MR imaging omics is an emerging method that uses data representation algorithms to transform image data into high-dimensional digable feature spaces that capture different tumor phenotypic differences and may have predictive prognostic capabilities. In this paper, the MR imaging omics
method is used to construct a predefined image omics quantitative feature database to quantitatively describe tumor heterogeneity. Combining traditional machine learning models, constructing image omics tags to create a scientific, quantitative and easy to use Prognostic analysis model for
rectal cancer can predict and analyze the prognosis of patients with rectal cancer before surgery. The improved neural network-based method proposed in this paper does not require manual selection of parameters, and only a large amount of training data can train accurate prediction models.
In addition, this paper also introduces the method of extracting lymph node metastasis parameters from MR image rectal cancer area, and the strategy for data missing value completion. Experiments show that the MR imaging of regional rectal cancer regional lymph node metastasis prediction model
based on improved deep neural network is better than formula prediction method and traditional artificial neural network based model, which reduces the prediction error. Further analysis also shows the missing value completion proposed in this paper. The method can effectively strengthen the
training of deep neural networks.
Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer
