Breast (Edinburgh, Scotland) · 2025
Explainable machine learning model for predicting internal mammary node metastasis in breast cancer: Multi-method development and cross-cohort validation.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xiang Yirong, Tie Jian, Zhang Siyuan, Shi Chen, Guo Changkuo, Peng Yushuo, Fan Zhaoqing, Wang Weihu
Аннотация
BACKGROUND: This study developed an explainable machine learning model for baseline internal mammary lymph node metastasis (IMNM) in breast cancer patients.
MATERIALS AND METHODS: This study included three cohorts: a derivation cohort (n = 1997) from Peking University Cancer Hospital, a temporal testing cohort (n = 633) from the same center, and a SEER cohort (n = 51,420). Multiple machine learning strategies were conducted: Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, backward stepwise regression, and best subset for feature selection, and logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) for model construction. The best-performing model was validated across internal and temporal testing cohorts. Shapley Additive Explanations (SHAP) analysis was conducted to improve interpretability.
RESULTS: Six clinical features (clinical N stage, size, stage, classification, grade and location) were used to construct the final predictive model with SVM. The model achieved robust performance, with AUCs of 0·811 (0·790-0·843), 0.806 (0·760-0·857) and 0·864 (0·830-0·926) in the training, internal testing and temporal testing cohort, respectively. High-risk patients exhibited significantly worse outcomes with DFS (HR 2·776, 95 % CI: 1·897-4·064, p < 0·001) and OS (HR of 1·962, 95 % CI: 1·853-2·077, p < 0·001). An online prediction tool was established that allows users to input key clinical variables and obtain model-predicted probabilities along with SHAP-based explanations.
CONCLUSION: This validated and explainable machine learning model offers a practical tool for early risk stratification, aiding clinicians in appropriate baseline imaging selection and adjuvant treatment planning.
MATERIALS AND METHODS: This study included three cohorts: a derivation cohort (n = 1997) from Peking University Cancer Hospital, a temporal testing cohort (n = 633) from the same center, and a SEER cohort (n = 51,420). Multiple machine learning strategies were conducted: Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, backward stepwise regression, and best subset for feature selection, and logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) for model construction. The best-performing model was validated across internal and temporal testing cohorts. Shapley Additive Explanations (SHAP) analysis was conducted to improve interpretability.
RESULTS: Six clinical features (clinical N stage, size, stage, classification, grade and location) were used to construct the final predictive model with SVM. The model achieved robust performance, with AUCs of 0·811 (0·790-0·843), 0.806 (0·760-0·857) and 0·864 (0·830-0·926) in the training, internal testing and temporal testing cohort, respectively. High-risk patients exhibited significantly worse outcomes with DFS (HR 2·776, 95 % CI: 1·897-4·064, p < 0·001) and OS (HR of 1·962, 95 % CI: 1·853-2·077, p < 0·001). An online prediction tool was established that allows users to input key clinical variables and obtain model-predicted probabilities along with SHAP-based explanations.
CONCLUSION: This validated and explainable machine learning model offers a practical tool for early risk stratification, aiding clinicians in appropriate baseline imaging selection and adjuvant treatment planning.