A nomogram for predicting recurrence in endometrial cancer patients: a population-based analysis.
A nomogram for predicting recurrence in endometrial cancer patients: a population-based analysis.
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DOI：
10.3389/fendo.2023.1156169
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发表时间：
2023
影响因子：
5.2
通讯作者：
中科院分区：
文献类型：
--
作者：
子宫内膜癌复发是导致死亡率升高的主要因素之一，目前尚缺乏预测模型。本研究旨在建立预测子宫内膜癌患者复发的诺模图预测模型。筛查2008-2018年来南京鼓楼医院就诊的517例子宫内膜癌患者。将所有数据列为训练组，然后将70%和60%随机分为验证1组和验证2组。采用单变量、多变量Logistic回归、逐步回归等方法进行诺模图变量选择。通过一致性指数(c-index)、受试者工作特征曲线下面积(AUC)随时间的变化和校准曲线函数来评价诺模图的识别和校准。通过决策曲线分析(DCA)、净重分类指数(NRI)、综合判别率改进(IDI)，比较和量化了诺模图和ESMO-ESGO-ESTRO模型预测肿瘤复发的净效益。筛选8个变量，建立子宫内膜癌复发的诺模图预测模型。C指数(对于训练队列和验证队列)和随时间变化的AUC表现出良好的诺模图的区分能力。校准图显示，在训练和验证集中，诺模图预测与实际观测之间都有很好的一致性。我们开发并验证了子宫内膜癌复发的预测模型，以帮助临床医生评估子宫内膜癌患者的复发。
Endometrial cancer recurrence is one of the main factors leading to increased mortality, and there is a lack of predictive models. Our study aimed to establish a nomogram predictive model to predict recurrence in endometrial cancer patients. Screen 517 endometrial cancer patients who came to Nanjing Drum Tower Hospital from 2008 to 2018. All these data are listed as the training group, and then 70% and 60% are randomly divided into verification groups 1 and 2. Univariate, Multivariate logistic regression, stepwise regression were used to select variables for nomogram. Nomogram identification and calibration were evaluated by concordance index (c-index), area under receiver operating characteristic curve (AUC) over time and calibration plot Function. By decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI), we compared and quantified the net benefit of nomogram and ESMO-ESGO-ESTRO model-based prediction of tumor recurrence. A nomogram predictive model of endometrial cancer recurrence was established with the eight variables screened. The c-index (for the training cohort and for the validation cohort) and the time-dependent AUC showed good discriminative power of the nomogram. Calibration plots showed good agreement between nomogram predictions and actual observations in both the training and validation sets. We developed and validated a predictive model of endometrial cancer recurrence to assist clinicians in assessing recurrence in endometrial cancer patients.
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影响因子：
2
作者：
Pencina, Michael J.;D'Agostino, Ralph B., Sr.;Steyerberg, Ewout W.
通讯作者：
Steyerberg, Ewout W.
影响因子：
51.1
作者：
Gafita, Andrei;Calais, Jeremie;Eiber, Matthias
通讯作者：
Eiber, Matthias
影响因子：
5.8
作者：
Bestvina, Christine M.;Fleming, Gini F.
通讯作者：
Fleming, Gini F.
影响因子：
5.7
作者：
Colombo, Nicoletta;Creutzberg, Carien;Sessa, Cristiana
通讯作者：
Sessa, Cristiana
影响因子：
2
作者：
Uno, Hajime;Tian, Lu;Cai, Tianxi;Kohane, Isaac S.;Wei, L. J.
通讯作者：
Wei, L. J.