Sofiane Bendifallah1,2, Geoffroy Canlorbe3, Enora Laas3, Florence Huguet4, Charles Coutant5, Delphine Hudry5, Olivier Graesslin6, Emilie Raimond6, Cyril Touboul7, Pierre Collinet8, Annie Cortez9, Géraldine Bleu8, Emile Daraï3,10,11, Marcos Ballester3,10,11. 1. Department of Obstetrics and Gynaecology, Tenon University Hospital, University Pierre and Marie Curie, Paris 6, France. sofiane.bendifallah@yahoo.fr. 2. INSERM UMR S 707, "Epidemiology, Information Systems, Modeling", University Pierre and Marie Curie, Paris, France. sofiane.bendifallah@yahoo.fr. 3. Department of Obstetrics and Gynaecology, Tenon University Hospital, University Pierre and Marie Curie, Paris 6, France. 4. Department of Radiation Oncology, Tenon University Hospital, University Pierre and Marie Curie, Paris 6, France. 5. Centre de Lutte Contre le Cancer Georges François Leclerc, Dijon, France. 6. Department of Obstetrics and Gynaecology, Institute Alix de Champagne University Hospital, Reims, France. 7. Department of Obstetrics and Gynecology, Centre Hospitalier Intercommunal, Créteil, France. 8. Department of Gynecological Surgery, Jeanne de Flandre University Hospital, Lille, France. 9. Department of Pathology, Tenon University Hospital, University Pierre and Marie Curie, Paris, France. 10. INSERM UMR S 938, University Pierre et Marie Curie, Paris 6, France. 11. Institut Universitaire de Cancérologie (IUC), University Pierre and Marie Curie, Paris 6, France.
Abstract
BACKGROUND: This study aimed to develop a predictive model using histopathologic characteristics of early-stage type 1 endometrial cancer (EC) to identify patients at high risk for lymph node (LN) metastases. METHODS: The data of 523 patients who received primary surgical treatment between January 2001 and December 2012 were abstracted from a prospective multicenter database (training set). A multivariate logistic regression analysis of selected prognostic features was performed to develop a nomogram predicting LN metastases. To assess its accuracy, an internal validation technique with a bootstrap approach was adopted. The optimal threshold in terms of clinical utility, sensitivity, specificity, negative predictive values (NPVs), and positive predictive values (PPVs) was evaluated by the receiver-operating characteristics (ROC) curve area and the Youden Index. RESULTS: Overall, the LN metastasis rate was 12.4 % (65/523). Lymph node metastases were associated with histologic grade, tumor diameter, depth of myometrial invasion, and lymphovascular space involvement status. These variables were included in the nomogram. Discrimination of the model was 0.83 [95 % confidence interval (CI) 0.80-0.85] in the training set. The area under the curve ROC for predicting LN metastases after internal validation was 0.82 (95 % CI 0.80-0.84). The Youden Index provided a value of 0.2, corresponding to a cutoff of 140 points (total score in the algorithm). At this threshold, the model had a sensitivity of 0.73 (95 % CI 0.62-0.83), a specificity of 0.84 (95 % CI 0.82-0.85), a PPV of 0.40 (95 % CI 0.34-0.45), and an NPV of 0.95 (95 % CI 0.94-0.97). CONCLUSION: The results show that the risk of LN metastases can be predicted correctly so that patients at high risk can benefit from adapted surgical treatment.
BACKGROUND: This study aimed to develop a predictive model using histopathologic characteristics of early-stage type 1 endometrial cancer (EC) to identify patients at high risk for lymph node (LN) metastases. METHODS: The data of 523 patients who received primary surgical treatment between January 2001 and December 2012 were abstracted from a prospective multicenter database (training set). A multivariate logistic regression analysis of selected prognostic features was performed to develop a nomogram predicting LN metastases. To assess its accuracy, an internal validation technique with a bootstrap approach was adopted. The optimal threshold in terms of clinical utility, sensitivity, specificity, negative predictive values (NPVs), and positive predictive values (PPVs) was evaluated by the receiver-operating characteristics (ROC) curve area and the Youden Index. RESULTS: Overall, the LN metastasis rate was 12.4 % (65/523). Lymph node metastases were associated with histologic grade, tumor diameter, depth of myometrial invasion, and lymphovascular space involvement status. These variables were included in the nomogram. Discrimination of the model was 0.83 [95 % confidence interval (CI) 0.80-0.85] in the training set. The area under the curve ROC for predicting LN metastases after internal validation was 0.82 (95 % CI 0.80-0.84). The Youden Index provided a value of 0.2, corresponding to a cutoff of 140 points (total score in the algorithm). At this threshold, the model had a sensitivity of 0.73 (95 % CI 0.62-0.83), a specificity of 0.84 (95 % CI 0.82-0.85), a PPV of 0.40 (95 % CI 0.34-0.45), and an NPV of 0.95 (95 % CI 0.94-0.97). CONCLUSION: The results show that the risk of LN metastases can be predicted correctly so that patients at high risk can benefit from adapted surgical treatment.
Authors: Matthew M Harkenrider; Alec M Block; Kaled M Alektiar; David K Gaffney; Ellen Jones; Ann Klopp; Akila N Viswanathan; William Small Journal: Brachytherapy Date: 2016-05-31 Impact factor: 2.362