BACKGOUND: In 2010, the World Health Organization (WHO) modified the classification for pancreatic neuroendocrine tumours (NETs). Recently, some modifications were proposed to improve its prognostic value. The aim of this study was to test the prognostic value of both the original and the modified 2010 WHO grading systems. METHODS: One hundred and twenty consecutive patients surgically resected for well-differentiated NETs were evaluated in multivariate Cox regression models. Age, sex, hormonal status, size, lymph node ratio, stage, margin status and grading were evaluated in order to predict disease-free survival (DFS). Four models were evaluated: model 1: grading according to the 2010 WHO; model 2: modified grading with cut-off at 5% of the Ki-67 index; model 3: modified grading in which the G2 category was divided into two subgroups (2-5% and 5-20%) and model 4: the Ki-67 index as a continuous variable. Decision curve analysis (DCA) was carried out to evaluate the clinical utility of the various cut-offs. RESULTS: All the grading systems remained independent factors in predicting DFS. Model 2 (c index = 0.814 and P = 0.012) and model 3 (c index = 0.865 and P = 0.015) showed higher predictive powers with respect to model 1 (c index = 0.799). Model 4 had a high predictive value (c index 0.848, P = 0.013). Decision curve analysis confirmed that biological behaviour represented the best prognostic parameter. CONCLUSION: This study presented some limitations: single centre, retrospective design and a long period of enrolment. The result showed that, by increasing the cut-off of the G2 category to 5% or by creating two subgroups in the G2 category, it was possible to obtain a better stratification of patients.
BACKGOUND: In 2010, the World Health Organization (WHO) modified the classification for pancreatic neuroendocrine tumours (NETs). Recently, some modifications were proposed to improve its prognostic value. The aim of this study was to test the prognostic value of both the original and the modified 2010 WHO grading systems. METHODS: One hundred and twenty consecutive patients surgically resected for well-differentiated NETs were evaluated in multivariate Cox regression models. Age, sex, hormonal status, size, lymph node ratio, stage, margin status and grading were evaluated in order to predict disease-free survival (DFS). Four models were evaluated: model 1: grading according to the 2010 WHO; model 2: modified grading with cut-off at 5% of the Ki-67 index; model 3: modified grading in which the G2 category was divided into two subgroups (2-5% and 5-20%) and model 4: the Ki-67 index as a continuous variable. Decision curve analysis (DCA) was carried out to evaluate the clinical utility of the various cut-offs. RESULTS: All the grading systems remained independent factors in predicting DFS. Model 2 (c index = 0.814 and P = 0.012) and model 3 (c index = 0.865 and P = 0.015) showed higher predictive powers with respect to model 1 (c index = 0.799). Model 4 had a high predictive value (c index 0.848, P = 0.013). Decision curve analysis confirmed that biological behaviour represented the best prognostic parameter. CONCLUSION: This study presented some limitations: single centre, retrospective design and a long period of enrolment. The result showed that, by increasing the cut-off of the G2 category to 5% or by creating two subgroups in the G2 category, it was possible to obtain a better stratification of patients.
Authors: Javier A Cienfuegos; Joseba Salguero; Jorge M Núñez-Córdoba; Miguel Ruiz-Canela; Alberto Benito; Sira Ocaña; Gabriel Zozaya; Pablo Martí-Cruchaga; Fernando Pardo; José Luis Hernández-Lizoáin; Fernando Rotellar Journal: Surg Endosc Date: 2017-01-26 Impact factor: 4.584
Authors: Barbara Nuñez-Valdovinos; Alberto Carmona-Bayonas; Paula Jimenez-Fonseca; Jaume Capdevila; Ángel Castaño-Pascual; Marta Benavent; Jose Javier Pi Barrio; Alex Teule; Vicente Alonso; Ana Custodio; Monica Marazuela; Ángel Segura; Adolfo Beguiristain; Marta Llanos; Maria Purificacion Martinez Del Prado; Jose Angel Diaz-Perez; Daniel Castellano; Isabel Sevilla; Carlos Lopez; Teresa Alonso; Rocio Garcia-Carbonero Journal: Oncologist Date: 2018-01-12
Authors: C G Genç; M Falconi; S Partelli; F Muffatti; S van Eeden; C Doglioni; H J Klümpen; C H J van Eijck; E J M Nieveen van Dijkum Journal: Ann Surg Oncol Date: 2018-05-22 Impact factor: 5.344
Authors: G Lamberti; C Ceccarelli; N Brighi; I Maggio; D Santini; C Mosconi; C Ricci; G Biasco; D Campana Journal: Gastroenterol Res Pract Date: 2017-10-29 Impact factor: 2.260