Development and Validation of a Prognostic Nomogram for Endometrial Cancer Based on Systemic Immune-Inflammation Index and Key Clinicopathological Features

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 ChinaCorrespondence: Yanqin Ji, Email [email protected] Minghui Zhou, Email [email protected]: 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

Authors

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2026-02-01
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and Validation of a Prognostic Nomogram for Endometrial Cancer Based on Systemic Immune-Inflammation Index and Key Clinicopathological Features

Ye Tian, Lin Z, Lu J, Zhou M et al.
SHILAP Revista de lepidopterología
Inflammatory Biomarkers in Disease Prognosis
article

Development and Validation of a Prognostic Nomogram for Endometrial Cancer Based on Systemic Immune-Inflammation Index and Key Clinicopathological Features

Ye Tian, Lin Z, Lu J, Zhou M, Ji Y
article en

Abstract

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 ChinaCorrespondence: Yanqin Ji, Email [email protected] Minghui Zhou, Email [email protected]: 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

SHILAP Revista de lepidopterología
Frontier Medical College (PK)
Openalex Percentile: Top 59%
Inflammatory Biomarkers in Disease Prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.