Biomarkers Associated with Lymph Nodal Metastasis in Endometrioid Endometrial Carcinoma

Mairé, Mathilde; Bourdon, Aurélien; Soubeyran, Isabelle; Lucchesi, Carlo; Guyon, Frederic; Babin, Guillaume; Floquet, Anne; Petit, A.; Baud, Jessica; Velasco, Valérie; Querleu, Denis; Croce, Sabrina

doi:10.3390/cancers14092188

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…A previous found that metabolic disorders are closely associated with tumor stage, grade, lymph-vascular space invasion (LVSI) and LNM of EC, therefore representing an independent risk factor of EEC (13). Previous studies analyzed stage I and II cases without adnexal pathological factors, and found that patients with type I EC without depth of MI ≥1/2 had a significant risk of ovarian metastasis and LNM (14,15). However, the association between metabolic abnormalities and MI is not clear.…”

Section: Discussionmentioning

confidence: 99%

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

2023

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The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

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Section: Discussionmentioning

confidence: 99%

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

2023

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“…Another study proposed a panel of eight gene biomarkers for predicting LNM in patients with early stage endometrial cancer ( 34 ). The activation of several pathways involved in biologic processes, such as epithelial–mesenchymal transition, extracellular matrix formation and smooth muscle contraction could be considered ( 35 ). Although obtaining molecular signatures at a preoperative stage is currently a challenge, molecular features provide assessment opportunities in addition to those of clinical variables, and their complementary integration might lead to a new scoring system with better predictive performance for LNM in endometrial cancer.…”

Section: Discussionmentioning

confidence: 99%

Use of clinical variables for preoperative prediction of lymph node metastasis in endometrial cancer

Ueno,

Yoshida,

Nojiri

et al. 2023

Japanese Journal of Clinical Oncology

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Objective Endometrial cancer is the most common gynaecological cancer, and most patients are identified during early disease stages. Noninvasive evaluation of lymph node metastasis likely will improve the quality of clinical treatment, for example, by omitting unnecessary lymphadenectomy. Methods The study population comprised 611 patients with endometrial cancer who underwent lymphadenectomy at four types of institutions, comprising seven hospitals in total. We systematically assessed the association of 18 preoperative clinical variables with postoperative lymph node metastasis. We then constructed statistical models for preoperative lymph node metastasis prediction and assessed their performance with a previously proposed system, in which the score was determined by counting the number of high-risk variables among the four predefined ones. Results Of the preoperative 18 variables evaluated, 10 were significantly associated with postoperative lymph node metastasis. A logistic regression model achieved an area under the curve of 0.85 in predicting lymph node metastasis; this value is significantly higher than that from the previous system (area under the curve, 0.74). When we set the false-negative rate to ~1%, the new predictive model increased the rate of true negatives to 21%, compared with 6.8% from the previous one. We also provide a spreadsheet-based tool for further evaluation of its ability to predict lymph node metastasis in endometrial cancer. Conclusions Our new lymph node metastasis prediction method, which was based solely on preoperative clinical variables, performed significantly better than the previous method. Although additional evaluation is necessary for its clinical use, our noninvasive system may help improve the clinical treatment of endometrial cancer, complementing minimally invasive sentinel lymph node biopsy.

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The KAT6B::KANSL1 Fusion Defines a New Uterine Sarcoma With Hybrid Endometrial Stromal Tumor and Smooth Muscle Tumor Features

Trecourt,

Azmani,

Hostein

et al. 2023

Modern Pathology

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Biomarkers Associated with Lymph Nodal Metastasis in Endometrioid Endometrial Carcinoma

Cited by 5 publications

References 48 publications

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

Use of clinical variables for preoperative prediction of lymph node metastasis in endometrial cancer

The KAT6B::KANSL1 Fusion Defines a New Uterine Sarcoma With Hybrid Endometrial Stromal Tumor and Smooth Muscle Tumor Features

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