Biologically interpretable deep learning–derived MRI phenotypes reveal lymph node involvement and neoadjuvant therapy response in intrahepatic cholangiocarcinoma

肝内胆管癌 医学 危险分层 淋巴结 肿瘤科 新辅助治疗 磁共振成像 内科学 接收机工作特性 放射科 病理 完全响应 多参数磁共振成像 表型 淋巴 文本挖掘
作者
Wentao Wang,Siqi Yin,Q Xu,Danjun Song,Danlan Lian,Jinjun Wang,Xiangge Guo,Danjiang Huang,Jiayi Xing,Lei Wu,Xuping Mao,Wei Sun,Ruoyu Shi,Qiang Gao,Kai Zhu,Manning Wang,Liangqing Dong,Si-Si Rao
出处
期刊:Hepatology [Lippincott Williams & Wilkins]
被引量:1
标识
DOI:10.1097/hep.0000000000001671
摘要

BACKGROUND AND AIMS: Accurate N-staging of intrahepatic cholangiocarcinoma (iCCA) remains challenging using noninvasive approaches. We aimed to develop a model to refine lymph node (LN) involvement stratification and inform therapeutic consideration. APPROACH AND RESULTS: This study enrolled a discovery cohort (n=682), an internal test cohort from the FU-iCCA (n=204), and an external multicenter cohort (n=88) for model development, and a neoadjuvant therapy (NAT) cohort (n=145) for therapeutic evaluation of the model. A SwinU-CliRad framework was constructed by integrating Swin UNEt TRansformers (Swin UNETR)-based magnetic resonance imaging-derived outputs of LN involvement with clinicoradiological features. Correlations between SwinU outputs and tumor multi-omics profiles were explored. The SwinU-CliRad model achieved areas under the curve (AUCs) of 0.932, 0.867, and 0.888 in LN risk stratification, and outperformed radiologist-based assessments by correcting more misclassifications than it introduced across the discovery, internal, and external test cohorts (18.8% vs. 7.3%, 18.1% vs. 4.9%, and 17.0% vs. 5.7%), respectively. In the NAT cohort, patients classified as high LN-involved risk by the SwinU-CliRad exhibited lower residual viable tumor rates than those with low LN-involved risk, with higher rates of pathological complete response (12.0% vs. 4.2%) and major pathological response (14.0% vs. 8.4%). SwinU outputs were associated with KRAS mutations, MUC5AC overexpression, and the large-duct histological subtype. Single-cell RNA sequencing analysis linked LN involvement to an immune-suppressive stroma tumor microenvironment. CONCLUSIONS: The SwinU-CliRad model can serve as a biologically interpretable tool for LN risk stratification in iCCA surgical candidates, with high-risk patients identified by the model potentially deriving benefit from NAT.
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