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Introduction <p>Ovarian cancer (OC) is a leading gynecological malignancy with poor prognosis due to late diagnosis. Traditional models overlook nonlinear interactions among clinicopathological factors. This study developed an interpretable eXtreme Gradient Boosting (XGBoost)-Cox model to predict overall survival (OS) and progression-free survival (PFS) in OC.</p> Methods <p>We conducted a retrospective analysis of 313 OC patients treated at a single center between 2012 and 2025. An XGBoost model with a Cox proportional hazards objective was used to generate a machine learning–derived risk score (ML-score), which was subsequently incorporated into Cox regression analysis. Model interpretability was enhanced using Shapley additive explanations (SHAP). A nomogram was constructed based on the ML-score, and model performance was evaluated using Harrell’s concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA).</p> Results <p>SHAP highlighted key factors: targeted therapy, histological type, tumor size, age, postoperative ICU admission for OS; age, tumor size, stage, targeted therapy, bleeding, histological type, omental/lymph node metastasis for PFS. ML-score stratified high-/low-risk groups (OS: HR = 4.67, 95% CI 3.14–6.96; PFS: HR = 4.05, 95% CI 2.85–5.75; <i>p</i> &lt; 0.0001). ML model showed superior C-index and area under the ROC curve (AUC) with increasing long-term accuracy and better DCA net benefit.</p> Conclusion <p>This ML model provides robust, interpretable prognostication, capturing nonlinear effects of factors like targeted therapy and tumor burden.</p>

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XGBoost-Cox modeling with SHAP analysis for survival prediction in ovarian cancer patients: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Yifan Feng,
  • Yuze Hu,
  • Tiantian Li,
  • Min Li,
  • Minmin Zhang

摘要

Introduction

Ovarian cancer (OC) is a leading gynecological malignancy with poor prognosis due to late diagnosis. Traditional models overlook nonlinear interactions among clinicopathological factors. This study developed an interpretable eXtreme Gradient Boosting (XGBoost)-Cox model to predict overall survival (OS) and progression-free survival (PFS) in OC.

Methods

We conducted a retrospective analysis of 313 OC patients treated at a single center between 2012 and 2025. An XGBoost model with a Cox proportional hazards objective was used to generate a machine learning–derived risk score (ML-score), which was subsequently incorporated into Cox regression analysis. Model interpretability was enhanced using Shapley additive explanations (SHAP). A nomogram was constructed based on the ML-score, and model performance was evaluated using Harrell’s concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA).

Results

SHAP highlighted key factors: targeted therapy, histological type, tumor size, age, postoperative ICU admission for OS; age, tumor size, stage, targeted therapy, bleeding, histological type, omental/lymph node metastasis for PFS. ML-score stratified high-/low-risk groups (OS: HR = 4.67, 95% CI 3.14–6.96; PFS: HR = 4.05, 95% CI 2.85–5.75; p < 0.0001). ML model showed superior C-index and area under the ROC curve (AUC) with increasing long-term accuracy and better DCA net benefit.

Conclusion

This ML model provides robust, interpretable prognostication, capturing nonlinear effects of factors like targeted therapy and tumor burden.