Background: Hepatocellular carcinoma is a common complication of chronic liver disease (CLD), and is conventionally diagnosed by radiological means. We aimed to build a statistical model that could determine the risk of hepatocellular carcinoma in individual patients with CLD using objective measures, particularly serological tumor markers.

Methods: A total of 670 patients with either CLD alone or hepatocellular carcinoma were recruited from a single UK center into a case–control study. Sera were collected prospectively and specifically for this study. A logistic regression analysis was used to determine independent factors associated with hepatocellular carcinoma and a model built and assessed in terms of sensitivity, specificity, and proportion of correct diagnoses.

Results: The final model involving gender, age, AFP-L3, α fetoprotein (AFP), and des-carboxy-prothrombin (“GALAD”) was developed in a “discovery” data set and validated in independent data sets both from the same institution and from an external institution. When optimized for sensitivity and specificity, the model gave values of more than 0.88 irrespective of the disease stage.

Conclusions: The presence of hepatocellular carcinoma can be detected in patients with CLD on the basis of a model involving objective clinical and serological factors. It is now necessary to test the model's performance in a prospective manner and in a routine clinical practice setting, to determine if it may replace or, more likely, enhance current radiological approaches.

Impact: Our data provide evidence that an entirely objective serum biomarker–based model may facilitate the detection and diagnosis of hepatocellular carcinoma and form the basis for a prospective study comparing this approach with the standard radiological approaches. Cancer Epidemiol Biomarkers Prev; 23(1); 144–53. ©2013 AACR.

Hepatocellular carcinoma was, until recently, diagnosed on the basis of histologic examination of tumor tissue but the diagnosis can now be established with a high degree of specificity on the basis of characteristic radiological features once tumors are greater than 1 cm in diameter (1–3). Furthermore, such an approach obviates both the risk of bleeding and tumor seeding along the biopsy tract (4, 5).

Surveillance, well accepted to be the key to effective delivery of potentially curative treatment (1, 2), involves ultrasound examination (USS; refs. 1, 2, 6) followed, where a suspicious lesion is detected, by confirmatory tests, including conventional computed tomography (CT) or MRI scanning with or without biopsy. Estimation of serum α-fetoprotein (AFP) has also been used for diagnosis, with grossly elevated levels being highly specific for hepatocellular carcinoma (7) but, as the importance of early diagnosis has become apparent, the limited sensitivity of AFP for hepatocellular carcinoma in smaller tumors has reduced its value (1, 2, 8, 9). Other serological diagnostic tests include des-carboxy-prothrombin (DCP; an abnormal prothrombin molecule derived from an acquired defect in the posttranslational carboxylation of the prothrombin precursor; refs. 10, 11) and AFP-L3 (an isoform of AFP characterized by the presence of an α 1-6–linked residue on the AFP carbohydrate side chain; refs. 12, 13).

The limitations of radiological diagnosis of hepatocellular carcinoma are, however, being increasingly recognized in terms of diagnosis of new cases and in the screening setting (14). Both the number of non-hepatocellular carcinoma lesions and pseudolesions, which may collectively be more common than hepatocellular carcinoma lesions in the cirrhotic liver, and the necessity for considerable expertise in liver-imaging, have been noted (14). The limitations of ultrasound for screening are also becoming apparent. It has limited sensitivity, usually quoted at between 65% and 80%, but rather lower in early disease in which appearances are not specific and performance characteristics have not been well defined in nodular cirrhotic liver undergoing surveillance. Furthermore, it is subjective and dependent on operator experience and the available equipment (15–20). Again, it is increasingly recognized that although screening by USS may be effective in specialized centers, this does not necessarily translate into an effective screening system in the wider community (21). Increasing levels of obesity in the West also limit the sensitivity of USS (22).

For all these reasons, we consider here the possibility of establishing the diagnosis of hepatocellular carcinoma in the clinical and, potentially, in the screening setting, using entirely objective measurements, mainly the three serological tests AFP, DCP, and AFP-L3 by developing a statistical model. Being cognizant of the concern that any statistical model would need to perform well in the early-disease setting, we were careful to collect the clinical material such that patients could be rigorously classified as to their disease stage.

