Purpose: The current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients.
Methods: The study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer.
Results: The mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731-0.751) in model dataset and 0.738 (95% confidence interval: 0.726-0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733-0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset.
Conclusion: The current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.
Keywords: Artificial intelligence; Individual mortality risk prediction; Ovary cancer; Precision medicine; Prognostic model; Restricted mean survival time.
© 2023. The Author(s).