The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using FDG PET/CT examinations

Abstract

Abstract Objective To create the 3D convolutional neural network (CNN)-based system that can use whole-body FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). Methods This study 1224 image sets from OC patients who underwent whole-body FDG PET/CT at Kowsar hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists’ interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. Results This study included 37 women (mean age, 56.3 years; age range, 36–83 years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging. Conclusions The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.