Dataset Information

Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer.

ABSTRACT: Patients with high-grade endometrial carcinoma (EC) have an increased risk of tumor spread and lymph node metastasis (LNM). Preoperative imaging and CA125 can be used in work-up. As data on cancer antigen 125 (CA125) in high-grade EC are limited, we aimed to study primarily the predictive value of CA125, and secondarily the contributive value of computed tomography (CT) for advanced stage and LNM. Patients with high-grade EC (n = 333) and available preoperative CA125 were included retrospectively. The association of CA125 and CT findings with LNM was analyzed by logistic regression. Elevated CA125 ((>35 U/mL), (35.2% (68/193)) was significantly associated with stage III-IV disease (60.3% (41/68)) compared with normal CA125 (20.8% (26/125), [p < 0.001]), and with reduced disease-specific-(DSS) (p < 0.001) and overall survival (OS) (p < 0.001). The overall accuracy of predicting LNM by CT resulted in an area under the curve (AUC) of 0.623 (p < 0.001) independent of CA125. Stratification by CA125 resulted in an AUC of 0.484 (normal), and 0.660 (elevated). In multivariate analysis elevated CA125, non-endometrioid histology, pathological deep myometrial invasion ≥50%, and cervical involvement were significant predictors of LNM, whereas suspected LNM on CT was not. This shows that elevated CA125 is a relevant independent predictor of advanced stage and outcome specifically in high-grade EC.

SUBMITTER: Lombaers MS

PROVIDER: S-EPMC10177432 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer.

Lombaers Marike S MS Cornel Karlijn M C KMC Visser Nicole C M NCM Bulten Johan J Küsters-Vandevelde Heidi V N HVN Amant Frédéric F Boll Dorry D Bronsert Peter P Colas Eva E Geomini Peggy M A J PMAJ Gil-Moreno Antonio A van Hamont Dennis D Huvila Jutta J Krakstad Camilla C Kraayenbrink Arjan A AA Koskas Martin M Mancebo Gemma G Matías-Guiu Xavier X Ngo Huy H Pijlman Brenda M BM Vos Maria Caroline MC Weinberger Vit V Snijders Marc P L M MPLM van Koeverden Sebastiaan W SW Enitec-Consortium Haldorsen Ingfrid S IS Reijnen Casper C Pijnenborg Johanna M A JMA

Cancers 20230504 9

Patients with high-grade endometrial carcinoma (EC) have an increased risk of tumor spread and lymph node metastasis (LNM). Preoperative imaging and CA125 can be used in work-up. As data on cancer antigen 125 (CA125) in high-grade EC are limited, we aimed to study primarily the predictive value of CA125, and secondarily the contributive value of computed tomography (CT) for advanced stage and LNM. Patients with high-grade EC (n = 333) and available preoperative CA125 were included retrospectivel ...[more]

PMID: 37174070

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Project description:IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

Project description:BACKGROUND:Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS:Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. CONCLUSIONS:In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.

Project description:BACKGROUND:Preoperative evaluations aiming to assess high-risk features in clinical stage 1 endometrial cancer patients are crucial to refer these patients to gynecologic oncologists. Cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) have been reported in endometrial cancer patients with poor prognostic factors. AIM:To evaluate the association between preoperative levels of CA125 and HE4 and high-risk features and establish optimal cut-off values in clinical stage 1 endometrial cancer. METHODS:A retrospective study was conducted in clinical stage 1 endometrial cancer patients who underwent primary surgery between January 2013 and December 2018. A total of 128 patients had preoperative serum CA125 and HE4 measurements. High-risk features included grade 3 tumors, large tumor sizes (more than 2 cm), deep myometrial invasion (more than 50%), lymphovascular space invasion (LVSI), cervical involvement, extrauterine involvement and node metastasis. Receiver operating characteristic (ROC) curves were generated to analyze the optimal cut-off values. RESULTS:The mean age of the patients was 57.4 years, and 69.5% of them were postmenopausal. Most patients presented with stage I disease (67.2%) and had the endometrioid subtype (97.7%). The median CA125 and HE4 levels in all patients were 22.1 U/mL and 104.7 pmol/L, respectively. CA125 and HE4 levels were significantly elevated in those with large tumor sizes, deep myometrial invasion, LVSI, extrauterine metastasis, and advanced stage, but node metastasis was associated with elevated CA125 only. According to the ROC curve, both serum markers had statistical significance for the prediction of high-risk features only in postmenopausal patients, with an optimal cut-off value of 20 U/mL for CA125 [area under the concentration-time curve (AUC) = 0.72, P = 0.002] and 113 pmol/L for HE4 (AUC = 0.70, P = 0.006). The combination of both serum markers had 80% sensitivity and 64.4% positive predictive value. Significantly worse 5-year disease-free survival was observed in patients with high levels of CA125 and HE4 (78.4% and 100%, respectively; P = 0.01). CONCLUSION:Preoperative CA125 levels greater than 20 U/mL or HE4 levels greater than 113 pmol/L are associated with an increased risk of having high-risk features and present as prognostic factors in clinical stage 1 postmenopausal endometrial cancer patients. This information is helpful for general gynecologists to refer high-risk patients to gynecologic oncologists to perform complete surgical staging.

Dataset Information

Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer.

Publications

Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer.

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