Details

Autor(en) / Beteiligte

• Tian, Meng-Xin
• He, Wen-Jun
• Liu, Wei-Ren
• Yin, Jia-Cheng
• Jin, Lei
• Tang, Zheng
• Jiang, Xi-Fei
• Wang, Han
• Zhou, Pei-Yun
• Tao, Chen-Yang
• Ding, Zhen-Bin
• Peng, Yuan-Fei
• Dai, Zhi
• Qiu, Shuang-Jian
• Zhou, Jian
• Fan, Jia
• Shi, Ying-Hong

Titel

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

Ist Teil von

• Journal of Cancer, 2018-01, Vol.9 (6), p.1025-1032

Ort / Verlag

Australia: Ivyspring International Publisher

Erscheinungsjahr

2018

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

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