Giannella Luca, Piva Francesco, Delli Carpini Giovanni, Di Giuseppe Jacopo, Grelloni Camilla, Giulietti Matteo, Sopracordevole Francesco, Giorda Giorgio, Del Fabro Anna, Clemente Nicolò, Gardella Barbara, Bogani Giorgio, Brasile Orsola, Martinello Ruby, Caretto Marta, Ghelardi Alessandro, Albanesi Gianluca, Stevenazzi Guido, Venturini Paolo, Papiccio Maria, Cannì Marco, Barbero Maggiorino, Fambrini Massimiliano, Maggi Veronica, Uccella Stefano, Spinillo Arsenio, Raspagliesi Francesco, Greco Pantaleo, Simoncini Tommaso, Petraglia Felice, Ciavattini Andrea
Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, Italy.
Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, 60131 Ancona, Italy.
Cancers (Basel). 2023 Dec 29;16(1):172. doi: 10.3390/cancers16010172.
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.
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).
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.
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.
非典型子宫内膜增生（AEH）患者并发子宫内膜癌（EC）的发生率可高达40%。一些患者特征与这种情况相关。然而，它们真正的预测能力以及相关验证尚未被发现。本研究旨在评估基于患者特征的各种模型在预测AEH女性患者发生EC方面的性能。
这是一项回顾性多机构研究，纳入了接受确定性手术的AEH女性患者。根据最终组织学结果（EC与非EC）对这些女性进行分组。将可用病例分为训练集和验证集。使用k折交叉验证，我们构建了许多预测模型，包括回归模型和人工神经网络（ANN）。
共有193/629名女性（30.7%）在子宫切除术中被诊断为EC。共有26/193名女性（13.4%）患有高危EC。回归模型和ANN模型在验证集上的预测性能分别显示曲线下平均面积为0.65和0.75。在最佳预测模型中，最常出现的患者特征是年龄、体重指数、林奇综合征、糖尿病和既往乳腺癌。这些自变量均未显示与EC女性患者的高危疾病相关。
患者特征在预测AEH患者发生EC方面表现并不理想。主要基于患者特征对AEH进行风险分层在临床上可能并不合适。
