结合临床表现和全腹部和骨盆的术前计算机断层扫描（CT），开发一种基于机器学习的早期宫颈癌盆腔淋巴结转移（PLNM）术前预测模型。纳入2003年1月1日至2020年12月31日期间接受双侧盆腔淋巴结切除术的初次根治性手术的妇产科IA2-IIA1期鳞状细胞癌、腺癌和宫颈腺鳞癌。使用逻辑回归、随机森林、支持向量机、自适应提升、梯度提升、极端梯度提升和类别提升等七种监督机器学习算法来评估 PLNM 的风险。 PLNM 在 199 (23.9%) 中被发现包括 832 名患者。年龄较小、肿瘤较大、分期较高、既往无锥切术、肿瘤外观、腺鳞组织学和阴道转移以及CT表现较大肿瘤、宫旁转移、盆腔淋巴结肿大和阴道转移与PLNM。模型的预测性能，包括准确性（89.1%-90.6%）、受试者工作特征曲线下面积（86.9%-91.0%）、敏感性（77.4%-82.4%）、特异性（92.1%-94.3%）、阳性所有算法的预测值（77.0%-81.7%）和阴性预测值（93.0%-94.4%）均令人满意且具有可比性。优化模型的决策阈值，将灵敏度提高到至少95%后，获得“高灵敏度”模型，PLNM预测的假阴性率为2.5%-4.4%。我们开发了早期宫颈PLNM的预测模型在我们的环境中具有有希望的预测性能的癌症。需要在其他人群中进行进一步的外部验证以实现潜在的临床应用。© 2024。亚洲妇科肿瘤学会、韩国妇科肿瘤学会和日本妇科肿瘤学会。

To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis.Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM.PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models' predictive performance, including accuracy (89.1%-90.6%), area under the receiver operating characteristics curve (86.9%-91.0%), sensitivity (77.4%-82.4%), specificity (92.1%-94.3%), positive predictive value (77.0%-81.7%), and negative predictive value (93.0%-94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model's decision threshold to enhance the sensitivity to at least 95%, the 'highly sensitive' model was obtained with a 2.5%-4.4% false-negative rate of PLNM prediction.We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.© 2024. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology, and Japan Society of Gynecologic Oncology.