This case–control study involved 670 patients, 331 with hepatocellular carcinoma and a control group of 339 patients with chronic liver disease (CLD), alone. The patients with hepatocellular carcinoma were recruited at the Queen Elizabeth Hospital (Birmingham, UK) from among patients who were approached and consented to the study between 2007 and 2012 (Table 1). For all patients, the diagnosis was established by the histologic examination of tumor tissue (23%) or characteristic radiotherapy according to international guidelines (1, 2). Samples were taken at the time of first referral for treatment or further investigation. The median period between sample acquisition and formal diagnosis on the basis of CT scan or histology was 1.7 months. No treatment was administered between sample acquisition and formal diagnosis of hepatocellular carcinoma. CLD control samples (n = 339) were recruited from patients who were attending outpatient clinics for CLD in the same institution and classified as hepatitis B virus (HBV)-related, hepatitis C virus (HCV)-related, alcoholic-related, and “other” or “no” underlying CLD. The “other” group comprised patients with hemochromatosis, primary biliary cirrhosis, nonalcoholic steatohepatitis, or cryptogenic cirrhosis. The diagnosis of CLD was made on the basis of liver biopsy and/or typical clinical and imaging features. None of the CLD-control group had evidence of hepatocellular carcinoma at the time the relevant serum sample was taken or within a minimum follow-up period of 6 months (Table 1), but three of them developed hepatocellular carcinoma between 6 and 12 months. For the purpose of analysis, these three patients remained assigned to the CLD group. An age- and sex-matched control group of 92 subjects without any evidence of liver disease were recruited from patients with upper gastrointestinal symptoms who had no clinically significant abnormal findings at endoscopy. All patients gave informed consent for donating blood and the study procedure was approved by the South Birmingham Research Ethics Committee or The Newcastle and North Tyneside Ethics Committee. A standard operating procedure was applied to all blood collection. The Birmingham serum samples were collected prospectively for the discovery and internal validation sets, specifically for this research project and according to the REMARK guidelines (23, 24). The “discovery” set comprised 218 patients with hepatocellular carcinoma seen between April 2007 and January 2011. This sample size was based upon the calculation that approximately 200 subjects per group would be sufficient, using a two-sided test, to reject the null hypothesis of AUROC (area under the receiver operator curve) = 0.75 in favor of AUROC = 0.85 with 90% power for a significance level 0.05. On the basis of Harrell's rule of thumb, this sample size is also sufficient to allow the fitting of up to 20 candidate variables within a logistic discrimination model. The validation set comprised 113 patients with hepatocellular carcinoma seen between February 2011 and March 2012. The external validation set was from a previously reported study (25) designed to assess surveillance biomarkers in fatty liver disease (alcoholic and nonalcoholic). All markers were measured again on stored sera (Table 1) and collected specifically and prospectively for biomarker assessment.

Table 1.

