Background: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. Methods: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). Results: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. Conclusions: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable.
背景：非典型子宫内膜增生（AEH）患者中并发子宫内膜癌（EC）的比例可高达40%。某些患者特征与此类并发情况存在关联，但其实际预测能力及相关验证尚未明确。本研究旨在评估基于患者特征构建的多种模型对AEH女性患者中EC的预测效能。 方法：本研究为一项回顾性多中心研究，纳入接受根治性手术的AEH女性患者。根据最终病理结果（EC组与非EC组）对患者进行分组。将可用病例划分为训练集与验证集，采用k折交叉验证方法构建了包括回归模型和人工神经网络（ANN）在内的多种预测模型。 结果：在629例患者中，共有193例（30.7%）子宫切除术后病理确诊为EC，其中26例（13.4%）为高危型EC。回归模型与ANN模型在验证集上的预测性能曲线下面积均值分别为0.65和0.75。在最优预测模型中，出现频率最高的患者特征包括年龄、体重指数、林奇综合征、糖尿病史及既往乳腺癌病史。在确诊EC的患者中，这些独立变量均未显示与高危型疾病存在关联。 结论：患者特征在预测AEH患者并发EC方面未能表现出令人满意的效能。主要基于患者特征进行AEH风险分层在临床上可能并不适宜。
肿瘤(癌症)患者之家