Pattern Classification for Ovarian Tumors by Integration of Radiomics and Deep Learning Features

Background: Ovarian tumor is a common female genital tumor, among which malignant tumors have a poor prognosis. The survival rate of 70% of patients with ovarian cancer is less than 5 years, while benign ovarian tumor is better, so the early diagnosis of ovarian cancer is important for the treatment and prognosis of patients. Objectives: Our aim is to establish a classification model for ovarian tumors. Methods: We extracted radiomics and deep learning features from patients’CT images. The four-step feature selection algorithm proposed in this paper was used to obtain the optimal combination of features, then, a classification model was developed by combining those selected features and support vector machine. The receiver operating characteristic curve and an area under the curve (AUC) analysis were used to evaluate the performance of the classification model in both the training and test cohort. Results: The classification model, which combined radiomics features with deep learning features, demonstrated better classification performance with respect to the radiomics features model alone in training cohort (AUC 0.9289 vs. 0.8804, P < 0.0001, accuracy 0.8970 vs. 0.7993, P < 0.0001), and significantly improve the performance in the test cohort (AUC 0.9089 vs. 0.8446, P = 0.001, accuracy 0.8296 vs. 0.7259, P < 0.0001). Conclusion: The experiments showed that deep learning features play an active role in the construction of classification model, and the proposed classification model achieved excellent classification performance, which can potentially become a new auxiliary diagnostic tool.