A Nomogram Model for Predicting Recurrence of Stage I-III Endometrial Cancer Based on Inflammation-Immunity-Nutrition...

Area of Science:

• Oncology
• Cancer Prognostics
• Biomarkers

Background:

• Endometrial cancer (EC) recurrence is a significant clinical concern.
• Accurate prognostic models are crucial for guiding treatment decisions in EC.
• The inflammation-immunity-nutrition score (IINS) has emerged as a potential prognostic indicator.

Purpose of the Study:

• To investigate the prognostic value of the inflammation-immunity-nutrition score (IINS) in patients with stage I-III endometrial cancer (EC).
• To establish a nomogram model for predicting EC recurrence by integrating IINS with traditional predictors.

Main Methods:

• A training cohort of 775 EC patients and a validation cohort of 491 EC patients were analyzed.
• The IINS was constructed preoperatively using C-reactive protein (CRP), lymphocytes (LYM), and albumin (ALB).
• Univariate and multivariate Cox regression analyses were employed to identify independent predictors for the nomogram model, which was validated internally and externally.

Main Results:

• High IINS was associated with a worse prognosis in EC patients.
• Multivariate analysis identified IINS, age, FIGO stage, pathological type, myometrial invasion, LVSI, Ki67, ER, and P53 expression as significant predictors of recurrence-free survival.
• The established nomogram model demonstrated good internal and external validation, with high C-indices (0.887 and 0.883, respectively), indicating superior prediction accuracy.

Conclusions:

• The inflammation-immunity-nutrition score (IINS) serves as a robust predictor of prognosis in endometrial cancer.
• The nomogram model incorporating IINS offers enhanced accuracy in predicting EC recurrence compared to traditional models.