A novel classification method of lymph node metastasis in colorectal cancer

Abstract

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.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer

“…The lymph node metastasis of CRC is highly correlated with postoperative cancer recurrence and survival time [ 27 ]. Therefore, it is of great significance to find biomarkers that can predict early CRC lymph node metastasis.…”

Clinical significance of securin expression in solid cancers: A PRISMA-compliant meta-analysis of published studies and bioinformatics analysis based on TCGA dataset

“…It has been well established that tumor metastasis is an adverse event that severely affects the prognosis of patients with cancers. [ 29 , 30 ] Given that, herein we also systematically assessed the association between securin expression and LNM through combining the data of the current literatures.…”

“…Therefore, we performed a meta-analysis of the current literature to systematically assess whether securin can be used as a prognostic biomarker in pan-cancer. Tumor metastasis is a key event that directly affects survival outcomes in cancer patients [ 29 , 30 ] ; therefore, the association of securin with metastasis has attracted much attention as well. To date, a convincing conclusion regarding the relationship between securin expression and metastasis has not yet been made.…”

Applications of Deep Learning for Drug Discovery Systems with BigData

“…For example, one of the reasons why deep learning has become so popular is that a computer figures out "what kind of data should be prepared for computer learning, feature design", instead of a data analyst. Conversely, deep learning is designed to automatically obtain useful feature values for prediction because it repeats the process of combining input feature values to create new feature values [56][57][58]. Such features are particularly useful when one is trying to complete a task in a data-driven manner without relying on known information or when large numbers of feature values in the input data make it difficult to determine which feature values are important for prediction.…”