Deng Erle, Gu Zheng, Wei Hongtao, Liu Chengdi, Dong Yiwen, Yu Junxian
Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Reprod Sci. 2025 Apr 7. doi: 10.1007/s43032-025-01856-0.
Cervical cancer is one of the most common malignant tumors in women worldwide, and patients with lymph node metastasis have a poor prognosis. This study aimed to develop an effective machine learning model to predict the prognosis of these patients. Data from the SEER*Stat database (version: November 2021) was used, including 1016 female patients diagnosed with cervical cancer and lymph node metastasis from 2000 to 2020. Various machine learning models, including XGBoost, random forest, SVM, ANN, and the Cox proportional hazards model, were constructed and evaluated using metrics such as C-index, AUC, accuracy, and precision. Additionally, to validate model stability, a random sample of 200 patients from 8 registries between 1975 and 2019 was used as a validation set. XGBoost outperformed other models with an AUC of 0.787 in the validation set and C-index values of 0.900 and 0.773 for the training and testing sets, respectively. Cox regression analysis showed that surgery at the primary site significantly improved survival outcomes and reduced mortality. XGBoost demonstrated superior performance in predicting the prognosis of cervical cancer patients with lymph node metastasis, providing new support for personalized clinical management.
宫颈癌是全球女性中最常见的恶性肿瘤之一，发生淋巴结转移的患者预后较差。本研究旨在开发一种有效的机器学习模型来预测这些患者的预后。使用了SEER*Stat数据库（版本：2021年11月）的数据，包括2000年至2020年期间诊断为宫颈癌并发生淋巴结转移的1016例女性患者。构建了各种机器学习模型，包括XGBoost、随机森林、支持向量机、人工神经网络和Cox比例风险模型，并使用C指数、AUC、准确率和精确率等指标进行评估。此外，为了验证模型稳定性，将1975年至2019年间来自8个登记处的200名患者的随机样本用作验证集。在验证集中，XGBoost的AUC为0.787，优于其他模型，训练集和测试集的C指数值分别为0.900和0.773。Cox回归分析表明，原发部位手术显著改善了生存结局并降低了死亡率。XGBoost在预测宫颈癌淋巴结转移患者的预后方面表现出卓越性能，为个性化临床管理提供了新的支持。
