Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Oncol.

Sec. Gynecological Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1627662

Fully-Connected Network-Based Prediction Model for Lymph Node Metastasis in Clinical Early-Stage Endometrial Cancer: Development and Validation in Two Centers

Provisionally accepted
Shuyan  CaiShuyan Cai1Yuzhen  HuangYuzhen Huang1Qing  LinQing Lin2Wei  LiuWei Liu1Yulan  RenYulan Ren3Huaying  WangHuaying Wang3Zhiying  XuZhiying Xu1Yu  XueYu Xue1Wanying  ZhouWanying Zhou4Yiqin  WangYiqin Wang1Weimin  TanWeimin Tan4Bo  YanBo Yan4Xiaojun  ChenXiaojun Chen1*
  • 1Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
  • 2Institute for Infocomm Research (I2R), Singapore, Singapore
  • 3Fudan University Shanghai Cancer Center, Shanghai, China
  • 4Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

The final, formatted version of the article will be published soon.

Objective: The 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 Methods: We 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. Results: The 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. Conclusions: The 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.

Keywords: early-stage, endometrial cancer, lymph node metastasis, Fully-Connected Network, Prediction model

Received: 13 May 2025; Accepted: 04 Aug 2025.

Copyright: © 2025 Cai, Huang, Lin, Liu, Ren, Wang, Xu, Xue, Zhou, Wang, Tan, Yan and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiaojun Chen, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.