Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma

作者：Qin‐Hua Guo, Fucai Xie, Fangmin Zhong, Wen Wen, Xue‐Ru Zhang, Xia‐Jing Yu, Xinlu Wang, Xin‐Lu Wang, Bo Huang, Liping Li, Xiaozhong Wang, Xiaozhong Wang · 发表于：Cancer Medicine · 年份：2024 · DOI：10.1002/cam4.7161 · 被引用次数：17 · 研究领域：Ovarian cancer diagnosis and treatment、AI in cancer detection、Chromatin Remodeling and Cancer

BACKGROUND: Ovarian 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. METHODS: Utilizing 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). RESULTS: In 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 p...