Bendifallah Sofiane, Daraï Emile, Ballester Marcos
Department of Gynecology and Obstetrics, Tenon University Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), University Pierre and Marie Curie, Institut Universitaire de Cancérologie (IUC), Paris 6, France.
INSERM UMR S 707, "Epidemiology, Information Systems, Modeling,", University Pierre and Marie Curie, Paris 6, France.
Ann Surg Oncol. 2016 Mar;23(3):975-88. doi: 10.1245/s10434-015-4924-2. Epub 2015 Nov 17.
With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.
随着诊断和治疗方式的大量新选择出现，已观察到子宫内膜癌（EC）医疗决策过程发生了转变。个体化医学的出现以及可用医学数据日益复杂，导致了几种预测模型的发展。在子宫内膜癌中，已经报道了那些临床模型（算法、列线图和风险评分系统），特别是用于对患者进行分层和亚组划分，但关于诸如淋巴结转移的最佳手术分期以及复发和生存结果评估等方面仍有各种未解决的问题。在本综述中，我们重点介绍子宫内膜癌中现有的预后和预测模型，特别关注其临床适用性。我们还讨论了此类预测模型开发的方法学方面以及将这些工具整合到临床决策中所需的步骤。未来，分子或生化标志物研究这一新兴领域可能会大幅改进预测和治疗方法。
