Feasibility of predicting pancreatic neuroendocrine tumor grade using deep features from unsupervised learning

This paper aimed to investigate if deep image features extracted via sparse autoencoder (SAE) could be used to preoperatively predict histologic grade in pancreatic neuroendocrine tumors (pNETs). In this study, a total of 114 patients from two institutions were involved. The deep image features were extracted based on the sparse autoencoder network via a 2000-time iteration. Considering the possible prediction error due to the small patient data size, we performed 10-fold cross-validation. To find the optimal hidden size, we set the size as a range of 6-16. The maximum relevance minimum redundancy (mRMR) features selection algorithm was used to select the most histologic grade-related image features. Then the radiomics signature was generated by using the selected features with Support Vector Machine (SVM), Multivariable Logistic Regression (MLR) and Artificial Neural Networks (ANN) methods. The prediction performance was evaluated using AUC value.