ArticleJournal of gynecologic oncology2019
Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.
Article in Journal of gynecologic oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- XGBoost-Cox modeling with SHAP analysis for survival prediction in ovarian cancer patients: a retrospective cohort study.BMC cancer · 2026Article
- Integrating multi-omics and clinical features to model survival in epithelial ovarian cancer subtypes.Scientific reports · 2025Article
- A Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.Surgical endoscopy · 2025Article
- Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.Scientific reports · 2025Article
- Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection.Life (Basel, Switzerland) · 2025Article
- Prognostic and Predictive Value of Systemic Inflammatory Markers in Epithelial Ovarian Cancer.Medicina (Kaunas, Lithuania) · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.Archives of academic emergency medicine · 2025Article
- Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques.Heliyon · 2024Article
- Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer.BMC cancer · 2024Article
- Screening ovarian cancer by using risk factors: machine learning assists.Biomedical engineering online · 2024Article
- Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024Review
- Development of prediction model to estimate future risk of ovarian lesions: A multi-center retrospective study.Preventive medicine reports · 2023Article
- Article
- A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesThe aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
methodsInformation of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model.
resultsIn the training cohort, the AUC of the GB model for predicting second year overall survival (OS), with the highest target value, was 0.830 (95% confidence interval [CI]=0.802-0.853). In the validation cohort, the GB model also showed high AUC of 0.843 (95% CI=0.833-0.853). In comparison, the conventional CoxPHR method showed lower AUC (0.668 (95% CI=0.617-0.719) for the training cohort and 0.597 (95% CI=0.474-0.719) for the validation cohort) compared to GB. New classification according to survival probability scores of the GB model identified four distinct prognostic subgroups that showed more discriminately classified prediction than the International Federation of Gynecology and Obstetrics staging system.
conclusionOur novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.
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