用可解释AI融合影像与临床数据，自动预测头颈癌生存率
Automated and Interpretable Survival Analysis from Multimodal Data
- 用深度学习提取影像和临床特征，结合符号表达建模
- 在RADCURE数据集上预测与分层的C-index分别达0.838和0.826
- 结果可解释，符合已知预后因子，适合临床信任与验证
准确且可解释的生存分析仍是肿瘤学的核心挑战。随着多模态数据增长及临床对透明模型的需求，这一挑战愈发复杂。我们提出一种可解释的多模态AI框架MultiFIX，通过整合临床变量与计算机断层扫描影像，实现生存分析的自动化。该框架利用深度学习提取生存相关特征，并进一步解释：影像特征通过Grad-CAM可视化，临床变量则通过遗传编程建模为符号表达式。风险评估采用透明的Cox回归，实现不同生存结局的分组。在头颈癌公开数据集RADCURE上，MultiFIX的预测和分层C-index分别为0.838和0.826，优于临床与学术基线方法，且与已知预后标志物一致。结果表明，可解释多模态AI在精准肿瘤学中具有巨大潜力。
原文摘要 · Abstract (English)
Accurate and interpretable survival analysis remains a core challenge in oncology. With growing multimodal data and the clinical need for transparent models to support validation and trust, this challenge increases in complexity. We propose an interpretable multimodal AI framework to automate survival analysis by integrating clinical variables and computed tomography imaging. Our MultiFIX-based framework uses deep learning to infer survival-relevant features that are further explained: imaging features are interpreted via Grad-CAM, while clinical variables are modeled as symbolic expressions through genetic programming. Risk estimation employs a transparent Cox regression, enabling stratification into groups with distinct survival outcomes. Using the open-source RADCURE dataset for head and neck cancer, MultiFIX achieves a C-index of 0.838 (prediction) and 0.826 (stratification), outperforming the clinical and academic baseline approaches and aligning with known prognostic markers. These results highlight the promise of interpretable multimodal AI for precision oncology with MultiFIX.
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