ArticleInternational journal of surgery (London, England)2025
Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.
Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Ileocecal and Small Bowel Involvement Are Independently Associated with Inferior Survival Despite Complete Cytoreduction in FIGO IIIC-IV Tubo-Ovarian and Primary Peritoneal Carcinoma.Annals of surgical oncology · 2026Article
- Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.Journal of ovarian research · 2026Article
- A prognostic model based on ScissorTranslational cancer research · 2026Article
- Extrapancreatic extension modifies chemotherapy benefit in resected pancreatic ductal adenocarcinoma: a population-based cohort study.Scientific reports · 2026Article
- Development and validation of machine learning models for predicting cancer-specific survival in colorectal signet ring cell carcinoma.BMC gastroenterology · 2026Article
- Dose-response relationship between ascites volume and survival in high-grade serous ovarian cancer: a prospective cohort study.Gland surgery · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Transcriptomics, lifestyle, and autoimmune thyroiditis: an integrated association study.Frontiers in immunology · 2026Article
- Age-dependent effects of surgical approach in T3b differentiated thyroid carcinoma: a population-based analysis using machine learning.Endocrine-related cancer · 2025Article
- Development and validation of a machine learning model for predicting vulnerable carotid plaques using routine blood biomarkers and derived indicators: insights into sex-related risk patterns.Cardiovascular diabetology · 2025Article
- Options for postoperative radiation therapy in patients with de novo metastatic breast cancer.Breast (Edinburgh, Scotland) · 2025Article
- Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection.Life (Basel, Switzerland) · 2025Article
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6 authors.
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Abstract
backgroundHigh-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.
methodsNine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app.
resultsThe random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750-0.780) and a Brier score of 0.183 (95% CI: 0.175-0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/ .
conclusionAn explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.
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