将肝癌的肝功能与肿瘤因素分离建模，提升生存预测准确性与可解释性。
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
- 采用生物监督的专家混合架构，显式分解肝功能与肿瘤因素
- 12/18/24个月生存预测AUC分别达75.33%/75.85%/73.96%，优于基线
- 可识别治疗相关生存差异，且无监督下嵌入向量与临床指标显著相关
肝细胞癌（HCC）具有复杂的生物学异质性，由肝功能储备与肿瘤相关因素共同决定；相似的生存结果可能反映根本不同的生物学过程。现有视觉语言模型通过单一纠缠表示融合肝与肿瘤因素，限制了预测准确性和生物学可解释性。本文提出BioFact-MoE，一种基于生物监督专家的混合专家框架，在残差MoE生存架构中显式分解肝功能与肿瘤因素。在包含588名患者的队列上（基于4,582组3D MRI图像-报告对预训练），该模型在多个时间窗内持续优于所有基线，12、18、24个月的AUC分别为75.33%、75.85%、73.96%。除风险评分外，专家门控权重支持表型感知分层。通路引导的门控揭示了临床相关的治疗相关生存异质性。在独立验证中，肝功能与肿瘤嵌入分别与肝功能和肿瘤负荷标志物显著关联（p<0.05），且无需监督。代码已开源。
原文摘要 · Abstract (English)
Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival outcomes may reflect fundamentally different underlying biological processes. Prognostic modeling in HCC is informed by rich multimodal information from multiparametric MRI and radiology reports from routine clinical practice. Existing prognostic vision-language models (VLMs) learn a single entangled latent representation that blends hepatic and tumor-related factors, limiting both accuracy and biological interpretability. We present BioFact-MoE, a biologically factorized Mixture of Experts (MoE) framework that explicitly decomposes liver and tumor factors via biologically supervised experts within a residual MoE survival architecture. On a HCC cohort of N=588 patients (pretrained on 4,582 3D MRI image-report pairs), BioFact-MoE consistently improves survival prediction over all baselines across time horizons, achieving 12-, 18-, and 24-month AUCs of 75.33%, 75.85%, and 73.96%. Beyond scalar risk prediction, gated expert weights enable phenotype-aware risk stratification. Pathway-informed gating uncovers clinically meaningful treatment-associated survival heterogeneity. In held-out validation, hepatic and tumor embeddings show selective associations with liver function and tumor burden markers, respectively (p<0.05), without supervision. The code is available at https://github.com/jy-639/BioFact-MoE.
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