Quantitative Measurement of Progesterone Receptor Immunohistochemical Expression to Predict Lymph Node Metastasis in Endometrial Cancer

Estrogen/Progesterone Receptor Expression and Cancer Antigen 125 Level as Preoperative Predictors to Estimate Lymph Node Metastasis in Endometrioid Endometrial Cancer

Loss of estrogen receptor/progesterone receptor (ER/PR) in endometrial cancer (EC) is associated with tumor progression and poor outcomes. Elevated pretreatment cancer antigen 125 (CA 125) level is a risk factor for lymph node metastasis (LNM). We evaluated whether the combination of ER/PR expression and CA 125 level could be used as a biomarker to predict LNM. We retrospectively investigated patients with endometrioid EC who underwent complete staging surgery during January 2015 to December 2020. We analyzed ER/PR status using immunohistochemical staining, and quantified its expression using the sum of both ER/PR H -scores. Receiver operating characteristic curves were used to identify optimal cutoff values of H -score and CA 125 levels for predicting LNM. A nomogram for predicting LNM was constructed and validated by bootstrap resampling. In 396 patients, the optimal cutoff values of the ER/PR H -score and CA 125 were 407 (area under the receiver operating characteristic curve: 0.645, P =0.001) and 40 U/mL (area under the receiver operating characteristic curve: 0.762, P <0.001), respectively. Multivariate analysis showed that CA 125 ≥40 UmL (odds ratio: 10.02; 95% CI: 4.74-21.18) and ER/PR H -score <407 (odds ratio: 4.20; 95% CI: 1.55-11.32) were independent predictors. An LNM predictive nomogram was constructed using these 2 variables and our model yielded a negative predictive value and negative likelihood ratio of 98.3% and 0.14, respectively. ER/PR expression with pretreatment CA 125 levels can help estimate LNM risk and aid in decision-making regarding the need for lymphadenectomy in patients with endometrioid EC.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

Pretreatment carcinoembryonic antigen combined with cancer antigen-125 for predicting lymph node metastasis in endometrial carcinoma: a retrospective cohort study

PURPOSE

To investigate whether the cost-effective, pretreatment tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen-125 (CA-125) can be used to predict lymph node metastasis (LNM) in endometrioid-type endometrial cancer (EC) and to develop a predictive model.

METHODS

This was a single-center retrospective study of patients with endometrioid-type EC who underwent complete staging surgery between January 2015 and June 2022. We identified the optimal cut-off values of CEA and CA-125 for predicting LNM using receiver operating characteristic (ROC) curves. Stepwise multivariate logistic regression analysis was used to identify independent predictors. A nomogram for predicting LNM was constructed and validated by bootstrap resampling.

RESULTS

The optimal cut-off values of CEA and CA-125 were 1.4 ng/mL (area under the ROC curve (AUC) 0.62) and 40 U/mL (AUC 0.75), respectively. Multivariate analysis showed that CEA (odds ratio (OR) 1.94; 95% confidence interval (CI) 1.01-3.74) and CA-125 (OR 8.75; 95% CI 4.42-17.31) were independent predictors of LNM. Our nomogram showed adequate discrimination with a concordance index of 0.78. Calibration curves for the probability of LNM showed optimal agreement between the predicted and actual probabilities. The risk of LNM for markers below the cut-offs was 3.6%. The negative predictive value and negative likelihood ratio were 96.6% and 0.26, respectively, with moderate ability to rule out the possibility of LNM.

CONCLUSION

We report a cost-effective method of using pretreatment CEA and CA-125 levels to identify patients with endometrioid-type EC who are at a low risk for LNM, which may guide decision-making regarding aborting lymphadenectomy.