Predicting Recurrence in Endometrial Cancer Based on a Combination of Classical Parameters and Immunohistochemical Markers

• Main Authors: Jiang P, Huang J, Deng Y, Hu J, Huang Z, Jia M, Long J, Hu Z
• Format: Article
• Language: English
• Published: Dove Medical Press 2020-08-01
• Series: Cancer Management and Research
• Subjects: classical parameters; immunohistochemical markers; endometrial cancer; predicting model; recurrence
• Online Access: https://www.dovepress.com/predicting-recurrence-in-endometrial-cancer-based-on-a-combination-of--peer-reviewed-article-CMAR

Peng Jiang,* Jin Huang,* Ying Deng, Jing Hu, Zhen Huang, Mingzhu Jia, Jiaojiao Long, Zhuoying Hu Department of Gynecology, First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhuoying HuDepartment of Gynecology, First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of ChinaTel [phone]Fax [phone]Email [e-mail]: The aim of this study was to establish a nomogram to predict the recurrence of endometrial cancer (EC) by immunohistochemical markers and clinicopathological parameters and to evaluate the discriminative power of this model.Methods: The data of 473 patients with stages I–III endometrial cancer who had received primary surgical treatment between October 2013 and May 2018 were randomly split into two sets: a training cohort and a validation cohort at a predefined ratio of 7:3. Univariate and multivariate Cox regression analysis of screening prognostic factors were performed in the training cohort (n=332) to develop a nomogram model for EC-recurrence prediction, which was further evaluated in the validation cohort (n=141).Results: Univariate analysis found that FIGO stage, histological type, histological grade, myometrial invasion, cervical stromal invasion, postoperative adjuvant treatment, and four immunohistochemical markers (Ki67, ER, PR, and p53) were associated with recurrence in EC. Multivariate analysis showed that FIGO stage, histological type, ER, and p53 were superior parameters to generate the nomogram model for recurrence prediction in EC. Recurrence-free survival was better predicted by the proposed nomogram, with a C-index value of 0.79 (95% CI 0.66– 0.92) in the validation cohort.Conclusion: This nomogram model involving immunohistochemical markers can better predict recurrence in FIGO stages I–III EC.Keywords: classical parameters, immunohistochemical markers, endometrial cancer, predicting model, recurrence