PURPOSE: The purpose of this study was to develop and initially validate a nomogram model in order to predict the 3-year and 5-year survival rates of neuroendocrine tumor patients. METHODS: Accordingly, 348 neuroendocrine tumor patients were enrolled as study objects, of which 244 (70%) patients were included in the training set to establish the nomogram model, while 104 (30%) patients were included in the validation set to verify the robustness of the model. First, the variables related to the survival rate were determined by univariable analysis. In addition, variables that were sufficiently significant were selected for constructing the nomogram model. Furthermore, the concordance index (C-index), receiver operating characteristic (ROC), and calibration curve analysis were used to evaluate the performance of the proposed nomogram model. The survival analysis was then used to evaluate the return to survival probability as well as the indicators of constructing the nomogram model. RESULTS: According to the multivariable analysis, lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and the number of tumor metastases were found to be independent predictors of survival rate. Moreover, the C-index results demonstrated that the model was robust in both the training set (0.891) and validation set (0.804). In addition, the ROC results further verified the robustness of the model either in the training set (AUC = 0.823) or training set (AUC = 0.768). Furthermore, the calibration curve results showed that the model can be used to predict the 3-year and 5-year survival probability of neuroendocrine tumor patients. Meaningfully, five variables were found: lymphatic metastasis (p = 0.0095), international standardized ratio (p = 0.024), prothrombin time (p = 0.0036), tumor differentiation (p = 0.0026), and the number of tumor metastases (p = 0.00096), which were all significantly related to the 3-year and 5-year survival probability of neuroendocrine tumor patients. CONCLUSION: In summary, a nomogram model was constructed in this study based on five variables (lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and number of tumor metastases), which was shown to predict the survival probability of patients with neuroendocrine tumors. Additionally, the proposed nomogram exhibited good ability in predicting survival probability, which may be easily adopted for clinical use.
PURPOSE: The purpose of this study was to develop and initially validate a nomogram model in order to predict the 3-year and 5-year survival rates of neuroendocrine tumor patients. METHODS: Accordingly, 348 neuroendocrine tumor patients were enrolled as study objects, of which 244 (70%) patients were included in the training set to establish the nomogram model, while 104 (30%) patients were included in the validation set to verify the robustness of the model. First, the variables related to the survival rate were determined by univariable analysis. In addition, variables that were sufficiently significant were selected for constructing the nomogram model. Furthermore, the concordance index (C-index), receiver operating characteristic (ROC), and calibration curve analysis were used to evaluate the performance of the proposed nomogram model. The survival analysis was then used to evaluate the return to survival probability as well as the indicators of constructing the nomogram model. RESULTS: According to the multivariable analysis, lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and the number of tumor metastases were found to be independent predictors of survival rate. Moreover, the C-index results demonstrated that the model was robust in both the training set (0.891) and validation set (0.804). In addition, the ROC results further verified the robustness of the model either in the training set (AUC = 0.823) or training set (AUC = 0.768). Furthermore, the calibration curve results showed that the model can be used to predict the 3-year and 5-year survival probability of neuroendocrine tumor patients. Meaningfully, five variables were found: lymphatic metastasis (p = 0.0095), international standardized ratio (p = 0.024), prothrombin time (p = 0.0036), tumor differentiation (p = 0.0026), and the number of tumor metastases (p = 0.00096), which were all significantly related to the 3-year and 5-year survival probability of neuroendocrine tumor patients. CONCLUSION: In summary, a nomogram model was constructed in this study based on five variables (lymphatic metastasis, international normalized ratio (INR), prothrombin time (PT), tumor differentiation, and number of tumor metastases), which was shown to predict the survival probability of patients with neuroendocrine tumors. Additionally, the proposed nomogram exhibited good ability in predicting survival probability, which may be easily adopted for clinical use.
Authors: Jeffrey A. Norton; Deshka S. Foster; Leslie H. Blumgart; George A. Poultsides; Brendan C. Visser; Douglas L. Fraker; H. Richard Alexander; Robert T. Jensen Journal: JAMA Surg Date: 2018-03-01 Impact factor: 14.766
Authors: Katiuscha Merath; Fabio Bagante; Eliza W Beal; Alexandra G Lopez-Aguiar; George Poultsides; Eleftherios Makris; Flavio Rocha; Zaheer Kanji; Sharon Weber; Alexander Fisher; Ryan Fields; Bradley A Krasnick; Kamran Idrees; Paula M Smith; Cliff Cho; Megan Beems; Carl R Schmidt; Mary Dillhoff; Shishir K Maithel; Timothy M Pawlik Journal: J Surg Oncol Date: 2018-02-15 Impact factor: 3.454
Authors: Matias Riihimäki; Akseli Hemminki; Kristina Sundquist; Jan Sundquist; Kari Hemminki Journal: Int J Cancer Date: 2016-09-09 Impact factor: 7.396
Authors: Guido Rindi; Catherine Klersy; Luca Albarello; Eric Baudin; Antonio Bianchi; Markus W Buchler; Martyn Caplin; Anne Couvelard; Jérôme Cros; Wouter W de Herder; Gianfranco Delle Fave; Claudio Doglioni; Birgitte Federspiel; Lars Fischer; Giuseppe Fusai; Francesca Gavazzi; Carsten P Hansen; Frediano Inzani; Henning Jann; Paul Komminoth; Ulrich P Knigge; Luca Landoni; Stefano La Rosa; Rita T Lawlor; Tu V Luong; Ilaria Marinoni; F Panzuto; Ulrich-Frank Pape; Stefano Partelli; Aurel Perren; Maria Rinzivillo; Corrado Rubini; Philippe Ruszniewski; Aldo Scarpa; Anja Schmitt; Giovanni Schinzari; Jean-Yves Scoazec; Fausto Sessa; Enrico Solcia; Paola Spaggiari; Christos Toumpanakis; Alessandro Vanoli; Bertram Wiedenmann; Giuseppe Zamboni; Wouter T Zandee; Alessandro Zerbi; Massimo Falconi Journal: Neuroendocrinology Date: 2018-10-09 Impact factor: 4.914