IEEE J Biomed Health Inform. 2021 Oct;25(10):3933-3942. doi: 10.1109/JBHI.2021.3087634. Epub 2021 Oct 5.
Gastric cancer (GC) is the third leading cause of cancer-associated deaths globally. Accurate risk prediction of the overall survival (OS) for GC patients shows significant prognostic value, which helps identify and classify patients into different risk groups to benefit from personalized treatment. Many methods based on machine learning algorithms have been widely explored to predict the risk of OS. However, the accuracy of risk prediction has been limited and remains a challenge with existing methods. Few studies have proposed a framework and pay attention to the low-level and high-level features separately for the risk prediction of OS based on computed tomography images of GC patients. To achieve high accuracy, we propose a multi-focus fusion convolutional neural network. The network focuses on low-level and high-level features, where a subnet to focus on lower-level features and the other enhanced subnet with lateral connection to focus on higher-level semantic features. Three independent datasets of 640 GC patients are used to assess our method. Our proposed network is evaluated by metrics of the concordance index and hazard ratio. Our network outperforms state-of-the-art methods with the highest concordance index and hazard ratio in independent validation and test sets. Our results prove that our architecture can unify the separate low-level and high-level features into a single framework, and can be a powerful method for accurate risk prediction of OS.
胃癌（GC）是全球癌症相关死亡的第三大主要原因。准确预测 GC 患者的总生存期（OS）风险具有重要的预后价值，有助于识别和分类患者为不同的风险组，以从个性化治疗中受益。已经广泛探索了许多基于机器学习算法的方法来预测 OS 风险。然而，现有的方法在风险预测的准确性方面受到限制，仍然是一个挑战。很少有研究提出了一个框架，并分别关注基于 GC 患者 CT 图像的 OS 风险预测的低水平和高水平特征。为了实现高精度，我们提出了一种多焦点融合卷积神经网络。该网络专注于低水平和高水平特征，其中一个子网专注于较低水平的特征，另一个增强子网具有横向连接，专注于更高水平的语义特征。使用三个独立的 640 名 GC 患者数据集来评估我们的方法。我们的网络通过一致性指数和风险比的指标进行评估。在独立验证集和测试集中，我们的网络在一致性指数和风险比方面优于最先进的方法。我们的结果证明了我们的架构可以将单独的低水平和高水平特征统一到一个单一的框架中，并且可以成为 OS 准确风险预测的强大方法。
