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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

邮发代号 80-967

2019 Impact Factor: 3.421

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Frontiers of Medicine  2015, Vol. 9 Issue (3): 350-355   https://doi.org/10.1007/s11684-015-0402-2
  本期目录
Exploring the diagnosis markers for gallbladder cancer based on clinical data
Lingqiang Zhang1,Runchen Miao1,Xiude Zhang2,Wei Chen1,Yanyan Zhou1,Ruitao Wang1,Ruiyao Zhang1,Qing Pang1,Xinsen Xu1,Chang Liu1,*()
1. Department of Hepatobiliary Surgery, the First Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an 710061, China
2. Department of Endocrinology, Xian Yang Center Hospital, Xianyang 712000, China
 全文: PDF(202 KB)   HTML
Abstract：

Presently, no effective markers are available to facilitate gallbladder cancer (GBC) diagnosis. This study aims to explore available markers for GBC diagnosis. Clinical data of 144 GBC and 116 cholelithiasis patients were retrospectively reviewed. Logistic regression analysis was performed to evaluate GBC risk factors. A receiver operating characteristic (ROC) curve was used to assess the diagnosis value of the risk factors. By comparing the characteristic of GBC and cholelithiasis patients, the following factors exhibited statistical difference: age, gender, gallstones, total bilirubin (TB), alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelet count (PLT), CA125 (carcinoembryonic antigen 125), and CA199 (carbohydrate antigen 199). Logistic regression analysis indicated that age [odds ratio (OR), 1.032; 95% confidence interval (95% CI), 1.004 to 1.061; P = 0.024], gender (OR, 0.346; 95% CI, 0.167 to 0.716; P = 0.004), gallstones (OR, 0.027; 95% CI, 0.007 to 0.095; P<0.001), ALP (OR, 1.003; 95% CI, 1.000 to 1.006; P = 0.032), TB (OR, 1.004; 95% CI, 1.000 to 1.009; P = 0.042), and CA125 (OR, 1.007; 95% CI, 1.002 to 1.013; P = 0.011) were independent risk factors for GBC. According to the ROC curve, CA125 [area under curve (AUC), 0.720], ALP (AUC, 0.713), TB (AUC, 0.636), and age (AUC, 0.573) were valuable diagnosis markers. Additionally, based on the independent risk factors, the GBC diagnosis model was established. Age, TB, ALP, and CA125 can be used as auxiliary diagnosis factors of GBC. The diagnosis model provides a quantitative tool for GBC diagnosis when comprehensively considering various risk factors.
Key words： marker    gallbladder cancer    diagnosis
收稿日期: 2015-01-29      出版日期: 2015-08-26
Corresponding Author(s): Chang Liu   
 引用本文:   
. [J]. Frontiers of Medicine, 2015, 9(3): 350-355.
Lingqiang Zhang,Runchen Miao,Xiude Zhang,Wei Chen,Yanyan Zhou,Ruitao Wang,Ruiyao Zhang,Qing Pang,Xinsen Xu,Chang Liu. Exploring the diagnosis markers for gallbladder cancer based on clinical data. Front. Med., 2015, 9(3): 350-355.
 链接本文:  
https://academic.hep.com.cn/fmd/CN/10.1007/s11684-015-0402-2
https://academic.hep.com.cn/fmd/CN/Y2015/V9/I3/350
Parameter Cholelithiasis patients (n = 116) (mean±SD) GBC patients (n = 144) (mean±SD) P value
Male, n (%) 58 (50) 46 (31.94) 0.003
Age (year) 58.91±14.87 63.26±11.15 0.008
Gallstones, n (%) 116 (100) 78 (68.42) 0.001
AST (U/L) 75.98±156.01 123.27±152.95 0.015
ALT (U/L) 73.98±137.36 111.16±139.15 0.032
ALP (U/L) 139.27±124.68 328.92±321.36 <0.0001
TB (μmol/L) 38.97±66.50 119.99±140.22 <0.0001
ALB (g/L) 37.04±5.56 37.03±6.29 0.995
RBC (×1012/L) 4.02±0.72 4.22±3.72 0.512
HGB (g/L) 123.19±23.93 119.26±16.56 0.120
PLT (×109/L) 180.77±94.11 216.86±90.85 0.002
WBC (×109/L) 7.32±5.47 7.08±3.53 0.659
LYM (×109/L) 1.33±0.81 1.53±2.31 0.324
NEU (×109/L) 5.41±4.78 4.97±3.36 0.388
CEA (ng/ml) 7.68±42.37 20.91±129.55 0.251
AFP (ng/ml) 215.08±2049.57 8.08±51.35 0.227
CA125 (U/ml) 27.49±38.9 91.18±328.31 0.039
CA199 (U/ml) 245.47±1136.57 935.21±2040.61 0.001
Tab.1  
Variable Univariate analysis Multivariate analysis
OR (95%CI) P value OR (95%CI) P value
Gender 0.469 (0.283?0.778) 0.003 0.346 (0.167?0.716) 0.004
Age (year) 1.026 (1.007?1.046) 0.009 1.032 (1.004?1.061) 0.024
Gallstones 0.031 (0.010?0.103) <0.001 0.027 (0.007?0.095) <0.001
AST 1.002 (1.000?1.004) 0.020 1.002 (0.997?1.006) 0.450
ALT 1.002 (1.000?1.004) 0.040 0.997 (0.992?1.002) 0.300
ALP 1.005 (1.003?1.007) <0.001 1.003 (1.000?1.006) 0.032
TB 1.008 (1.005?1.011) <0.001 1.004 (1.000?1.009) 0.042
PLT 1.005 (1.002?1.008) 0.003 1.003 (0.999?1.007) 0.109
CA125 1.013 (1.005?1.021) 0.001 1.007 (1.002?1.013) 0.011
CA199 1.000 (1.000?1.001) 0.010 1.000 (1.000?1.000) 0.552
Tab.2  
Fig.1  
Variable Cut-off value AUC SN (%) SP (%) PPV (%) NPV (%) Accuracy (%) P value
Age (year) 57 0.573 0.729 0.422 0.389 0.443 0.408 0.043
ALP (U/L) 113.885 0.713 0.688 0.647 0.293 0.375 0.331 <0.0001
TB (μmol/L) 147.615 0.636 0.375 0.948 0.100 0.450 0.369 <0.0001
CA125 (U/ml) 13.665 0.720 0.882 0.466 0.344 0.246 0.319 <0.0001
Model 2.5 0.791 0.764 0.664 0.262 0.306 0.281 <0.0001
Tab.3  
Fig.2  
Variable Category OR (95% CI) P value
Gender Male vs. female 2.338 (1.290?4.239) 0.005
Age (year) >57 vs.≤57 2.141 (1.147?3.999) 0.017
ALP level (U/L) >113.885 vs.≤113.885 2.491 (1.321?4.697) 0.005
TB level (μmol/L) >147.615 vs.≤147.615 4.822 (1.811?12.839) 0.002
CA125 level (U/ml) >13.665 vs.≤13.665 3.145 (1.582?6.250) 0.001
Tab.4  
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