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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868170/
https://www.ncbi.nlm.nih.gov/pubmed/29581782
http://dx.doi.org/10.7150/jca.23229
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author 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
author_facet 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
author_sort Tian, Meng-Xin
collection PubMed
description 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.
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spelling pubmed-58681702018-03-26 A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma 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 J Cancer Research Paper 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. Ivyspring International Publisher 2018-02-28 /pmc/articles/PMC5868170/ /pubmed/29581782 http://dx.doi.org/10.7150/jca.23229 Text en © Ivyspring International Publisher 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.
spellingShingle Research Paper
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
A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title_full A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title_fullStr A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title_full_unstemmed A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title_short A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma
title_sort novel risk prediction model for patients with combined hepatocellular-cholangiocarcinoma
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868170/
https://www.ncbi.nlm.nih.gov/pubmed/29581782
http://dx.doi.org/10.7150/jca.23229
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