A novel preoperative scoring system based on 18-FDG PET-CT for predicting lymph node metastases in patients with high-risk endometrial cancer

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis

BACKGROUND

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer.

OBJECTIVE

The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer.

DESIGN

This study is a systematic review.

DATA SOURCES AND METHODS

We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST).

RESULTS

A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer.

CONCLUSION

A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed.

REGISTRATION

None.

Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer

Background

Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC.

Methods

We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences.

Results

Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status.

Conclusion

Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08935-w.

Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis

BACKGROUND

In endometrial carcinoma (EC), preoperative classification is based on histopathological criteria, with only moderate diagnostic performance for the risk of lymph node metastasis (LNM). So far, existing molecular classification systems have not been evaluated for prediction of LNM. Optimized use of clinical biomarkers as recommended by international guidelines might be a first step to improve tailored treatment, awaiting future molecular biomarkers.

AIM

To determine the diagnostic accuracy of preoperative clinical biomarkers for the prediction of LNM in endometrial cancer.

METHODS

A systematic review was performed according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies identified in MEDLINE and EMBASE were selected by two independent reviewers. Included biomarkers were based on recommended guidelines (cancer antigen 125 [Ca-125], lymphadenopathy on magnetic resonance imaging, computed tomography, and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography [¹⁸FDG PET-CT]) or obtained by physical examination (body mass index, cervical cytology, blood cell counts). Pooled sensitivity, specificity, area under the curve (AUC), and likelihood ratios were calculated with bivariate random-effects meta-analysis. Likelihood ratios were classified into small (0.5-1.0 or 1-2.0), moderate (0.2-0.5 or 2.0-5.0) or large (0.1-0.2 or ≥ 5.0) impact.

RESULTS

Eighty-three studies, comprising 18,205 patients, were included. Elevated Ca-125 and thrombocytosis were associated with a moderate increase in risk of LNM; lymphadenopathy on imaging with a large increase. Normal Ca-125, cytology, and no lymphadenopathy on ¹⁸FDG PET-CT were associated with a moderate decrease. AUCs were above 0.75 for these biomarkers. Other biomarkers had an AUC <0.75 and incurred only small impact.

CONCLUSION

Ca-125, thrombocytosis, and imaging had a large and moderate impact on risk of LNM and could improve preoperative risk stratification.

IMPLICATIONS FOR PRACTICE

Routine lymphadenectomy in clinical early-stage endometrial carcinoma does not improve outcome and is associated with 15%-20% surgery-related morbidity, underlining the need for improved preoperative risk stratification. New molecular classification systems are emerging but have not yet been evaluated for the prediction of lymph node metastasis. This article provides a robust overview of diagnostic performance of all clinical biomarkers recommended by international guidelines. Based on these, at least measurement of cancer antigen 125 serum level, assessment of thrombocytosis, and imaging focused on lymphadenopathy should complement current preoperative risk stratification in order to better stratify these patients by risk.