ArticleCancer medicine2024
Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Prolonged P3 latency predicts clinical response to repetitive transcranial magnetic stimulation in tinnitus.Clinical neurophysiology practice · 2026Article
- An Interpretable AdaBoost Model for 1-Year Readmission Risk Prediction in AECOPD Patients with Hypertension.International journal of chronic obstructive pulmonary disease · 2026Article
- Machine-learning prediction of 3- and 5-year mortality in lymph-node-positive medullary thyroid carcinoma: a study based on the SEER database and external validation in a Chinese cohort.Frontiers in oncology · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Development and validation of a risk prediction model for postoperative urinary retention after gynecologic abdominal-pelvic surgery.Scientific reports · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Development of a Predictive Risk Model for Recurrence of Chronic Pulmonary Aspergillosis in Post-Tuberculosis Patients: A Retrospective Observational Study.International journal of general medicine · 2025Article
- A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database.Frontiers in immunology · 2025Article
- Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.Biomedicines · 2024Article
- Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.Cancer medicine · 2024Article
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10 authors.
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Abstract
backgroundOvarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC.
methodsUtilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC).
resultsIn the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients.
conclusionsThis study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.
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