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已发表论文
基于全身免疫炎症指数和关键临床病理特征的子宫内膜癌预后列线图开发与验证
Authors Lu J, Lin Z, Tian Y, Zhou M, Ji Y
Received 25 October 2025
Accepted for publication 30 January 2026
Published 11 February 2026 Volume 2026:18 572098
DOI https://doi.org/10.2147/CMAR.S572098
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Seema Singh
Jialu Lu,1,2 Ziyi Lin,1,2 Yu Tian,3 Minghui Zhou,2 Yanqin Ji1,2
1The First Clinical Medical College, Guangdong Medical University, Zhanjiang, Guangdong, People’s Republic of China; 2Department of Gynecological Oncology, Huizhou Central People’s Hospital, Huizhou, Guangdong, People’s Republic of China; 3Research Center, Huizhou Central People’s Hospital, Guangdong Medical University, Huizhou, Guangdong, People’s Republic of China
Correspondence: Yanqin Ji, Email yanqinji8899@163.com Minghui Zhou, Email ppa666@163.com
Objective: This study aimed to develop and validate a prognostic nomogram integrating systemic immune-inflammatory index (SII) and key clinicopathological features for predicting 2-, 3-, and 5-year overall survival (OS, primary surgery to all-cause death) in patients with Endometrial Cancer (EC), subgrouped into Survivors or Non-survivors, so as to address the unmet need for low-cost, dynamic risk stratification tools in EC precision medicine.
Methods: A retrospective cohort of 341 patients with EC (per WHO 2020 criteria) from January 2015 to January 2023 was stratified randomly into training (60%, n=205) and validation (40%, n=136) sets (stratification factors: age). Independent prognostic factors were identified through LASSO and multivariate Cox regression analyses. A nomogram was constructed and evaluated using ROC curves, calibration plots, and decision curve analysis (DCA). Key indicators included pathological differentiation grade, lymphovascular space invasion (LVSI), and systemic immune-inflammatory index (SII).
Results: Four independent predictors were identified: age (HR = 1.039, 95% CI: 0.994– 1.086, P = 0.002), pathological differentiation grade (HR = 0.384, 95% CI: 0.188– 0.786, P = 0.001), LVSI (HR = 4.208, 95% CI: 1.523– 11.625, P = 0.001), and SII (HR = 1.001, 95% CI: 1.001– 1.002, P < 0.001). The nomogram demonstrated excellent discrimination, with AUCs of 0.881, 0.883, and 0.874 for 2-, 3-, and 5-year OS in the training cohort, and 0.870, 0.834, and 0.838 in the validation cohort.
Conclusion: This study successfully developed and validated a prognostic model for EC based on SII and pathological differentiation. The model significantly outperformed traditional clinical parameters and may serve as a valuable tool for early risk stratification and individualized management in clinical practice.
Keywords: endometrial cancer, prognosis, nomogram, systemic immune-inflammation index, overall survival, predictive model
1The First Clinical Medical College, Guangdong Medical University, Zhanjiang, Guangdong, People’s Republic of China; 2Department of Gynecological Oncology, Huizhou Central People’s Hospital, Huizhou, Guangdong, People’s Republic of China; 3Research Center, Huizhou Central People’s Hospital, Guangdong Medical University, Huizhou, Guangdong, People’s Republic of China
Correspondence: Yanqin Ji, Email yanqinji8899@163.com Minghui Zhou, Email ppa666@163.com
Objective: This study aimed to develop and validate a prognostic nomogram integrating systemic immune-inflammatory index (SII) and key clinicopathological features for predicting 2-, 3-, and 5-year overall survival (OS, primary surgery to all-cause death) in patients with Endometrial Cancer (EC), subgrouped into Survivors or Non-survivors, so as to address the unmet need for low-cost, dynamic risk stratification tools in EC precision medicine.
Methods: A retrospective cohort of 341 patients with EC (per WHO 2020 criteria) from January 2015 to January 2023 was stratified randomly into training (60%, n=205) and validation (40%, n=136) sets (stratification factors: age). Independent prognostic factors were identified through LASSO and multivariate Cox regression analyses. A nomogram was constructed and evaluated using ROC curves, calibration plots, and decision curve analysis (DCA). Key indicators included pathological differentiation grade, lymphovascular space invasion (LVSI), and systemic immune-inflammatory index (SII).
Results: Four independent predictors were identified: age (HR = 1.039, 95% CI: 0.994– 1.086, P = 0.002), pathological differentiation grade (HR = 0.384, 95% CI: 0.188– 0.786, P = 0.001), LVSI (HR = 4.208, 95% CI: 1.523– 11.625, P = 0.001), and SII (HR = 1.001, 95% CI: 1.001– 1.002, P < 0.001). The nomogram demonstrated excellent discrimination, with AUCs of 0.881, 0.883, and 0.874 for 2-, 3-, and 5-year OS in the training cohort, and 0.870, 0.834, and 0.838 in the validation cohort.
Conclusion: This study successfully developed and validated a prognostic model for EC based on SII and pathological differentiation. The model significantly outperformed traditional clinical parameters and may serve as a valuable tool for early risk stratification and individualized management in clinical practice.
Keywords: endometrial cancer, prognosis, nomogram, systemic immune-inflammation index, overall survival, predictive model