Characteristics of patients with hepatocellular carcinoma and CLD

BirminghamNewcastle
VariableHCC (n = 331)CLD (n = 339)HCC (n = 63)CLD (n = 100)
Demographics 
Median age, y 66.3 (59.2–72.9) 53 (45–63) 68.6 (62.2–75.0) 63.6 (57.3–69.3) 
Mean age, y (± SD) 65.3 (± 9.9) 52.8 (± 13.7) 68.3 (± 8.5) 62.2 (± 11.2) 
Gender (M:F) 272:59 214:125 53:10 42:58 
Etiology 
Alcohol 81 53 27 17 
HCV 43 74 
HBV 30 58 
HBV+HCV 
Other 78 75 27 83 
Hemochromatosis 
Autoimmune 28 
PBC 11 18 
NASH/NAFLD 17 12 12 81 
Cryptogenic/other 42 14 11 
Noncirrhotic 34 10 
Multiple (more than one etiology)a 48 58 
Unknown 15 
CLD (No:Yes:NK) 37:283:11 13:320:6 9:54:0 0:100:0 
HCC biomarkers 
AFP (ng/mL) 57 (8.3–1438) n = 331 2.8 (2–4.7) n = 339 44.5 (6.1–1501.9) n = 63 3.2 (2.3–4.7) n = 99 
Log10 AFP (ng/mL) 1.76 (0.92–3.16) n = 331 0.4 (0.3–0.7) n = 339 2.1 (± 1.6) n = 63 0.5 (0.4–0.7) n = 99 
L3 (%) 16.6 (7–51.9) n = 319 1(1–7.1) n = 339 24.5 (8.1–49.4) n = 63 1 (1–7.7) n = 99 
Log10 L3 (%) 1.2 (0.85–1.72) n = 319 0 (0–0.8) n = 339 1.2 (± 0.6) n = 63 0 (0–0.9) n = 99 
DCP (ng/mL) 20.8 (2.6–169.7) n = 320 0.35 (0.27–0.6) n = 339 16.3 (3.0–102.7) n = 63 0.5 (0.4–0.8) n = 99 
Log10 DCP (ng/mL) 1.37 (± 1.2) n = 320 −0.5 (−0.6 to −0.2) n = 339 1.4 (± 1.2) n = 63 −0.3 (−0.4 – −0.1) n = 99 
LFTs 
Albumin (g/L) 39 (34.3–43) n = 330 44 (40–46) n = 339 35.9 (± 5.6) n = 63 44 (41–47) n = 100 
ALP (U/L) 370 (258.3–568.8) n = 330 213 (163–313) n = 339 177 (127–260) n = 63 89 (71.8–123.8) n = 100 
INR 1.1 (1.0–1.2) n = 324 1 (1–1.1) n = 333 1 (1–1.2) n = 63 1 (0.9–1.1) n = 100 
Bilirubin (μmol/L) 17 (11–28) n = 330 11 (8–19.5) n = 339 17 (12–30) n = 63 9 (6–13) n = 100 
Creatinine (μmol/L) 77 (64–96) n = 330 72 (63–85) n = 339 100.7 (±28.6) n = 63 88.7 (± 18.7) n = 100 
Child–Pugh score 
A:B:C:NK 245:73:10:3 291:43:4 40:12:11 NK 
Tumor characteristics 
Vascular invasion (No:Yes:NK) 225:87:19 NA 45:18:0 NA 
Milan criteria (No:Yes: NK) 223:82:26 NA 49:14:0 NA 
Treatments 
Curative (intended:actual) 67:52 NA NK NA 
Palliative (intended:actual) 255:255 NA NK NA 
Not known (intended:actual) 9:24    
Biopsy confirmation of HCC/CLD 77 (23%) 88 (26%) 27 (42.9%) NA 
BirminghamNewcastle
VariableHCC (n = 331)CLD (n = 339)HCC (n = 63)CLD (n = 100)
Demographics 
Median age, y 66.3 (59.2–72.9) 53 (45–63) 68.6 (62.2–75.0) 63.6 (57.3–69.3) 
Mean age, y (± SD) 65.3 (± 9.9) 52.8 (± 13.7) 68.3 (± 8.5) 62.2 (± 11.2) 
Gender (M:F) 272:59 214:125 53:10 42:58 
Etiology 
Alcohol 81 53 27 17 
HCV 43 74 
HBV 30 58 
HBV+HCV 
Other 78 75 27 83 
Hemochromatosis 
Autoimmune 28 
PBC 11 18 
NASH/NAFLD 17 12 12 81 
Cryptogenic/other 42 14 11 
Noncirrhotic 34 10 
Multiple (more than one etiology)a 48 58 
Unknown 15 
CLD (No:Yes:NK) 37:283:11 13:320:6 9:54:0 0:100:0 
HCC biomarkers 
AFP (ng/mL) 57 (8.3–1438) n = 331 2.8 (2–4.7) n = 339 44.5 (6.1–1501.9) n = 63 3.2 (2.3–4.7) n = 99 
Log10 AFP (ng/mL) 1.76 (0.92–3.16) n = 331 0.4 (0.3–0.7) n = 339 2.1 (± 1.6) n = 63 0.5 (0.4–0.7) n = 99 
L3 (%) 16.6 (7–51.9) n = 319 1(1–7.1) n = 339 24.5 (8.1–49.4) n = 63 1 (1–7.7) n = 99 
Log10 L3 (%) 1.2 (0.85–1.72) n = 319 0 (0–0.8) n = 339 1.2 (± 0.6) n = 63 0 (0–0.9) n = 99 
DCP (ng/mL) 20.8 (2.6–169.7) n = 320 0.35 (0.27–0.6) n = 339 16.3 (3.0–102.7) n = 63 0.5 (0.4–0.8) n = 99 
Log10 DCP (ng/mL) 1.37 (± 1.2) n = 320 −0.5 (−0.6 to −0.2) n = 339 1.4 (± 1.2) n = 63 −0.3 (−0.4 – −0.1) n = 99 
LFTs 
Albumin (g/L) 39 (34.3–43) n = 330 44 (40–46) n = 339 35.9 (± 5.6) n = 63 44 (41–47) n = 100 
ALP (U/L) 370 (258.3–568.8) n = 330 213 (163–313) n = 339 177 (127–260) n = 63 89 (71.8–123.8) n = 100 
INR 1.1 (1.0–1.2) n = 324 1 (1–1.1) n = 333 1 (1–1.2) n = 63 1 (0.9–1.1) n = 100 
Bilirubin (μmol/L) 17 (11–28) n = 330 11 (8–19.5) n = 339 17 (12–30) n = 63 9 (6–13) n = 100 
Creatinine (μmol/L) 77 (64–96) n = 330 72 (63–85) n = 339 100.7 (±28.6) n = 63 88.7 (± 18.7) n = 100 
Child–Pugh score 
A:B:C:NK 245:73:10:3 291:43:4 40:12:11 NK 
Tumor characteristics 
Vascular invasion (No:Yes:NK) 225:87:19 NA 45:18:0 NA 
Milan criteria (No:Yes: NK) 223:82:26 NA 49:14:0 NA 
Treatments 
Curative (intended:actual) 67:52 NA NK NA 
Palliative (intended:actual) 255:255 NA NK NA 
Not known (intended:actual) 9:24    
Biopsy confirmation of HCC/CLD 77 (23%) 88 (26%) 27 (42.9%) NA 

