Li Dongmei, Wang Zhichao, Liu Yan, Zhou Meiyuan, Xia Bo, Zhang Lin, Chen Keming, Zeng Yong
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze University, 8 Hangkong Road, Shashi District, Jingzhou, Hubei, China.
Oncology Department, The First Affiliated Hospital of Yangtze University, 8 Hangkong Road, Shashi District, Jingzhou, Hubei, China.
Infect Agent Cancer. 2024 Dec 5;19(1):61. doi: 10.1186/s13027-024-00625-z.
This study aims to analyze factors associated with the missed diagnosis of high-grade squamous intraepithelial lesions (HSIL+) in patients initially diagnosed with low-grade squamous intraepithelial lesions (LSIL) through colposcopic biopsy and to develop a predictive model for assessing the risk of missed HSIL+.
We conducted a retrospective analysis of 505 patients who underwent loop electrical excision procedure (LEEP) following an LSIL diagnosis by colposcopic biopsy. Logistic regression was used to identify demographic and pathological parameters associated with missed diagnoses of HSIL+. Additionally, several machine learning methods were employed to construct and assess the performance of the risk prediction models.
The overall rate of missed diagnoses for HSIL+ was 15.2%. Independent risk factors identified were HPV16/18 infection (OR 2.071; 95% CI 1.039-4.127; p = 0.039), TCT ≥ ASC-H (OR 4.147; 95% CI 1.392-12.355; p = 0.011), TZ3 (OR 1.966; 95% CI 1.003-3.853; p = 0.049) and Colposcopic impression G2 (OR 3.627; 95% CI 1.350-9.743; p = 0.011). Among the models tested, the Decision Tree algorithm demonstrated superior performance with an accuracy of 94.7%, sensitivity of 80.0%, specificity of 96.9%, and an area under the curve (AUC) of 0.936 in the validation set.
Key independent risk factors for the missed diagnosis of HSIL in patients with LSIL include HPV16/18 infection, TCT ≥ ASC-H, TZ3, and colposcopic impression G2. The Decision Tree model offers a cost-effective, reliable, and clinically valuable tool for accurately predicting the risk of missed diagnosis of HSIL+, facilitating early intervention and management.
本研究旨在分析经阴道镜活检最初诊断为低级别鳞状上皮内病变（LSIL）的患者中，与高级别鳞状上皮内病变（HSIL+）漏诊相关的因素，并建立一个预测模型来评估HSIL+漏诊风险。
我们对505例经阴道镜活检诊断为LSIL后接受环形电切术（LEEP）的患者进行了回顾性分析。采用逻辑回归分析来确定与HSIL+漏诊相关的人口统计学和病理参数。此外，还采用了几种机器学习方法来构建和评估风险预测模型的性能。
HSIL+的总体漏诊率为15.2%。确定的独立危险因素包括HPV16/18感染（OR 2.071；95%CI 1.039 - 4.127；p = 0.039）、TCT≥ASC-H（OR 4.147；95%CI 1.392 - 12.355；p = 0.011）、转化区3型（TZ3）（OR 1.966；95%CI 1.003 - 3.853；p = 0.049）和阴道镜印象G2（OR 3.627；95%CI 1.350 - 9.743；p = 0.011）。在所测试的模型中，决策树算法表现出卓越性能，在验证集中准确率为94.7%，灵敏度为80.0%，特异性为96.9%，曲线下面积（AUC）为0.936。
LSIL患者中HSIL漏诊的关键独立危险因素包括HPV16/18感染、TCT≥ASC-H、TZ3和阴道镜印象G2。决策树模型为准确预测HSIL+漏诊风险提供了一种经济有效、可靠且具有临床价值的工具，有助于早期干预和管理。
