Fully-connected network-based prediction model for lymph node metastasis in clinical early-stage endometrial cancer: development and validation in two centers - Cai, Shuyan; Huang, Yuzhen; Liu, Wei; Ren, Yulan; Wang, Huaying; Xu, Zhiying; Xue, Yu; Wang, Yiqin; Chen, Xiaojun (Frontiers in Oncology, 2025)
ObjectiveThe risk of lymph node metastasis significantly influences the choice of surgical strategy for patients with early-stage endometrial cancer. While sentinel lymph node dissection can be considered in clinically early-stage endometrial cancer, lymph node evaluation might be omitted in patients with very low risk of lymph node metastasis. This study aims to develop a predicting model for lymph node metastasis in these patients, identifying potential metastases as thoroughly as possible to provide clinicians with a preoperative reference that helps in decisions about surgical procedures and treatments.Materials and MethodsWe retrospectively collected data from 4,400 cases across two centers to develop a predictive model for lymph node metastasis in patients with early-stage endometrial cancer using a Fully-connected (FC) Network. Internal validation was performed, and an additional 750 cases were prospectively collected from subcenter 1 for external validation. After comparing commonly used imputation methods, missing values were filled using the K-Nearest Neighbors (KNN) for the highest sensitivity of the model. The model was evaluated by precision, sensitivity, specificity, and overall accuracy. The performance of the model was compared to other machine-learning models. The risk stratification was divided by 1%, 5%, and 25%. Combining the results of Logistic regression, the pathological subtype-specific nomograms were constructed and served as alternatives to the FC Network.ResultsThe FC Network achieved the highest sensitivity—0.982 in internal validation and 0.900 in external validation—demonstrating exceptional performance in identifying patients with probable lymph node metastasis compared to other machine-learning methods. Considering the prognostic implications of histological subtypes, subtype-specific nomograms were constructed, achieving AUCs of 0.810/0.784/0.834 for non-aggressive and 0.726/0.810/0.650 for aggressive subtypes across the training, internal, and external cohorts.ConclusionsThe model proposed in this study can be used for risk prediction of lymph node metastasis in early-stage patients. The nomograms can be used as a feasible and easily used alternative for the model.