Radiomics Analysis Combining Unsupervised Learning and Handcrafted Features: A Multiple-Disease Transfer Study

Abstract

Background: To investigate the performance of incorporating features from unsupervised learning and handcrafted methods for radiomics study in clinical multiple-disease identification. Method: In this retrospective study, we collected a total of 666 patients with four different types of tumor and four heterogeneous clinical end points (pancreatic neuroendocrine tumors (pNETs) with different pathological grades, high-grade osteosarcoma (HOS) with different 5-year survival status, intrahepatic cholangiocarcinoma (ICC) with different early recurrence results and gastric cancer (GC) with different lymph node (LN) status). A total of 67 handcrafted features and 900 sparse autoencoder (SAE) features were extracted by handcrafted and SAE method respectively. Prediction models developed by handcrafted, SAE and hybrid features respectively. Pearson correlation method was performed to assess the correlation between handcrafted features and SAE features. Findings: In the four clinical tasks, the SAE model yielded a better prediction performance than the handcrafted model in identifying the pNETs, HOS and ICC datasets. In GC dataset, the handcrafted model showed a higher performance than the SAE model. The predictive capability of the hybrid model was superior to the SAE and handcrafted models in all four datasets. Correlation analysis showed that only 12.75% (pNETs), 7.11% (HOS), 14.78% (ICC), and 12.79% (GC) features had significant correlation for each disease. Interpretation: This study demonstrated SAE features had potential as radiomics features which could fused with handcrafted features to improve the performance of model in multiple-disease. Funding:This work was supported by the National Key R&D Program of China (2018YFE0114800), Natural Science Foundation of China (NSFC Grant No. 81871351, 81950410632), Zhejiang Provincial Key Projects of Technology Research (WKJ-ZJ-2033). Declaration of Interests: The authors declare no competing financial interests. Ethics Approval Statement: This study was approved by the institutional review boards of all participant centers and the requirement of informed consent was waived.