School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Comput Methods Programs Biomed. 2024 Jun;249:108159. doi: 10.1016/j.cmpb.2024.108159. Epub 2024 Apr 2.
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients.
We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction.
The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework.
Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.
结直肠癌（CRC）是全球最常见的癌症之一。对 CRC 患者进行准确的生存预测对制定治疗策略具有重要意义。最近，机器学习和深度学习方法已越来越多地应用于癌症生存预测。然而，大多数现有方法不能充分表示和利用特征之间的依赖性，也不能充分挖掘和利用 CRC 的合并症模式。为了解决这些问题，我们提出了一种基于自注意力的图学习（SAGL）框架，以提高 CRC 患者术后癌症特异性生存预测的准确性。
我们提出了一种构建依赖图（DG）的新方法，以反映合并症-合并症依赖性和与患者特征和癌症治疗相关的特征之间的依赖性这两种类型的依赖性。然后，通过疾病合并症网络对该图进行细化，该网络提供了 CRC 合并症模式的整体视图。提出了一种 DG 引导的自注意力机制，以发现 DG 提供的新的依赖性，从而增强 CRC 生存预测。最后，将为每个患者构建表示，并用这些表示进行生存预测。
实验结果表明，SAGL 在真实数据集上优于最先进的方法，3 年和 5 年生存预测的接收者操作特征曲线分别达到 0.849±0.002 和 0.895±0.005。此外，与不同基于图神经网络的变体的比较结果表明了我们的 DG 引导的自注意力图学习框架的优势。
我们的研究表明，DG 引导的自注意力在优化特征图学习方面具有潜力，可以提高 CRC 生存预测的性能。
