ArticleJournal of gynecologic oncology2026
Risk factors associated with overall survival in patients with cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional hazards models.
Article in Journal of gynecologic oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveCervical cancer (CCa) significantly affects female fertility and quality of life. This study aimed to construct and validate a random survival forest (RSF) model to identify the factors that affect the overall survival (OS) in patients with CCa in China and compare its performance with that of the Cox proportional hazards model (Cox model).
methodsData on CCa patients were collected from Chongqing University Cancer Hospital. The performance and discrimination ability of the models were evaluated via the C-index, integrated Brier score (IBS), accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The Kaplan-Meier (K-M) survival curve was used to analyze the difference in OS between patients with high and low risk predicted by RSF model.
resultsA total of 3,982 patients were included in this study. Comparing to Cox model, the RSF model ranked important variables and identified radiotherapy (RT) as an important treatment measure. A comprehensive analysis of the evaluation indices confirmed that the RSF model outperformed the Cox model (IBS: 0.152 vs. 0.162, C-index: 0.863 vs. 0.764). The RSF model metrics for the validation cohort (VC) were as follows: 1-, 3-, and 5-year AUC (0.908, 0.884, and 0.869), sensitivity (0.746), specificity (0.825), and accuracy (0.808). The OS of low-risk patients predicted by RSF was greater than that of high-risk patients.
conclusionThe RSF model demonstrated excellent discrimination, calibrated predictions, and stratified risk for CCa patients. Furthermore, it outperformed the Cox model in predicting risks, thus enabling the delivery of personalised treatment and follow-up strategies.
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