Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, 100730, Beijing, China.
Division of Health Research, Lancaster University, Lancaster, UK.
BMC Cancer. 2023 Feb 17;23(1):163. doi: 10.1186/s12885-023-10646-3.
Colposcopic examination with biopsy is the standard procedure for referrals with abnormal cervical cancer screening results; however, the decision to biopsy is controvertible. Having a predictive model may help to improve high-grade squamous intraepithelial lesion or worse (HSIL+) predictions which could reduce unnecessary testing and protecting women from unnecessary harm.
This retrospective multicenter study involved 5,854 patients identified through colposcopy databases. Cases were randomly assigned to a training set for development or to an internal validation set for performance assessment and comparability testing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidate predictors and select statistically significant factors. Multivariable logistic regression was then used to establish a predictive model which generates risk scores for developing HSIL+. The predictive model is presented as a nomogram and was assessed for discriminability, and with calibration and decision curves. The model was externally validated with 472 consecutive patients and compared to 422 other patients from two additional hospitals.
The final predictive model included age, cytology results, human papillomavirus status, transformation zone types, colposcopic impressions, and size of lesion area. The model had good overall discrimination when predicting HSIL + risk, which was internally validated (Area Under the Curve [AUC] of 0.92 (95%CI 0.90-0.94)). External validation found an AUC of 0.91 (95%CI 0.88-0.94) across the consecutive sample, and 0.88 (95%CI 0.84-0.93) across the comparative sample. Calibration suggested good coherence between predicted and observed probabilities. Decision curve analysis also suggested this model would be clinically useful.
We developed and validated a nomogram which incorporates multiple clinically relevant variables to better identify HSIL + cases during colposcopic examination. This model may help clinicians determining next steps and in particular, around the need to refer patients for colposcopy-guided biopsies.
阴道镜检查和活检是宫颈癌筛查异常患者的标准转诊程序；然而，活检的决定存在争议。拥有预测模型可以帮助提高高级别鳞状上皮内病变或更高级别病变（HSIL+）的预测准确率，从而减少不必要的检查，并保护女性免受不必要的伤害。
本回顾性多中心研究纳入了通过阴道镜数据库识别的 5854 名患者。病例被随机分配到训练集进行开发或内部验证集进行性能评估和可比性测试。最小绝对收缩和选择算子（LASSO）回归用于减少候选预测因子的数量，并选择具有统计学意义的因素。然后，使用多变量逻辑回归建立预测模型，该模型生成发生 HSIL+的风险评分。该预测模型以列线图的形式呈现，并评估其区分能力、校准和决策曲线。该模型在 472 名连续患者中进行了外部验证，并与来自另外两家医院的 422 名患者进行了比较。
最终的预测模型包括年龄、细胞学结果、人乳头瘤病毒状态、转化区类型、阴道镜印象和病变面积。该模型在预测 HSIL+风险方面具有良好的整体区分能力，在内部验证中（AUC 为 0.92（95%CI 0.90-0.94））。外部验证发现连续样本的 AUC 为 0.91（95%CI 0.88-0.94），比较样本的 AUC 为 0.88（95%CI 0.84-0.93）。校准表明预测概率与观察概率之间具有良好的一致性。决策曲线分析也表明该模型具有临床应用价值。
我们开发并验证了一种列线图，该列线图结合了多个临床相关变量，以更好地在阴道镜检查中识别 HSIL+病例。该模型可以帮助临床医生确定下一步措施，特别是在需要决定是否将患者转介行阴道镜引导下活检时。
