目的:利用机器学习技术确定子宫内膜上皮内瘤变(EIN)患者并发子宫内膜癌(EC)的临床病理预测因素。
方法:回顾性分析160例活检证实为EIN的患者。我们分析了具有不同参数的多个机器学习模型(n=48)的性能,以预测术后EC的诊断。预测变量包括:奇偶校验,妊娠,抽样方法,子宫内膜厚度,年龄,身体质量指数,糖尿病,高血压,血清CA-125,术前组织学和术前激素治疗。Python\'sklearn\'库用于训练和测试模型。通过灵敏度评估模型性能,特异性,PPV,NPV和AUC。进行了五次内部交叉验证迭代,和平均值用于模型之间的比较。
结果:在术前诊断为EIN的160名妇女中,37.5%(60)的术后诊断为EC。在单变量分析中,没有显著的EIN预测因子。对于五种最佳机器学习模型,所有模型均具有高特异性(71%-88%)和低敏感性(23%-51%).Logistic回归模型的特异性最高为88%,XGBoost的灵敏度最高为51%,最高的阳性预测值为62%,阴性预测值为73%。通过随机森林模型0.646获得曲线下的最高面积。
结论:即使使用最复杂的AI算法,目前尚无法预测术前诊断为EIN的女性并发EC.由于患有EIN的女性并发EC的风险很高,手术分期可能有价值,包括前哨淋巴结评估,在最终病理上鉴定出EC的事件中,更精确地指导辅助治疗。
OBJECTIVE: To identify predictive clinico-pathologic factors for concurrent endometrial carcinoma (EC) among patients with endometrial intraepithelial neoplasia (EIN) using machine learning.
METHODS: a retrospective analysis of 160 patients with a biopsy proven EIN. We analyzed the performance of multiple machine learning models (n = 48) with different parameters to predict the diagnosis of postoperative EC. The prediction variables included: parity, gestations, sampling method, endometrial thickness, age, body mass index, diabetes, hypertension, serum CA-125, preoperative histology and preoperative hormonal therapy. Python \'sklearn\' library was used to train and test the models. The model performance was evaluated by sensitivity, specificity, PPV, NPV and AUC. Five iterations of internal cross-validation were performed, and the mean values were used to compare between the models.
RESULTS: Of the 160 women with a preoperative diagnosis of EIN, 37.5% (60) had a post-op diagnosis of EC. In univariable analysis, there were no significant predictors of EIN. For the five best machine learning models, all the models had a high specificity (71%-88%) and a low sensitivity (23%-51%). Logistic regression model had the highest specificity 88%, XG Boost had the highest sensitivity 51%, and the highest positive predictive value 62% and negative predictive value 73%. The highest area under the curve was achieved by the random forest model 0.646.
CONCLUSIONS: Even using the most elaborate AI algorithms, it is not possible currently to predict concurrent EC in women with a preoperative diagnosis of EIN. As women with EIN have a high risk of concurrent EC, there may be a value of surgical staging including sentinel lymph node evaluation, to more precisely direct adjuvant treatment in the event EC is identified on final pathology.