Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness

Purpose: to investigate the preoperative role of ML-based classification using conventional 18F-FDG PET parameters and clinical data in predicting features of EC aggressiveness.

Methods: retrospective study, including 123 EC patients who underwent 18F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities.

Results: on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression.

Conclusions: ML-based classification using conventional 18F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.

目的：探讨基于机器学习的分类在术前利用传统18F-FDG正电子发射断层扫描（PET）参数和临床数据预测子宫内膜癌（EC）侵袭性特征中的先导作用。
方法：一项回顾性研究，纳入了123例接受术前分期18F-FDG PET检查（时间范围：2009-2021年）的子宫内膜癌患者。在原发肿瘤上计算了最大标准化摄取值（SUVmax）、平均标准化摄取值（SUVmean）、代谢肿瘤体积（MTV）和总病变葡萄糖酵解（TLG）。收集了年龄和BMI数据。从组织学中检索了组织学类型、肌层浸润（MI）、风险组、淋巴结受累（LN）和p53表达。将人群分为训练集和验证集（比例80-20%）。训练集用于选择相关参数（Mann-Whitney U检验；ROC分析）并实施机器学习模型，而验证集用于测试预测能力。
结果：在验证集上，通过单个参数和机器学习获得的最佳准确率分别为：MI特征的61%（TLG）和87%（机器学习）；风险组的71%（SUVmax）和79%（机器学习）；LN特征的72%（TLG）和83%（机器学习）；p53表达特征的45%（SUVmax；SUVmean）和73%（机器学习）。
结论：基于机器学习的分类，利用传统18F-FDG PET参数和临床数据，能够表征所研究的子宫内膜癌侵袭性特征，为子宫内膜癌患者的术前分层提供了一种无创的辅助方法。