Department of Women's Health, University Hospital Tuebingen, Calwerstraße 7, 72076, Tuebingen, Germany.
Department of Radiation Oncology, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
J Cancer Res Clin Oncol. 2023 Jul;149(7):3361-3369. doi: 10.1007/s00432-022-04218-4. Epub 2022 Aug 8.
Preoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network model, showed high predictive performance. It was the aim of this study to validate ENDORISK by applying the model to a population-based case series of EC patients.
ENDORISK was applied to a retrospective cohort of women surgically treated for EC from 2003 to 2013. Prediction accuracy for LNM as well as 5-year DSS was investigated. The model's overall performance was quantified by the Brier score, discriminative performance by area under the curve (AUC).
A complete dataset was evaluable from 247 patients. 78.1% cases were endometrioid histotype. The majority of patients (n = 156;63.2%) had stage IA disease. Overall, positive lymph nodes were found in 20 (8.1%) patients. Using ENDORISK predicted probabilities, most (n = 156;63.2%) patients have been assigned to low or very low risk group with a false-negative rate of 0.6%. AUC for LNM prediction was 0.851 [95% confidence interval (CI) 0.761-0.941] with a Brier score of 0.06. For 5-year DSS the AUC was 0.698 (95% CI 0.595-0.800) as Brier score has been calculated 0.09.
We were able to successfully validate ENDORISK for prediction of LNM and 5-year DSS. Next steps will now have to focus on ENDORISK performance in daily clinical practice. In addition, incorporating TCGA-derived molecular subtypes will be of key importance for future extended use. This study may support further promoting of data-based decision-making tools for personalized treatment of EC.
新诊断子宫内膜癌（EC）患者的术前风险分层多年来一直受到仅中度预测性能的阻碍。最近，ENDORISK 是一种贝叶斯网络模型，显示出了较高的预测性能。本研究的目的是通过将该模型应用于 EC 患者的基于人群的病例系列来验证 ENDORISK。
ENDORISK 应用于 2003 年至 2013 年间接受手术治疗的 EC 女性回顾性队列。研究了预测淋巴结转移（LNM）和 5 年无病生存率（DSS）的准确性。通过 Brier 评分量化模型的整体性能，通过曲线下面积（AUC）评估判别性能。
可评估 247 例患者的完整数据集。78.1%的病例为子宫内膜样组织学类型。大多数患者（n=156；63.2%）为 IA 期疾病。总体而言，20 例（8.1%）患者发现阳性淋巴结。使用 ENDORISK 预测概率，大多数（n=156；63.2%）患者被分配到低或极低风险组，假阴性率为 0.6%。LNM 预测的 AUC 为 0.851（95%CI 0.761-0.941），Brier 评分 0.06。5 年 DSS 的 AUC 为 0.698（95%CI 0.595-0.800），Brier 评分计算为 0.09。
我们成功地验证了 ENDORISK 用于预测 LNM 和 5 年 DSS。下一步将集中在 ENDORISK 在日常临床实践中的性能。此外，纳入 TCGA 衍生的分子亚型对于未来的扩展使用将至关重要。本研究可能支持进一步推广基于数据的决策工具，以实现 EC 的个体化治疗。
