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目的 分析宫颈癌前病变的危险因素，并构建和验证列线图预测模型，以优化对阴道镜转诊患者识别宫颈高级别病变及活检的准确性。方法 本回顾性研究纳入2021年-2024年新疆地区转诊阴道镜检查的1 710名妇女，按7∶3的比例随机分配到训练集和验证集。采用单因素及多因素Logistic回归分析筛选出高级别鳞状上皮内病变(High-grade squamous intraepithelial lesions or worse, HSIL+)发生的独立危险因素，构建列线图预测模型。并通过区分能力、校准和决策曲线进行评估。结果 单因素分析显示，年龄、细胞学结果、HPV状态、转化区类型及阴道镜拟诊与HSIL+发生有关(P均<0.05)。Logistic多因素分析显示，年龄、细胞学结果、HPV状态、转化区类型及阴道镜拟诊均是HSIL+发生的独立危险因素(P均<0.05)。基于以上变量所构建的列线图预测模型在训练集和验证集所得的曲线下面积(AUC)值分别为0.977(95%CI:0.968～0.985)和0.982(95%CI:0.972～0.992)。模型验证结果表明训练集和验证集校准曲线表现出良好的一致性。模型的临床净获益较高，临床效能较好。结论 基于年龄、细胞学和HPV结果、阴道镜特征构建的列线图模型有助于妇科医生在阴道镜下个体化评估，为阴道镜下活检决策及高级别宫颈病变预测提供有利工具。
Abstract：Objective To analyze the risk factors for cervical precancerous lesions and to develop and validate a nomogram prediction model, aiming to optimize the identification of high-grade cervical lesions and biopsy accuracy among colposcopy-referred patients. Methods This retrospective study enrolled 1 710 women who were referred for colposcopy between 2021 and 2024, who were randomly assigned to the training and validation sets in a 7∶3 ratio. Univariate and multivariate Logistic regression analyses were performed to identify independent risk factors for high-grade squamous intraepithelial lesions or worse(HSIL+), and a nomogram prediction model was developed. The model was then evaluated using discrimination ability, calibration, and decision curve analysis. Results Univariate analysis showed that age, cytological results, HPV status, transformation zone(TZ) types and colposcopic impression were significantly associated with the occurrence of HSIL+(all P<0.05). Multivariate Logistic regression analysis further demonstrated that age, cytological results, HPV status, TZ types and colposcopic impression were independent risk factors for HSIL+(all P<0.05). The nomogram prediction model constructed based on these variables achieved areas under the curve(AUC) of 0.977(95%CI: 0.968-0.985) in the training set and 0.982(95%CI: 0.972-0.992) in the validation set. Model validation results indicated good calibration consistency between the training and validation sets. The model demonstrated high clinical utility and strong clinical performance. Conclusion The nomogram model based on age, cytology, HPV results and colposcopic features can assist gynecologists in individualized assessments during colposcopy, providing a valuable tool for colposcopic biopsy decision-making and predicting high-grade cervical lesions.
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基本信息:
中图分类号:R737.33
引用信息:
[1]沙丽娅·阿不都吾甫,张媛媛,夏日瓦娜·阿巴斯,等.阴道镜转诊患者高级别宫颈癌前病变风险评估列线图预测模型构建及验证[J].新疆医科大学学报,2025,48(08):1031-1038.
基金信息:
新疆维吾尔自治区自然科学基金重点项目(2021D01D23); 国家自然科学基金项目(72264038)
2025-08-15
2025-08-15