Tong Yifan1, Li Zheyong1, Chen Miaoqin2, Shi Liang1, Cai Xiujun3. 1. Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. 2. Department of Biological Treatment Research Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. 3. Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. Electronic address: srrsh_cxj@zju.edu.cn.
Abstract
BACKGROUND:Gallbladder cancer (GBC) is a life-threatening disease with a poor prognosis worldwide. Although several risk factors for survival have been identified, an ideal model for predicting prognosis has still not been developed due to the low incidence of GBC. This study aims to solve this dilemma by attempting to develop an efficient survival prediction model for GBC. METHODS: This is a retrospective study. From January 2009 to June 2016, 164 patients with a confirmed histological diagnosis of gallbladder adenocarcinoma were enrolled in this study. The cohort was randomly divided into two cohorts, the development cohort (n = 110) and validation cohort (n = 54). On the basis of the risk factors identified in the development cohort, a nomogram-based predictive model (P-risk Plus), composed of carbohydrate antigen 199 and pathological characteristics, was established for prognosis. RESULTS: In this model, the calibration curves for the 1-, 2-, and 3-year survival probabilities were well-matched with the actual survival rates. In addition, the highest C-index and best decision curve analysis were able to be obviously determined. Meanwhile, the P-risk Plus model result yielded a better fit for survival between the development and validation groups. CONCLUSION: Compared with conventional tumor stages, our nomogram-based P-risk Plus model for gallbladder adenocarcinoma has a better predictive capacity and thereby has a better potential to facilitate decision-making clinically.
RCT Entities:
BACKGROUND: Gallbladder cancer (GBC) is a life-threatening disease with a poor prognosis worldwide. Although several risk factors for survival have been identified, an ideal model for predicting prognosis has still not been developed due to the low incidence of GBC. This study aims to solve this dilemma by attempting to develop an efficient survival prediction model for GBC. METHODS: This is a retrospective study. From January 2009 to June 2016, 164 patients with a confirmed histological diagnosis of gallbladder adenocarcinoma were enrolled in this study. The cohort was randomly divided into two cohorts, the development cohort (n = 110) and validation cohort (n = 54). On the basis of the risk factors identified in the development cohort, a nomogram-based predictive model (P-risk Plus), composed of carbohydrate antigen 199 and pathological characteristics, was established for prognosis. RESULTS: In this model, the calibration curves for the 1-, 2-, and 3-year survival probabilities were well-matched with the actual survival rates. In addition, the highest C-index and best decision curve analysis were able to be obviously determined. Meanwhile, the P-risk Plus model result yielded a better fit for survival between the development and validation groups. CONCLUSION: Compared with conventional tumor stages, our nomogram-based P-risk Plus model for gallbladder adenocarcinoma has a better predictive capacity and thereby has a better potential to facilitate decision-making clinically.
Authors: Fátima Ramalhosa; Maria João Amaral; Marco Serôdio; Rui Caetano Oliveira; Paulo Teixeira; Maria Augusta Cipriano; José Guilherme Tralhão Journal: J Gastrointest Oncol Date: 2022-08