College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China.
Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province, China.
Bioengineered. 2021 Dec;12(1):2007-2021. doi: 10.1080/21655979.2021.1930333.
Colorectal cancer lymph node metastasis, which is highly associated with the patient's cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient's cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model.
结直肠癌淋巴结转移与患者癌症复发和生存率密切相关，一直是许多治疗策略的重点，这些策略与患者癌症复发和生存率密切相关。然而，神经网络分类淋巴结转移的流行方法存在局限性，因为可用的低级特征不足以进行分类，并且放射科医生无法快速查看图像。识别结直肠癌中的淋巴结转移是治疗结直肠癌患者的关键因素。在本工作中，提出了一种基于深度迁移学习的自动分类方法。具体来说，该方法解决了低级特征重复的问题，并将这些特征与高级特征结合到新的特征图中进行分类；以及一个合并层，将所有从前一层传输的特征合并到第一个全连接层的映射中。在从哈尔滨医科大学肿瘤医院收集的数据集上进行实验，涉及 3364 名患者的样本。在这些样本中，1646 个为阳性，1718 个为阴性。实验结果表明，敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）分别为 0.8732、0.8746、0.8746 和 0.8728，准确性和 AUC 分别为 0.8358 和 0.8569，表明我们的方法在不增加模型深度和宽度的情况下，显著优于以前的结直肠癌淋巴结转移分类方法。
