Dataset Information

Development and Validation of an Individualized Nomogram for Predicting Survival in Patients with Esophageal Carcinoma after Resection.

ABSTRACT: An accurate estimation of prognosis of the esophageal carcinoma patients after surgery is urgently needed. Clinical nomogram has been developed to quantify risk by incorporating prognostic factors for individual patient. Based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2013, a total of 4566 patients were selected. Of those, 3198 patients were assigned to training set to construct the nomogram, which incorporated age, gender, histology, grade, T stage, N stage, nodes examined, radiation and chemotherapy. The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The C-index of the nomogram was 0.71(95%CI 0.70-0.72), which was statistically higher than the TNM staging system. The results were then validated using bootstrap resampling and a validation set of 1368 patients in the SEER database. Besides, in the esophageal squamous cell carcinoma and esophageal adenocarcinoma subgroups, the nomogram discrimination was superior to the TNM staging system. It is likely that these results would play a supplementary role in the current staging system and help to identify the high risk population after surgery.

SUBMITTER: Du F

PROVIDER: S-EPMC7196250 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and Validation of an Individualized Nomogram for Predicting Survival in Patients with Esophageal Carcinoma after Resection.

Du Feng F Sun Zhiwei Z Jia Jun J Yang Ying Y Yu Jing J Shi Youwu Y Jia Bo B Zhao Jiuda J Zhang Xiaodong X

Journal of Cancer 20200406 14

An accurate estimation of prognosis of the esophageal carcinoma patients after surgery is urgently needed. Clinical nomogram has been developed to quantify risk by incorporating prognostic factors for individual patient. Based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2013, a total of 4566 patients were selected. Of those, 3198 patients were assigned to training set to construct the nomogram, which incorporated age, gender, histology, grade, T stage, N stage ...[more]

PMID: 32368284

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Project description:Purpose This study aims to develop and validate a prediction model for non-operative, epidermal growth factor receptor (EGFR)-positive, locally advanced elderly esophageal cancer (LAEEC). Methods A total of 80 EGFR-positive LAEEC patients were included in the study. All patients underwent radiotherapy, while 41 cases received icotinib concurrent systemic therapy. A nomogram was established using univariable and multivariable Cox analyses. The model’s efficacy was assessed through area under curve (AUC) values, receiver operating characteristic (ROC) curves at different time points, time-dependent AUC (tAUC), calibration curves, and clinical decision curves. Bootstrap resampling and out-of-bag (OOB) cross-validation methods were employed to verify the model’s robustness. Subgroup survival analysis was also conducted. Results Univariable and multivariable Cox analyses revealed that icotinib, stage, and ECOG score were independent prognostic factors for LAEEC patients. The AUCs of model-based prediction scoring (PS) for 1-, 2-, and 3-year overall survival (OS) were 0.852, 0.827, and 0.792, respectively. Calibration curves demonstrated that the predicted mortality was consistent with the actual mortality. The time-dependent AUC of the model exceeded 0.75, and the internal cross-validation calibration curves showed good agreement between predicted and actual mortality. Clinical decision curves indicated that the model had a substantial net clinical benefit within a threshold probability range of 0.2 to 0.8. Model-based risk stratification analysis demonstrated the model’s excellent ability to distinguish survival risk. Further subgroup analyses showed that icotinib significantly improved survival in patients with stage III and ECOG score of 1 (HR 0.122, P<0.001). Conclusions Our nomogram model effectively predicts the overall survival of LAEEC patients, and the benefits of icotinib were found in the clinical stage III population with good ECOG scores.

Project description:Male breast cancer (MBC) is rare, and most patients are diagnosed at an advanced stage. We aimed to develop a reliable nomogram to predict breast cancer-specific survival (BCSS) for MBC patients, thus helping clinical diagnosis and treatment. Based on data from the Surveillance, Epidemiology, and End Results (SEER) database, 2,451 patients diagnosed with MBC from 2010 to 2015 were selected for this study. They were randomly assigned to either a training cohort (n = 1715) or a validation cohort (n = 736). The Multivariate Cox proportional hazards regression analysis was used to determine the independent prognostic factors, which were then utilized to build a nomogram for predicting 3- and 5-year BCSS. The discrimination and calibration of the new model was evaluated using the Concordance index (C-index) and calibration curves, while its accuracy and benefits were assessed by comparing it to the traditional AJCC staging system using the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and the decision curve analysis (DCA). Multivariate models revealed that age, AJCC stage, ER status, PR status, and surgery all showed a significant association with BCSS. A nomogram based on these variables was constructed to predict survival in MBC patients. Compared to the AJCC stage, the C-index (training group: 0.840 vs. 0.775, validation group: 0.818 vs. 0.768), the areas under the receiver operating characteristic curve of the training set (3-year AUC: 0.852 vs. 0.778, 5-year AUC: 0.841 vs. 0.774) and the validation set (3-year AUC: 0.778 vs. 0.752, 5-year AUC: 0.852 vs. 0.794), and the calibration plots of this model all exhibited better performance. Additionally, the NRI and IDI confirmed that the nomogram was a great prognosis tool. Finally, the 3- and 5-year DCA curves yielded larger net benefits than the traditional AJCC stage. In conclusion, we have successfully established an effective nomogram to predict BCSS in MBC patients, which can assist clinicians in determining the appropriate therapy strategies for individual male patients.

Dataset Information

Development and Validation of an Individualized Nomogram for Predicting Survival in Patients with Esophageal Carcinoma after Resection.

Publications

Development and Validation of an Individualized Nomogram for Predicting Survival in Patients with Esophageal Carcinoma after Resection.

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