J Cancer 2018; 9(6):1025-1032. doi:10.7150/jca.23229 This issue

Research Paper

A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma

Meng-Xin Tian1*, Wen-Jun He2*, Wei-Ren Liu1*, Jia-Cheng Yin1, Lei Jin1, Zheng Tang1, Xi-Fei Jiang1, Han Wang1, Pei-Yun Zhou1, Chen-Yang Tao1, Zhen-Bin Ding1, Yuan-Fei Peng1, Zhi Dai1, Shuang-Jian Qiu1, Jian Zhou1,3, Jia Fan1,3✉, Ying-Hong Shi1✉

1. Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, China;
2. Department of Medical Statistic and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China;
3. Institutes of Biomedical Sciences, Fudan University, Shanghai, People's Republic of China.
* These authors contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Tian MX, He WJ, Liu WR, Yin JC, Jin L, Tang Z, Jiang XF, Wang H, Zhou PY, Tao CY, Ding ZB, Peng YF, Dai Z, Qiu SJ, Zhou J, Fan J, Shi YH. A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma. J Cancer 2018; 9(6):1025-1032. doi:10.7150/jca.23229. Available from https://www.jcancer.org/v09p1025.htm

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Backgrounds: Regarding the difficulty of CHC diagnosis and potential adverse outcomes or misuse of clinical therapies, an increasing number of patients have undergone liver transplantation, transcatheter arterial chemoembolization (TACE) or other treatments.

Objective: To construct a convenient and reliable risk prediction model for identifying high-risk individuals with combined hepatocellular-cholangiocarcinoma (CHC).

Methods: 3369 patients who underwent surgical resection for liver cancer at Zhongshan Hospital were enrolled in this study. The epidemiological and clinical characteristics of the patients were collected at the time of tumor diagnosis. Variables (P <0.25 in the univariate analyses) were evaluated using backward stepwise method. A receiver operating characteristic (ROC) curve was used to assess model discrimination. Calibration was performed using the Hosmer-Lemeshow test and a calibration curve. Internal validation was performed using a bootstrapping approach.

Results: Among the entire study population, 250 patients (7.42%) were pathologically defined with CHC. Age, HBcAb, red blood cells (RBC), blood urea nitrogen (BUN), AFP, CEA and portal vein tumor thrombus (PVTT) were included in the final risk prediction model (area under the curve, 0.69; 95% confidence interval, 0.51-0.77). Bootstrapping validation presented negligible optimism. When the risk threshold of the prediction model was set at 20%, 2.73% of the patients diagnosed with liver cancer would be diagnosed definitely, which could identify CHC patients with 12.40% sensitivity, 98.04% specificity, and a positive predictive value of 33.70%.

Conclusions: Herein, the study established a risk prediction model which incorporates the clinical risk predictors and CT/MRI-presented PVTT status that could be adopted to facilitate the diagnosis of CHC patients preoperatively.

Keywords: combined hepatocellular-cholangiocarcinoma, risk prediction model, liver cancer, preoperation.