OBJECTIVE: Existing prognostic models for endometrial cancer are short of facility and effective validation. In this study, we aim to develop and validate a novel prognostic model for endometrial cancer based on clinical characteristics. METHODS: The clinical data such as age, BMI (body mass index), FIGO stage, surgical approach, myometrial invasion, grade, lymph node metastasis, pathology and menopause status were collected for constructing and validating the prognostic model from The Cancer Genome Atlas (TCGA) and Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, respectively. COX regression and the least absolute shrinkage and selection operator (LASSO) COX were applied to identify the significant predictors of overall survival (OS) and construct the prognostic model. The discrimination, calibration, and clinical usefulness of the model were evaluated in both cohorts. RESULTS: Three hundred and sixty-seven and 286 EC patients were collected for training and validation cohort, respectively. A clinical prognostic model integrating six clinical variables including age, BMI, FIGO stage, surgical approach, myometrial invasion and grade was established. K-M analysis shows a significant difference between the low- and high-risk groups. The area under the receiver operating characteristic curve (AUC-ROC) was 0.775 (95% CI, 0.708 to 0.843) and 0.870 (95% CI, 0.758 to 0.982) for the training and validation cohorts which indicating reliable discrimination. The calibration curve revealed excellent predictive accuracy and the Hosmer-Lemeshow test also verified this. Decision curve analysis (DCA) for the prognostic model indicated that it would add more benefits than either the detect-all-patients scheme or the detect-none scheme. In addition, our model has a superior AUC comparing with any single factor as predicting OS. CONCLUSION: Our predictive model offers a convenient and accurate tool for clinicians to estimate the prognosis of EC patients.
OBJECTIVE: Existing prognostic models for endometrial cancer are short of facility and effective validation. In this study, we aim to develop and validate a novel prognostic model for endometrial cancer based on clinical characteristics. METHODS: The clinical data such as age, BMI (body mass index), FIGO stage, surgical approach, myometrial invasion, grade, lymph node metastasis, pathology and menopause status were collected for constructing and validating the prognostic model from The Cancer Genome Atlas (TCGA) and Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, respectively. COX regression and the least absolute shrinkage and selection operator (LASSO) COX were applied to identify the significant predictors of overall survival (OS) and construct the prognostic model. The discrimination, calibration, and clinical usefulness of the model were evaluated in both cohorts. RESULTS: Three hundred and sixty-seven and 286 EC patients were collected for training and validation cohort, respectively. A clinical prognostic model integrating six clinical variables including age, BMI, FIGO stage, surgical approach, myometrial invasion and grade was established. K-M analysis shows a significant difference between the low- and high-risk groups. The area under the receiver operating characteristic curve (AUC-ROC) was 0.775 (95% CI, 0.708 to 0.843) and 0.870 (95% CI, 0.758 to 0.982) for the training and validation cohorts which indicating reliable discrimination. The calibration curve revealed excellent predictive accuracy and the Hosmer-Lemeshow test also verified this. Decision curve analysis (DCA) for the prognostic model indicated that it would add more benefits than either the detect-all-patients scheme or the detect-none scheme. In addition, our model has a superior AUC comparing with any single factor as predicting OS. CONCLUSION: Our predictive model offers a convenient and accurate tool for clinicians to estimate the prognosis of EC patients.
Authors: Lindsey A Torre; Farhad Islami; Rebecca L Siegel; Elizabeth M Ward; Ahmedin Jemal Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-02-21 Impact factor: 4.254
Authors: Vivian E von Gruenigen; Chunqiao Tian; Heidi Frasure; Steven Waggoner; Henry Keys; Richard R Barakat Journal: Cancer Date: 2006-12-15 Impact factor: 6.860
Authors: Jorge P Orezzoli; Stefano Sioletic; Alexander Olawaiye; Esther Oliva; Marcela G del Carmen Journal: Gynecol Oncol Date: 2009-04-05 Impact factor: 5.482
Authors: Alexander Melamed; Daniel J Margul; Ling Chen; Nancy L Keating; Marcela G Del Carmen; Junhua Yang; Brandon-Luke L Seagle; Amy Alexander; Emma L Barber; Laurel W Rice; Jason D Wright; Masha Kocherginsky; Shohreh Shahabi; J Alejandro Rauh-Hain Journal: N Engl J Med Date: 2018-10-31 Impact factor: 91.245
Authors: Sophia Scharl; Michael Gerken; Tim Sprötge; Karin Kronberger; Anton Scharl; Atanas Ignatov; Olaf Ortmann; Oliver Kölbl; Monika Klinkhammer-Schalke; Thomas Papathemelis Journal: Acta Oncol Date: 2021-04-27 Impact factor: 4.089