Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation

New online dynamic nomograms to predict recurrence-free and overall survival after resection of endometrial cancer: a single-institution retrospective cohort study

PURPOSE

The significant global burden of endometrial cancer (EC) and the challenges associated with predicting EC recurrence indicate the need for a dynamic prediction model. This study aimed to propose nomograms based on clinicopathological variables to predict recurrence-free survival (RFS) and overall survival (OS) after surgical resection for EC.

METHODS

This single-institution retrospective cohort study included patients who underwent surgical resection for EC. Web-based nomograms were developed to predict RFS and OS following resection for EC, and their discriminative and calibration abilities were assessed.

RESULTS

This study included 289 patients (median age, 56 years). At a median follow-up of 51.1 (range, 4.1-128.3) months, 13.5% (39/289) of patients showed relapse or died, and 10.7% (31/289) had non-endometrioid tumors (median size: 2.8 cm). Positive peritoneal cytology result (hazard ratio [HR], 35.06; 95% confidence interval [CI], 1.12-1095.64; P = 0.0428), age-adjusted Charlson comorbidity index (AACCI) (HR, 52.08; 95% CI, 12.35-219.61; P < 0.001), and FIGO (Federation of Gynecology and Obstetrics) stage IV (HR, 138.33; 95% CI, 17.38-1101.05; P < 0.001) were predictors of RFS. Similarly, depth of myometrial invasion ≥ 1/2 (HR, 1; 95% CI, 0.46-2.19; P = 0.995), AACCI (HR, 93.63; 95% CI, 14.87-589.44; P < 0.001), and FIGO stage IV (HR, 608.26; 95% CI, 73.41-5039.66; P < 0.001) were predictors of OS. The nomograms showed good predictive capability, positive discriminative ability, and calibration (RFS: 0.895 and OS: 0.891).

CONCLUSION

The nomograms performed well in internal validation when patients were stratified into prognostic groups, offering a personalized approach for risk stratification and treatment decision-making.

Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients

OBJECTIVE

Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC.

METHODS

A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram.

RESULTS

The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from -8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan-Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram.

CONCLUSION

We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.

Predictive factors of surgical site infection after hysterectomy for endometrial carcinoma: a retrospective analysis

Background

Surgical site infection (SSI) is a common postoperative complication. We aimed to analyze the potential risk factors of SSI in patients with endometrial carcinoma.

Methods

Patients with endometrial carcinoma who underwent surgery treatment in our hospital from Sept 1, 2018 to August 31, 2020 were included. We retrospectively compared the characteristics of SSI and no SSI patients, and logistic regression analyses were performed to identify the risk factors of SSI in patients with endometrial carcinoma.

Results

A total of 318 postoperative patients with endometrial carcinoma were included. The incidence of SSI in patients with endometrial carcinoma was 14.47 %. There were significant differences on the FIGO stage, type of surgery, durations of drainage, postoperative serum albumin and postoperative blood sugar (all p < 0.05), and no significant differences on the age, BMI, hypertension, diabetes, hyperlipidemia, estimated blood loss, length of hospital stay were found (all p > 0.05). FIGO stage IV (HR3.405, 95 %CI 2.132–5.625), open surgery (HR2.692, 95 %CI 1.178–3.454), durations of drainage ≥ 7 d (HR2.414,95 %CI 1.125–2.392), postoperative serum albumin < 30 g/L (HR1.912,95 %CI 1.263–2.903), postoperative blood sugar ≥ 10 mmol/L (HR1.774,95 %CI 1.102–2.534) were the independent risk factors of SSI in patients with endometrial carcinoma (all p <  0.05).

Conclusions

Measures including reasonable control of serum albumin and blood glucose levels, minimally invasive surgery as much as possible, timely assessment of drainage and early removal of the tube may be beneficial to reduce the postoperative SSI in in patients with endometrial carcinoma.

Identification of an immune-related risk signature and nomogram predicting the overall survival in patients with endometrial cancer

Objective

Aimed to construct an immune-related risk signature and nomogram predicting endometrial cancer (EC) prognosis.

Methods

An immune-related risk signature in EC was constructed using the least absolute shrinkage and selection operator regression analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A nomogram integrating the immune-related genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve to predict the overall survival (OS) of EC patients. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) R tool was used to explore the immune and stromal scores.

Results

CCL17, CTLA4, GPI, HDGF, HFE2, ICOS, IFNG, IL21R, KAL1, NR3C1, S100A2, and S100A9 were used in developing an immune-related risk signature evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had better OS (p<0.001). The area under the ROC curve (AUC) values of this model were 0.737, 0.764, and 0.782 for the 3-, 5-, and 7-year OS, respectively. A nomogram integrating the immune-related risk model and clinical features could accurately predict the OS (AUC=0.772, 0.786, and 0.817 at 3-, 5-, and 7-year OS, respectively). The 4 immune cell scores were lower in the high-risk group. Forkhead box P3 (FOXP3) and basic leucine zipper ATF-like transcription factor (BATF) showed a potential significant role in the immune-related risk signature.

Conclusion

Twelve immune-related genes signature and nomogram for assessing the OS of patients with EC had a good practical value.