Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Jiang, Peng; Huang, Yuzhen; Tu, Yuan; Li, Ning; Kong, Wei; Di, Feiyao; Jiang, Shan; Zhang, Jingni; Yi, Qianlin; Yuan, Rui

doi:10.3389/fonc.2021.682925

Cited by 12 publications

(15 citation statements)

References 35 publications

(63 reference statements)

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“…In addition, CA125 and CA153 were confirmed to be independent risk factors for LNM in EC patients in our study, which was consistent with previous studies. [31][32][33] Moreover, our study combined the SII, CA125, CA153 and LVSI to significantly improve the efficacy of predicting LNM in EC patients.…”

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confidence: 97%

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

Lei¹,

Xu²,

Mao³

et al. 2021

JIR

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This study assessed the predictive value of the preoperative systemic immuneinflammatory index (SII) for lymph node metastasis (LNM) in endometrial cancer (EC) patients. Methods: We retrospectively included 392 EC patients between January 2013 and January 2019. Data on clinical indicators including age, body mass index (BMI), menopause, serum inflammatory immune index, serum tumor markers, history of diabetes and hypertension, stage, histological type, and myometrial invasion (MI) were collected. The association between clinical indicators and LNM was evaluated. Results:The results indicated that neutrophil (NE), monocyte (MO) counts, SII, cancer antigen 125 (CA125), cancer antigen 153 (CA153), cancer antigen 199 (CA199), and the expression of estrogen receptor (ER) and Ki67 were higher in EC patients with LNM than in those without LNM (P<0.05). Lymph vascular space invasion (LVSI) was also associated with LNM (P<0.05). Consequently, the SII, CA125, CA153 and LVSI were found to be independent risk factors for LNM, and a nomogram including these indicators was performed. The ROC curve analysis showed that compared with a single index, the combination of the SII, CA125, CA153 and LVSI significantly improved the efficiency of diagnosing LNM in EC patients (AUC=0.865, P < 0.001). Moreover, the SII was significantly associated with age, menopause, and FIGO stage (P < 0.05). Further logistic regression analysis suggested that elevated serum SII was an independent risk factor for MI and progression to a higher pathological grade in young premenopausal EC patients. In addition, elevated SII was an independent risk factor for advanced EC progression in age ≥55 or postmenopausal EC patients. Conclusion: An elevated SII is an independent risk factor for LNM in patients with EC. In addition, the SII can be used as a predictor of MI and higher pathological grade in young premenopausal EC patients.

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confidence: 97%

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

Lei¹,

Xu²,

Mao³

et al. 2021

JIR

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“…Previous models used traditional clinicopathological characteristics including LVSI, myometrial invasion, histotype, grade, age, and/or BMI (11,12). So far, results were only moderate and, currently, additional immunohistochemical markers are already frequently used in the clinic: ER, PR, L1CAM, p53, Ki67 (13,14). Some authors also included imaging information such as tumor diameter, myometrial/cervical invasion, or lymphadenopathy (15,16).…”

Section: Discussionmentioning

confidence: 99%

External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

et al. 2022

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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.

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“…The predictive accuracy according to the discrimination of the AUC criteria was 0.64 for the nomogram. Recently, several studies have managed to combine clinicopathological parameters and molecular indicators to predict lymph node metastasis in EEC ( 20 , 26 – 29 ). It was showed that incorporating molecular indicators can predict lymph node metastasis more accurately ( 20 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several studies have managed to combine clinicopathological parameters and molecular indicators to predict lymph node metastasis in EEC ( 20 , 26 – 29 ). It was showed that incorporating molecular indicators can predict lymph node metastasis more accurately ( 20 ). However, POLE and MMR are important parameters in the molecular-based classification introduced by TCGA.…”

Section: Discussionmentioning

confidence: 99%

A Model to Identify Candidates for Lymph Node Dissection Among Patients With High-Risk Endometrial Endometrioid Carcinoma According to Mayo Criteria

Chen²,

Ni³

et al. 2022

Front. Oncol.

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BackgroundThe Mayo criteria are the most widely accepted algorithm for predicting the risk of lymph node metastasis in endometrial endometrioid carcinoma (EEC). However, the clinical value of these criteria in high-risk patients is limited and inconclusive.MethodsA total of 240 patients with EEC meeting the Mayo high-risk criteria between January 1, 2015, and December 31, 2018 were included in our study. We retrospectively collected the laboratory reports, basic clinical information, clinicopathological and immunohistochemistry (IHC) findings, and the sequences of molecular pathological markers of these patients. A nomogram for predicting the likelihood of positive lymph node status was established based on these parameters.ResultsAmong the 240 patients, 17 were diagnosed with lymph node metastasis. The univariable analyses identified myometrial invasion >50%, aberrant p53 expression, microsatellite instable (MSI), and cancer antigen 125 (CA125) ≥35 U/ml as potential risk factors for lymph node metastasis. The multivariable analyses showed that aberrant p53 expression, MSI, and CA125 ≥35 U/ml were independent predictors of lymph node metastasis. The area under the curve (AUC) for the nomogram was 0.870, as compared to 0.665 for the Mayo criteria.ConclusionsOur novel prediction model effectively identifies patients at high risk for lymphatic metastasis. This model is a promising strategy for personalized surgery in patients with high risk according to the Mayo criteria.

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Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Cited by 12 publications

References 35 publications

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

A Model to Identify Candidates for Lymph Node Dissection Among Patients With High-Risk Endometrial Endometrioid Carcinoma According to Mayo Criteria

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