Feng Zhang1, Keshu Hu1, Bei Tang1, Mengxin Tian1, Shenxin Lu1, Jia Yuan1, Miao Li1, Rongxin Chen1, Zhenggang Ren1, Yinghong Shi1, Xin Yin2. 1. Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai, 200032, China. 2. Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai, 200032, China. yin.xin@zs-hospital.sh.cn.
Abstract
BACKGROUND AND AIM: Combined hepatocellular and cholangiocarcinoma (cHCC-CCA) is a rare but aggressive primary liver cancer with dismal prognosis. We aim to develop a new scoring method for personalized prognostic prediction in patients with cHCC-CCA undergoing surgical resection. METHODS: Between January 1993 and December 2015, a total of 296 Allen type C cHCC-CCA patients who had received surgical resection in Liver Cancer Institute, Zhongshan Hospital were retrospectively enrolled. A novel prognostic scoring method for cHCC-CCA (PSM-CHCC model) was established and validated. The predictive value of the new model was compared with current prognostic staging systems. RESULTS: The scoring model was developed based on the independent prognostic variables identified by Cox regression model. Based on the PSM-CHCC model, patients were stratified into three prognostic subgroups according to their individual score: A (scoring 0-2), B (scoring 3-5), and C (scoring > 5). The prediction performance of the PSM-CHCC model outperformed the widely accepted TNM staging system and other staging systems in both training and validation cohorts. Subgroup analysis also verified the discrimination efficacy of the PSM-CHCC model. CONCLUSIONS: The newly established PSM-CHCC model may facilitate prognostic stratification and clinical decision-making in patients with cHCC-CCA.
BACKGROUND AND AIM: Combined hepatocellular and cholangiocarcinoma (cHCC-CCA) is a rare but aggressive primary liver cancer with dismal prognosis. We aim to develop a new scoring method for personalized prognostic prediction in patients with cHCC-CCA undergoing surgical resection. METHODS: Between January 1993 and December 2015, a total of 296 Allen type C cHCC-CCA patients who had received surgical resection in Liver Cancer Institute, Zhongshan Hospital were retrospectively enrolled. A novel prognostic scoring method for cHCC-CCA (PSM-CHCC model) was established and validated. The predictive value of the new model was compared with current prognostic staging systems. RESULTS: The scoring model was developed based on the independent prognostic variables identified by Cox regression model. Based on the PSM-CHCC model, patients were stratified into three prognostic subgroups according to their individual score: A (scoring 0-2), B (scoring 3-5), and C (scoring > 5). The prediction performance of the PSM-CHCC model outperformed the widely accepted TNM staging system and other staging systems in both training and validation cohorts. Subgroup analysis also verified the discrimination efficacy of the PSM-CHCC model. CONCLUSIONS: The newly established PSM-CHCC model may facilitate prognostic stratification and clinical decision-making in patients with cHCC-CCA.
Entities:
Keywords:
Combined hepatocellular and cholangiocarcinoma; Overall survival; Prognosis; Recurrence-free survival
Authors: Jane E Rogers; Ryan M Bolonesi; Asif Rashid; Khaled M Elsayes; Mohamed G Elbanan; Lindsey Law; Ahmed Kaseb; Rachna T Shroff Journal: J Gastrointest Oncol Date: 2017-04
Authors: Fabio Farinati; Alessandro Vitale; Gaya Spolverato; Timothy M Pawlik; Teh-la Huo; Yun-Hsuan Lee; Anna Chiara Frigo; Anna Giacomin; Edoardo G Giannini; Francesca Ciccarese; Fabio Piscaglia; Gian Lodovico Rapaccini; Mariella Di Marco; Eugenio Caturelli; Marco Zoli; Franco Borzio; Giuseppe Cabibbo; Martina Felder; Rodolfo Sacco; Filomena Morisco; Elisabetta Biasini; Francesco Giuseppe Foschi; Antonio Gasbarrini; Gianluca Svegliati Baroni; Roberto Virdone; Alberto Masotto; Franco Trevisani; Umberto Cillo Journal: PLoS Med Date: 2016-04-26 Impact factor: 11.069