Development and validation of a nomogram involving immunohistochemical markers for prediction of recurrence in early low-risk endometrial cancer

作者全名："Kong, Wei; Tu, Yuan; Jiang, Peng; Huang, Yuzhen; Zhang, Jingni; Jiang, Shan; Li, Ning; Yuan, Rui"

作者地址："[Kong, Wei; Tu, Yuan; Jiang, Peng; Huang, Yuzhen; Zhang, Jingni; Jiang, Shan; Li, Ning; Yuan, Rui] Chongqing Med Univ, Affiliated Hosp 1, Dept Gynecol, Chongqing, Peoples R China; [Kong, Wei] Guiqian Int Gen Hosp, Dept Gynecol, Guiyang, Guizhou, Peoples R China"

通信作者："Yuan, R (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Gynecol, Chongqing, Peoples R China."

来源：INTERNATIONAL JOURNAL OF BIOLOGICAL MARKERS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000868870600001

JCR分区：Q4

影响因子：2

年份：2022

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期号：　

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文献类型：Article; Early Access

关键词：Nomogram model; immunohistochemical markers; classical parameters; early low-risk endometrial cancer; recurrence

摘要："Background The purpose of this study was to construct a nomogram based on classical parameters and immunohistochemical markers to predict the recurrence of early low-risk endometrial cancer patients. Methods A total of 998 patients with early low-risk endometrial cancer who underwent primary surgical treatment were enrolled (668 in the training cohort, 330 in the validation cohort). Prognostic factors identified by univariate and multivariate analysis in the training cohort were used to construct the nomogram. Prediction performance of the nomogram was evaluated using the calibration curve, concordance index (C-index), and the time-dependent receiver operating characteristic curve. The cumulative incidence curve was used to describe the prognosis of patients in high-risk and low-risk groups divided by the optimal risk threshold of the model. Results In the training cohort, grade (P = 0.040), estrogen receptor (P < 0.001), progesterone receptor (P = 0.001), P53 (P = 0.004), and Ki67 (P = 0.002) were identified as independent risk factors of recurrence of early low-risk endometrial cancer, and were used to establish the nomogram. The calibration curve showed that the fitting degree of the model was good. The C-indexes of training and validation cohorts were 0.862 and 0. 827, respectively. Based on the optimal risk threshold of the nomogram, patients were split into a high-risk group and a low-risk group. The cumulative incidence curves showed that the prognosis of the high-risk group was far worse than that of the low-risk group (P < 0.001). Conclusion This nomogram, with a combination of classical parameters and immunohistochemical markers, can effectively predict recurrence in early low-risk endometrial cancer patients."

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