Przeglad menopauzalny = Menopause review · 2026
Can machine learning predict endometrial cancer in patients with postmenopausal uterine bleeding?
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Gluska Hadar, Gnaiem Nagam, Katz Adi Ashkenazi, Miller Netanella, Tzidki Amit, Baron Alexandra, Hants Yael, Beiner Mario, Asali Aula
Аннотация
INTRODUCTION: Postmenopausal bleeding (PMB) is a common clinical symptom, with endometrial cancer (EC) accounting for about 10% of cases. This study aimed to develop machine-learning models to predict EC in women with PMB, supporting risk stratification and optimizing diagnostic pathways.
MATERIAL AND METHODS: We retrospectively analysed 617 women who underwent hysteroscopy for PMB at the Meir Medical Center between 2014 and 2023. Demographic, clinical, laboratory, and imaging data were collected. Three supervised machine-learning algorithms - Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) - were trained and evaluated. Synthetic minority oversampling technique addressed class imbalance. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve.
RESULTS: Seventy-two women (11.7%) were diagnosed with EC. eXtreme Gradient Boosting achieved the highest sensitivity (80%) for detecting EC, despite moderate accuracy (65%). Random Forest and LightGBM showed higher accuracy (85% and 84%, respectively) but much lower sensitivity. The most influential predictors in the XGBoost model were tamoxifen use, hormone therapy, age, hypertension, gravidity, parity, endometrial thickness, diabetes, and duration of bleeding.
CONCLUSIONS: eXtreme Gradient Boosting provided the best clinical performance by minimizing missed diagnoses. Machine-learning models may enhance decision-making by identifying women at high risk for EC who require further evaluation. Larger datasets and additional clinical features are needed to improve specificity and reduce false positives.
MATERIAL AND METHODS: We retrospectively analysed 617 women who underwent hysteroscopy for PMB at the Meir Medical Center between 2014 and 2023. Demographic, clinical, laboratory, and imaging data were collected. Three supervised machine-learning algorithms - Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) - were trained and evaluated. Synthetic minority oversampling technique addressed class imbalance. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve.
RESULTS: Seventy-two women (11.7%) were diagnosed with EC. eXtreme Gradient Boosting achieved the highest sensitivity (80%) for detecting EC, despite moderate accuracy (65%). Random Forest and LightGBM showed higher accuracy (85% and 84%, respectively) but much lower sensitivity. The most influential predictors in the XGBoost model were tamoxifen use, hormone therapy, age, hypertension, gravidity, parity, endometrial thickness, diabetes, and duration of bleeding.
CONCLUSIONS: eXtreme Gradient Boosting provided the best clinical performance by minimizing missed diagnoses. Machine-learning models may enhance decision-making by identifying women at high risk for EC who require further evaluation. Larger datasets and additional clinical features are needed to improve specificity and reduce false positives.