用多模态影像与注意力机制提升头颈癌生存预测准确率
Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion
- 结合CT/PET影像，用CBAM模块提取关键特征
- 在两个数据集上优于传统统计与机器学习模型
- 适合临床个性化治疗决策支持场景
头颈癌（HNC）的精准生存预测对指导临床决策和优化治疗策略至关重要。传统模型如Cox比例风险模型在处理复杂多模态数据方面能力有限。本文提出一种基于深度学习的方法，利用CT和PET影像模态预测HNC患者的生存结局。方法融合特征提取与卷积块注意力模块（CBAM），并通过多模态数据融合层生成紧凑的特征表示。最终采用全参数离散时间生存模型进行预测，可灵活建模风险函数，克服传统生存模型局限。在HECKTOR和HEAD-NECK-RADIOMICS-HN1数据集上评估，结果表明该深度学习模型显著优于传统统计与机器学习模型，提升了生存预测准确性，为头颈癌个性化治疗规划提供有力工具。
原文摘要 · Abstract (English)
Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in their ability to handle complex multi-modal data. This paper proposes a deep learning-based approach leveraging CT and PET imaging modalities to predict survival outcomes in HNC patients. Our method integrates feature extraction with a Convolutional Block Attention Module (CBAM) and a multi-modal data fusion layer that combines imaging data to generate a compact feature representation. The final prediction is achieved through a fully parametric discrete-time survival model, allowing for flexible hazard functions that overcome the limitations of traditional survival models. We evaluated our approach using the HECKTOR and HEAD-NECK-RADIOMICS- HN1 datasets, demonstrating its superior performance compared to conconventional statistical and machine learning models. The results indicate that our deep learning model significantly improves survival prediction accuracy, offering a robust tool for personalized treatment planning in HNC
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