深度学习 (Deep Learning) 是一种深度学习.肝脏内胆性胆性癌淋巴结是一个淋巴结.磁共振成像技术磁共振成像技术

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