A nomogram for predicting overall survival in patients with endometrial carcinoma: A SEER-based study.

To construct and validate a nomogram for patients with endometrial carcinoma to predict the 3- and 5-year overall survival (OS) based on the Surveillance, Epidemiology, and End Results (SEER) database. Demographic and clinical pathologic characteristics of patients with endometrial carcinoma diagnosed between 1973 and 2015 were extracted from the SEER database. Univariate and multivariate Cox analyses were carried out to identify the independent characteristics and further included into the construction of a nomogram. Finally, concordance index and calibration curves were used to validate the nomogram. A total of 49 844 patients were enrolled into our analysis. The results of univariate Cox analysis showed that age, race, marital status, FIGO Stage, grade, and metastatic status to bone, brain, lung, or liver were significant factors. Multivariate Cox analysis was performed and it confirmed all factors as independent variables. Next, a nomogram was constructed using these independent variables in prediction of the 3- and 5-year OS. Furthermore, results with concordance indices (0.852 in training set and 0.861 in validation set) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram. The individualized nomogram demonstrated a good ability in prognostic prediction for patients with endometrial carcinoma.

Li R ，Yue Q 《-》

A nomogram for predicting overall survival in patients with Ewing sarcoma: a SEER-based study.

Zhou Z ，Wang J ，Fang L ，Ma J ，Guo M ... -  《BMC MUSCULOSKELETAL DISORDERS》

Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma.

To find the factors impacting overall survival (OS) prognosis in patients with endometrioid endometrial carcinoma (EEC) and adenocarcinoma and to establish a nomogram model to validate the 2023 International Federation of Obstetrics and Gynecology (FIGO) staging system for endometrial cancer. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) training cohort. An independent validation cohort was obtained from the First Affiliated Hospital of Anhui Medical University between 2008 and 2023. Cox regression analysis identified independent prognostic factors for OS in EEC and adenocarcinoma patients. A nomogram predicting OS was developed and validated utilizing the C-index, calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The relationship between the tumor grade and prognosis of EEC and adenocarcinoma was quantified using net reclassification improvement (NRI), propensity score matching (PSM), and Kaplan-Meier curves. Cox regression analysis identified age, race, marital status, tumor grade, tumor stage, tumor size, and chemotherapy as independent prognostic factors for OS. A nomogram for predicting OS was developed based on these factors. The C-indexes for the OS nomogram was 0.743 and 0.720 for the SEER training set and external validation set, respectively. The area under the ROC (AUC) for the OS nomogram was 0.755, 0.757, and 0.741 for the SEER data subsets and 0.844, 0.719, and 0.743 for the external validation subsets. Calibration plots showed high concordance between the nomogram-predicted and observed OS. DCA also demonstrated the clinical utility of the OS nomogram. NRI, PSM, and survival analyses revealed that tumor grade was the most important histopathological factor for EEC and adenocarcinoma prognosis. Seven independent prognostic variables for the OS of patients with EEC and adenocarcinoma were identified. The established OS nomogram has good predictive ability and clinical utility and validates the 2023 endometrial cancer FIGO staging system.

Feng Y ，Miao F ，Li Y ，Li M ，Cao Y ... -  《Cancer Medicine》

A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis.

Low-grade endometrial stromal sarcoma (LG-ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients. A total of 1172 patients confirmed to have LG-ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria-based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria-based tumor staging. The risk stratifications of the nomogram and the FIGO criteria-based tumor staging were compared. Seven variables were selected to establish the nomogram for LG-ESS. The C-index (0.814 for the training cohort and 0.837 for the validation cohort) and the time-dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5-year and 0.433 for 10-year OS prediction; validation cohort: 0.310 for 5-year and 0.383 for 10-year OS prediction) and IDI (training cohort: 0.146 for 5-year and 0.185 for 10-year OS prediction; validation cohort: 0.177 for 5-year and 0.191 for 10-year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria-based tumor staging alone (P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria-based tumor staging. A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG-ESS patients.

Wu J ，Zhang H ，Li L ，Hu M ，Chen L ，Xu B ，Song Q ... -  《Cancer Communications》

Analysis of prognostic factors of metastatic endometrial cancer based on surveillance, epidemiology, and end results database.

To explore the risk factors for survival and prognosis of patients with metastatic endometrial cancer and to build and verify a reliable prediction model. We retrospectively analyzed patients diagnosed with metastatic endometrial cancer in the US Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015. Univariate and multivariate Cox regression analyses were used to assess clinical variables impact on survival and to construct nomograms. The results of the consistency index (C-index), subject operating characteristic (ROC) curve, and calibration curve were used to evaluate the predictive ability of the nomogram. This study included 3,878 patients with metastatic endometrial cancer. In the univariate analysis, variables associated with overall survival (OS) and cancer-specific survival (CSS) included age, race, marital status, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. In the multivariate analysis, age, race, pathological type, pathological grade, T-stage, N-stage, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and lung metastasis were independent risk factors for OS and CSS (all P < 0.05). Combined with the results of the multiple factors, the 1-, 3-, 5-, and 8-year nomograms were constructed. For OS and CSS, T-stage had the greatest impact on the adverse prognosis of patients with metastatic endometrial cancer. The C-indexes of the OS and CSS nomograms in the training cohort were 0.749 (95% CI, 0.739-0.760) and 0.746 (95% CI, 0.736-0.756), respectively. The C-indices of OS and CSS in the validation cohort were 0.730 (95% CI, 0.714-0.746) and 0.728 (95% CI, 0.712-0.744), respectively. The ROC curve revealed our model's good prediction accuracy and clinical practicability. The calibration curve also confirmed the consistency between the model and actual existence. The Kaplan-Meier curves revealed statistically significant differences between the risk subgroups (P < 0.05). Our SEER-based nomograms for predicting survival in patients with metastatic endometrial cancer were helpful for the clinical evaluation of patient prognosis.

Zhang M ，Li R ，Zhang S ，Xu X ，Liao L ，Yang Y ，Guo Y ... -  《Frontiers in Surgery》