Abstract：
BACKGROUND: The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines.
METHODS: Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution.
RESULTS: Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %).
CONCLUSIONS: In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.
METHODS: Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution.
RESULTS: Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %).
CONCLUSIONS: In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.
摘要：
背景：这项研究旨在验证Betella算法，专注于专门针对子宫内膜癌患者的分子分析，其中分子分类根据ESGO/ESTRO/ESP2020指南改变了风险评估。
方法：在2021年3月至2023年3月之间进行的回顾性研究涉及子宫内膜癌患者接受手术和全面的分子分析。这些包括p53和错配修复蛋白免疫组织化学，以及POLE外切核酸酶结构域的DNA测序。我们将Betella算法应用于我们的人群，并评估了分子分析改变风险等级归因的患者比例。
结果：在102名患者中，97%获得了完整的分子分析。该队列显示出不同的分子分类：10.1％为POLE超突变，30.3%为错配修复缺陷，11.1%为p53异常，48.5%为非指定分子分类。在3%的病例中存在多个分类器。将分子分类整合到风险组计算中导致11.1％的患者发生风险组迁移：7由于POLE突变而转移到较低风险类别，而4由于p53改变而转移到更高的风险。应用Betella算法，我们可以节省65例（65.7％）的POLE测序和17例（17.2％）的p53免疫化学。
结论：结论：我们在我们的人群中外部验证了Betella算法。这种新提出的算法的应用使得能够分配适当的风险类别，因此,辅助治疗的适当适应症，允许其他方式可以分配的资源合理化，不仅是为了低资源环境的好处，但一般的所有设置。
方法：在2021年3月至2023年3月之间进行的回顾性研究涉及子宫内膜癌患者接受手术和全面的分子分析。这些包括p53和错配修复蛋白免疫组织化学，以及POLE外切核酸酶结构域的DNA测序。我们将Betella算法应用于我们的人群，并评估了分子分析改变风险等级归因的患者比例。
结果：在102名患者中，97%获得了完整的分子分析。该队列显示出不同的分子分类：10.1％为POLE超突变，30.3%为错配修复缺陷，11.1%为p53异常，48.5%为非指定分子分类。在3%的病例中存在多个分类器。将分子分类整合到风险组计算中导致11.1％的患者发生风险组迁移：7由于POLE突变而转移到较低风险类别，而4由于p53改变而转移到更高的风险。应用Betella算法，我们可以节省65例（65.7％）的POLE测序和17例（17.2％）的p53免疫化学。
结论：结论：我们在我们的人群中外部验证了Betella算法。这种新提出的算法的应用使得能够分配适当的风险类别，因此,辅助治疗的适当适应症，允许其他方式可以分配的资源合理化，不仅是为了低资源环境的好处，但一般的所有设置。
