PDF(Pubmed)
Abstract：
OBJECTIVE: This study aims to analyze factors associated with positive surgical margins following cold knife conization (CKC) in patients with cervical high-grade squamous intraepithelial lesion (HSIL) and to develop a machine-learning-based risk prediction model.
METHODS: We conducted a retrospective analysis of 3,343 patients who underwent CKC for HSIL at our institution. Logistic regression was employed to examine the relationship between demographic and pathological characteristics and the occurrence of positive surgical margins. Various machine learning methods were then applied to construct and evaluate the performance of the risk prediction model.
RESULTS: The overall rate of positive surgical margins was 12.9%. Independent risk factors identified included glandular involvement (OR = 1.716, 95% CI: 1.345-2.189), transformation zone III (OR = 2.838, 95% CI: 2.258-3.568), HPV16/18 infection (OR = 2.863, 95% CI: 2.247-3.648), multiple HR-HPV infections (OR = 1.930, 95% CI: 1.537-2.425), TCT ≥ ASC-H (OR = 3.251, 95% CI: 2.584-4.091), and lesions covering ≥ 3 quadrants (OR = 3.264, 95% CI: 2.593-4.110). Logistic regression demonstrated the best prediction performance, with an accuracy of 74.7%, sensitivity of 76.7%, specificity of 74.4%, and AUC of 0.826.
CONCLUSIONS: Independent risk factors for positive margins after CKC include HPV16/18 infection, multiple HR-HPV infections, glandular involvement, extensive lesion coverage, high TCT grades, and involvement of transformation zone III. The logistic regression model provides a robust and clinically valuable tool for predicting the risk of positive margins, guiding clinical decisions and patient management post-CKC.
METHODS: We conducted a retrospective analysis of 3,343 patients who underwent CKC for HSIL at our institution. Logistic regression was employed to examine the relationship between demographic and pathological characteristics and the occurrence of positive surgical margins. Various machine learning methods were then applied to construct and evaluate the performance of the risk prediction model.
RESULTS: The overall rate of positive surgical margins was 12.9%. Independent risk factors identified included glandular involvement (OR = 1.716, 95% CI: 1.345-2.189), transformation zone III (OR = 2.838, 95% CI: 2.258-3.568), HPV16/18 infection (OR = 2.863, 95% CI: 2.247-3.648), multiple HR-HPV infections (OR = 1.930, 95% CI: 1.537-2.425), TCT ≥ ASC-H (OR = 3.251, 95% CI: 2.584-4.091), and lesions covering ≥ 3 quadrants (OR = 3.264, 95% CI: 2.593-4.110). Logistic regression demonstrated the best prediction performance, with an accuracy of 74.7%, sensitivity of 76.7%, specificity of 74.4%, and AUC of 0.826.
CONCLUSIONS: Independent risk factors for positive margins after CKC include HPV16/18 infection, multiple HR-HPV infections, glandular involvement, extensive lesion coverage, high TCT grades, and involvement of transformation zone III. The logistic regression model provides a robust and clinically valuable tool for predicting the risk of positive margins, guiding clinical decisions and patient management post-CKC.
摘要：
目的：本研究旨在分析宫颈高级别鳞状上皮内病变（HSIL）患者冷刀锥切术（CKC）后手术切缘阳性的相关因素，并建立基于机器学习的风险预测模型。
方法：我们对在我们机构接受HSILCKC的3,343例患者进行了回顾性分析。采用Logistic回归分析人口统计学和病理特征与手术切缘阳性发生之间的关系。然后应用各种机器学习方法来构建和评估风险预测模型的性能。
结果：总的手术切缘阳性率为12.9%。确定的独立危险因素包括腺体受累（OR=1.716，95％CI：1.345-2.189），转化区III(OR=2.838，95%CI：2.258-3.568)，HPV16/18感染（OR=2.863，95%CI：2.247-3.648），多重HR-HPV感染（OR=1.930，95%CI：1.537-2.425），TCT≥ASC-H（OR=3.251，95%CI：2.584-4.091），病变覆盖≥3个象限（OR=3.264，95%CI：2.593-4.110）。Logistic回归显示出最佳的预测性能，准确率为74.7%，灵敏度为76.7%，特异性74.4%，AUC为0.826。
结论：CKC术后切缘阳性的独立危险因素包括HPV16/18感染，多种HR-HPV感染，腺体受累，广泛的病变覆盖，高TCT等级,以及转化区III的参与。逻辑回归模型提供了一个强大的和临床有价值的工具来预测积极的边缘的风险，指导CKC后的临床决策和患者管理。
方法：我们对在我们机构接受HSILCKC的3,343例患者进行了回顾性分析。采用Logistic回归分析人口统计学和病理特征与手术切缘阳性发生之间的关系。然后应用各种机器学习方法来构建和评估风险预测模型的性能。
结果：总的手术切缘阳性率为12.9%。确定的独立危险因素包括腺体受累（OR=1.716，95％CI：1.345-2.189），转化区III(OR=2.838，95%CI：2.258-3.568)，HPV16/18感染（OR=2.863，95%CI：2.247-3.648），多重HR-HPV感染（OR=1.930，95%CI：1.537-2.425），TCT≥ASC-H（OR=3.251，95%CI：2.584-4.091），病变覆盖≥3个象限（OR=3.264，95%CI：2.593-4.110）。Logistic回归显示出最佳的预测性能，准确率为74.7%，灵敏度为76.7%，特异性74.4%，AUC为0.826。
结论：CKC术后切缘阳性的独立危险因素包括HPV16/18感染，多种HR-HPV感染，腺体受累，广泛的病变覆盖，高TCT等级,以及转化区III的参与。逻辑回归模型提供了一个强大的和临床有价值的工具来预测积极的边缘的风险，指导CKC后的临床决策和患者管理。
