锥切术与阴道镜引导活检（CDB）证实宫颈高度鳞状上皮内病变（HSIL）的一致性为64-85%。我们的目的是确定宫颈 HSIL 患者锥切术后病理升级或降级相关的危险因素，并基于机器学习预测模型提供风险分层管理。这项回顾性研究纳入了2019年1月1日至12月31日到复旦大学妇产科医院就诊、经CDB诊断为宫颈HSIL并随后接受锥切术的患者。从病历中收集了各种各样的数据，包括人口统计数据、实验室检查结果、阴道镜检查描述和病理结果。根据锥切后病理结果将患者分为三组：低度鳞状上皮内病变（LSIL）或以下（降级组）、HSIL（HSIL组）和宫颈癌（升级组）。通过单因素和多因素分析来确定宫颈HSIL患者病理变化的独立危险因素。建立、评估机器学习预测模型，并随后使用外部测试数据进行验证。总共纳入了 1585 名患者，其中 65 名（4.1%）在锥切后升级为宫颈癌，1147 名（72.4%）仍患有 HSIL，373 名（23.5%）降级为 LSIL 或以下。多变量分析显示，年龄每增加一岁，病理降级的发生率就会降低 2%，病变大小会增加 1%。细胞学检查> LSIL（比值比[OR] = 0.33；95%置信区间[CI]，0.21-0.52）、人乳头瘤病毒（HPV）感染（OR = 0.33；95% CI，0.14-0.81）、HPV 33感染的患者(OR = 0.37; 95% CI, 0.18-0.78)，阴道镜检查显示粗点状血管(OR = 0.14; 95% CI, 0.06-0.32)，宫颈管内HSIL病变(OR = 0.48; 95% CI, 0.30) -0.76) 和 HSIL 印象 (OR = 0.02; 95% CI, 0.01-0.03) 与对应者相比，锥切后经历病理降级的可能性较小。锥切术后病理升级为宫颈癌的独立危险因素包括：年龄（OR = 1.08；95% CI，1.04-1.12）、HPV 16感染（OR = 4.07；95% CI，1.70-9.78）、存在宫颈癌。阴道镜检查时发现粗点状血管 (OR = 2.21; 95% CI, 1.08-4.50)、非典型血管 (OR = 6.87; 95% CI, 2.81-16.83) 和宫颈管内 HSIL 病变 (OR = 2.91; 95) % CI，1.46-5.77）。在六种机器学习预测模型中，反向传播（BP）神经网络模型在降级组、HSIL 组和升级组中表现出最高且最一致的预测性能，曲线下面积（AUC）分别为 0.90、0.84 和 0.69 ;灵敏度为 0.74、0.84 和 0.42；特异性为 0.90、0.71 和 0.95；准确度分别为 0.74、0.84 和 0.95。在外部测试集中，BP神经网络模型表现出比逻辑回归模型更高的预测性能，总体AUC为0.91。因此，本研究开发了一种基于网络的预测工具。 BP神经网络预测模型具有优异的预测性能，可用于CDB诊断的HSIL患者的风险分层。© 2024 The Author(s). 《医学病毒学杂志》由 Wiley periodicals LLC 出版。

The concordance rate between conization and colposcopy-directed biopsy (CDB) proven cervical high-grade squamous intraepithelial lesion (HSIL) were 64-85%. We aimed to identify the risk factors associated with pathological upgrading or downgrading after conization in patients with cervical HSIL and to provide risk-stratified management based on a machine learning predictive model. This retrospective study included patients who visited the Obstetrics and Gynecology Hospital of Fudan University from January 1 to December 31, 2019, were diagnosed with cervical HSIL by CDB, and subsequently underwent conization. A wide variety of data were collected from the medical records, including demographic data, laboratory findings, colposcopy descriptions, and pathological results. The patients were categorized into three groups according to their postconization pathological results: low-grade squamous intraepithelial lesion (LSIL) or below (downgrading group), HSIL (HSIL group), and cervical cancer (upgrading group). Univariate and multivariate analyses were performed to identify the independent risk factors for pathological changes in patients with cervical HSIL. Machine learning prediction models were established, evaluated, and subsequently verified using external testing data. In total, 1585 patients were included, of whom 65 (4.1%) were upgraded to cervical cancer after conization, 1147 (72.4%) remained having HSIL, and 373 (23.5%) were downgraded to LSIL or below. Multivariate analysis showed a 2% decrease in the incidence of pathological downgrade for each additional year of age and a 1% increase in lesion size. Patients with cytology > LSIL (odds ratio [OR] = 0.33; 95% confidence interval [CI], 0.21-0.52), human papillomavirus (HPV) infection (OR = 0.33; 95% CI, 0.14-0.81), HPV 33 infection (OR = 0.37; 95% CI, 0.18-0.78), coarse punctate vessels on colposcopy examination (OR = 0.14; 95% CI, 0.06-0.32), HSIL lesions in the endocervical canal (OR = 0.48; 95% CI, 0.30-0.76), and HSIL impression (OR = 0.02; 95% CI, 0.01-0.03) were less likely to experience pathological downgrading after conization than their counterparts. The independent risk factors for pathological upgrading to cervical cancer after conization included the following: age (OR = 1.08; 95% CI, 1.04-1.12), HPV 16 infection (OR = 4.07; 95% CI, 1.70-9.78), the presence of coarse punctate vessels during colposcopy examination (OR = 2.21; 95% CI, 1.08-4.50), atypical vessels (OR = 6.87; 95% CI, 2.81-16.83), and HSIL lesions in the endocervical canal (OR = 2.91; 95% CI, 1.46-5.77). Among the six machine learning prediction models, the back propagation (BP) neural network model demonstrated the highest and most uniform predictive performance in the downgrading, HSIL, and upgrading groups, with areas under the curve (AUCs) of 0.90, 0.84, and 0.69; sensitivities of 0.74, 0.84, and 0.42; specificities of 0.90, 0.71, and 0.95; and accuracies of 0.74, 0.84, and 0.95, respectively. In the external testing set, the BP neural network model showed a higher predictive performance than the logistic regression model, with an overall AUC of 0.91. Therefore, a web-based prediction tool was developed in this study. BP neural network prediction model has excellent predictive performance and can be used for the risk stratification of patients with CDB-diagnosed HSIL.© 2024 The Author(s). Journal of Medical Virology published by Wiley Periodicals LLC.