The following is a summary of “A nomogram model to predict recurrence of early-onset endometrial cancer after resection based on clinical parameters and immunohistochemical markers: a multi-institutional study,” published in the November 2024 issue of Oncology by Zheng et al.   

Early-onset endometrial cancer (EC) is diagnosed in many individuals, and identifying prognosis factors can guide treatment decisions.  

Researchers conducted a retrospective study developing a nomogram predicting recurrence-free survival (RFS) in patients with early-onset EC after resection.  

They analyzed a training dataset of 458 patients and an independent testing dataset of 170 patients. Independent risk factors for RFS were identified using Cox regression models. A nomogram was developed to predict RFS at 3- and 5-year post-hysterectomy. The predictive accuracy of the nomogram was analyzed using the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and calibration curves.  

The results showed that more than half (58.6%, 368/628) of early-onset patients with EC were diagnosed between 45 and 49 years, with a recurrence rate of approximately 10.8%. Multivariate Cox regression analyses showed that histological subtype, FIGO stage, myometrial invasion, lymphovascular space invasion (LVSI), P53 expression, and mismatch repair (MMR) status were independent prognostic factors related to RFS (all P<0.05). The nomogram predicted 3- and 5-year RFS with high accuracy. Calibration curves and the C-index demonstrated a strong correlation between predicted and actual RFS. Based on the model, patients were categorized into high- and low-risk groups for recurrence.  

They concluded that the developed nomogram, incorporating clinical parameters and immunohistochemical markers, accurately predicts RFS in patients with early-onset EC after surgery.  

Source: frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1442489/full