淋巴结转移（LNM）是宫颈癌（CC）的重要预后因素，并决定治疗策略。据报道，血液学指标是多种癌症预后的有用生物标志物。本研究旨在评估以术前血液学指标为特征的机器学习模型预测CC患者术前LNM状态的可行性。对第一附属医院妇科肿瘤科236例经病理证实的CC患者的临床资料进行回顾性分析蚌埠医科大学于2020年11月至2022年8月进行的研究。使用最小绝对收缩和选择算子（LASSO）从35个血液学指标中选择21个特征，并构建6个机器学习预测模型，包括自适应提升（AdaBoost）、高斯朴素贝叶斯 (GNB) 和逻辑回归 (LR)，以及随机森林 (RF)、支持向量机 (SVM) 和极限梯度提升 (XGBoost)。预测模型的评价指标包括受试者工作特征曲线下面积（AUC）、准确性、特异性、敏感性和F1分数。RF在训练集中的十倍交叉验证中具有最佳的整体预测性能。 RF的具体表现指标为AUC（0.910，95%置信区间[CI]：0.820-1.000）、准确度（0.831，95% CI：0.702-0.960）、特异性（0.835，95% CI：0.708-0.962）、敏感性（0.831，95% CI：0.702-0.960）和 F1 分数（0.829，95% CI：0.696-0.962）。 RF在测试集中具有最高的AUC（AUC = 0.854）。基于临床实践中容易获得的术前血液学指标的RF在CC LNM的术前预测中表现出优越的性能。然而，需要对更大的外部患者队列进行调查，以进一步验证我们的研究结果。版权所有 © 2024 赵、王、孙、王、石、刘和张。

Lymph node metastasis (LNM) is an important prognostic factor for cervical cancer (CC) and determines the treatment strategy. Hematological indicators have been reported as being useful biomarkers for the prognosis of a variety of cancers. This study aimed to evaluate the feasibility of machine learning models characterized by preoperative hematological indicators to predict the LNM status of CC patients before surgery.The clinical data of 236 patients with pathologically confirmed CC were retrospectively analyzed at the Gynecology Oncology Department of the First Affiliated Hospital of Bengbu Medical University from November 2020 to August 2022. The least absolute shrinkage and selection operator (LASSO) was used to select 21 features from 35 hematological indicators and for the construction of 6 machine learning predictive models, including Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), and Logistic Regression (LR), as well as Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). Evaluation metrics of predictive models included the area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1-score.RF has the best overall predictive performance for ten-fold cross-validation in the training set. The specific performance indicators of RF were AUC (0.910, 95% confidence interval [CI]: 0.820-1.000), accuracy (0.831, 95% CI: 0.702-0.960), specificity (0.835, 95% CI: 0.708-0.962), sensitivity (0.831, 95% CI: 0.702-0.960), and F1-score (0.829, 95% CI: 0.696-0.962). RF had the highest AUC in the testing set (AUC = 0.854).RF based on preoperative hematological indicators that are easily available in clinical practice showed superior performance in the preoperative prediction of CC LNM. However, investigations on larger external cohorts of patients are required for further validation of our findings.Copyright © 2024 Zhao, Wang, Sun, Wang, Shi, Liu and Zhang.