Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features

Abstract Purpose The FIGO stage is critical to guiding the treatments of OC. We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients’ pretreatment clinical, PET/CT metabolic, and radiomics features. (2) Methods: We enrolled OC patients who underwent PET/CT scans and divided them into two cohorts according to their FIGO stage. Then we manually delineated the VOI and calculated PET metabolic features. Other radiomics PET/CT features were extracted by Python. We developed 11 prediction models to predict stages based on four groups of features and conducted three experiments to verify the meaning of PET/CT features. We also redesigned experiments to demonstrate the stage prediction performance in OCCC and MCOC. (3) Results: 183 OC patients were enrolled in this study, and we obtained 137 features from four groups of data. The best model was an adaptive ensemble with an AUC value of 0.819. Our proposed models presented the best result of 0.808 in terms of AUC in OCCC and MCOC patients’ groups. (4) Conclusion: Through AI algorithms, the PET/CT metabolic and radiomics features combined with clinical features could improve the accuracy of staging prediction.