Intra- and peritumoral radiomics for predicting early recurrence in patients with high-grade serous ovarian cancer.

Abstract

To explore values of intra- and peritumoral CT-based radiomics for predicting recurrence in high-grade serous ovarian cancer (HGSOC) patients. This study enrolled 110 HGSOC patients from our hospital between Aug 2017 and Apr 2021. All patients underwent contrast-enhanced CT scans before treatment. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features from intra- and peritumoral areas. Radiomics signatures were built based on selected features from Intra-RS, Peri-RS, and in Com-RS. A nomogram was constructed by combining radiomics signatures and clinical parameters with predictive potential. Receiver operating characteristics (ROC), calibration, and decision curve analyses (DCA) curves were used to evaluate performance of the nomogram. The intra- and peritumoral combined Com-RS showed effective ability in predicting recurrent HGSOC in the training (AUCs, Intra-RS vs. Peri-RS vs. Com-RS, 0.861 vs. 0.836 vs. 899) and validation (AUCs, Intra-RS vs. Peri-RS vs. Com-RS, 0.788 vs. 0.762 vs. 815) cohort. The Federation of International of FIGO stage, menstruation, and location were found to be strongly associated with tumor recurrence. The nomogram has the best predictive ability in the training (AUCs, Com-RS vs. clinical model vs. nomogram, 0.899 vs. 0.648 vs. 0.901) and validation (AUCs, Com-RS vs. clinical model vs. nomogram, 0.815 vs. 0.666 vs. 0.818) cohort. Our findings suggested values of intra- and peritumoral-based radiomics for predicting recurrent HGSOC. The constructed nomogram may be of importance in clinical application.