Decision-Level Fusion Classification of Ovarian CT Benign and Malignant Tumors Based on Radiomics and Deep Learning of Dual Views

Abstract

Ovarian cancer is one of the most prevalent malignant tumors in the female reproductive system, and its early diagnosis has always posed a challenge. Computed tomography (CT) is a widely utilized clinical management tool that can extract much detail through computer algorithms, playing a vital role in the early diagnosis of ovarian cancer. This research aims to develop an ovarian benign-malignant classification model based on radiomics and deep learning of dual views. A retrospective analysis of CT images from 135 ovarian tumor patients was conducted using the StratifiedKFold method (K =5) for cross-validation. Radiomics features were extracted from CT data and inputted into an automated machine learning (A-ML) framework. Meanwhile, the deep learning (DL) model called Dual-View Global Representation and Local Cross Transformer (D_GR_LCT) was proposed for ovarian tumor classification using a global-local parallel analysis approach for end-to-end training. Radiomics results indicate the superiority of 3D input over 2D, with an average AUC-ROC of 88.35% and an average AUC-PR of 88.73%. Comparative experiments demonstrate enhanced model performance with parameter settings. The DL model achieves an average AUC-ROC and AUC-PR of 88.15% and 85.17%, respectively, validated by ablative and comparative experiments. At the decision-making level, the fusion of radiomics and DL models demonstrates an average AUC-ROC and AUC-PR of 91.35% and 90.20%, respectively, utilizing the stacking method. The fusion model outperformed individual models. Thus, models based on radiomics and dual-view DL are recommended for early identification and screening of ovarian cancer in clinical practice.

Topics & Concepts