Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models.

BACKGROUND

Epithelial ovarian cancer(EOC) has a higher mortality and morbidity rate than other types, and it has a dramatic impact on the survival of ovarian cancer(OC) patients. Therefore, investigating, developing and validating prognostic models to predict overall survival(OS) in patients with epithelial ovarian cancer represents an area of research with significant clinical implications.

METHODS

Patients with a confirmed diagnosis of epithelial ovarian cancer from 2010 to 2017 in The Surveillance, Epidemiology, and End Results(SEER) database were identified for enrollment based on inclusion and exclusion criteria(N=10902). Patients with epithelial ovarian cancer diagnosed from 2010 to 2022 were selected from Dalian Municipal Central Hospital as an external validation cohort based on the same criteria (N=116). COX proportional risk regression for screening independent prognostic factors. Survival outcomes were compared between different risk subgroups based on Kaplan-Meier analysis. Three predictive models were developed using machine learning(ML) techniques, and another was a nomogram based on COX proportional risk regression for estimating 3-year and 5-year overall survival in patients with epithelial ovarian cancer. Evaluation of several models based on multiple metrics including C-index, ROC curve, calibration curve and decision curve analysis (DCA).

RESULTS

Through univariate and multivariate COX proportional risk regression analyses, we selected 12 significantly independent prognostic factors affecting overall survival (P<0.05). In conclusion, comparing several models cited, it was found that DeepSurv (Deep Survival) model had the best performance in both internal validation set and external validation set. The C-index for internal validation was 0.715, and the 3-year and 5-year ROC curves were 0.746 and 0.766; the C-index for external validation was 0.672, and the 3-year and 5-year ROC curves were 0.731 and 0.756.

CONCLUSION

This study successfully developed a nomogram and three machine learning models, which collectively served as important predictive instruments to support clinical decision making.