Dataset Information

Development and validation of predictive model for lymph node metastasis in endometrial cancer: a SEER analysis.

ABSTRACT:

Background

The purpose of this study was to develop a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial cancer (EC).

Methods

The clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen by a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves.

Results

The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be significant predictors of LNM were age, tumor size, histological type, myometrial invasion, cervical stromal invasion, and tumor grade in training group (N=42,558). These variables were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95% CI: 0.801-0.812), respectively. In the validation group (N=21,278), the AUC values were 0.847 (95% CI: 0.840-0.857) and 0.804 (95% CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively (P<0.01). Calibration plots showed that training and validation cohorts were well-calibrated.

Conclusions

A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC. This convenient predictive tool may help clinicians to formulate suitable individualized treatment.

SUBMITTER: Li X

PROVIDER: S-EPMC8105869 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Development and validation of predictive model for lymph node metastasis in endometrial cancer: a SEER analysis.

Li Xingchen X Cheng Yuan Y Dong Yangyang Y Zhou Jingyi J Wang Zhiqi Z Li Xiaoping X Wang Jianliu J

Annals of translational medicine 20210401 7

<h4>Background</h4>The purpose of this study was to develop a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial cancer (EC).<h4>Methods</h4>The clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen by a multivariate logistic regression analysis. Accuracy was validated for both groups usin ...[more]

PMID: 33987236

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Project description:The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Dataset Information

Development and validation of predictive model for lymph node metastasis in endometrial cancer: a SEER analysis.

Background

Methods

Results

Conclusions

Publications

Development and validation of predictive model for lymph node metastasis in endometrial cancer: a SEER analysis.

Similar Datasets

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