NOTE: For all continuous variables, values are presented either as median (interquartile range) or mean (±SD), the latter for normal distributions where appropriate.

Abbreviations: ALP, alkaline phosphatase; F, female; HCC, hepatocellular carcinoma; INR, international normalized ratio; M, male; NA, not applicable; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; NK, not known; PBC, primary biliary cirrhosis.

aFor example, alcoholic and HCV-positive.

Patients were classified as having “early” or “late” disease on the basis of three staging systems: tumor–node–metastasis (TNM) 6, Barcelona Liver Cancer Clinic (BCLC; refs. 26–30), Milan criteria (31), or on an “operational” basis. Stages I and II of TNM 6 and BCLC stages 0 and A were classified as early disease and, as an additional measure to disease stage, tumor size equal to or below 5 cm was considered early, whereas a size of more than 5 cm was considered late irrespective of tumor number. Those within and outside Milan criteria were categorized as early and late, respectively. Early and late disease was “operationally” classified on the basis of whether or not an experienced multidisciplinary team recommended potentially curative treatment. Where patients were listed for transplantation but had TransArterial ChemoEmbolization as initial treatment as a “bridge” to transplantation, they were classified as having early disease.

Routine liver and renal function tests (LFT and RFT) were measured on an automated analytical platform (the Roche Cobas 8000 Modular system) and the severity of the liver disease was defined according to the Child–Pugh score (32, 33). The hepatitis B surface antigen (HBsAg) and anti-HCV antibodies were measured using the e602 module (employing electrochemiluminescence technology) on the Roche Cobas 8000 system.

Assays of AFP, AFP-L3%, and DCP

AFP, AFP-L3%, and DCP were all measured in the same serum sample. The measurements of all three biomarkers were undertaken using a microchip capillary electrophoresis and liquid-phase binding assay on a μTASWako i30 auto analyzer (Wako Pure Chemical Industries Ltd.; ref. 34). Analytical sensitivity of μTASWako i30 is 0.3 ng/mL AFP and 0.1 ng/mL DCP, and the percentage of AFP-L3 can be measured when AFP-L3 is more than 0.3 ng/mL (34). All aspects of the test system performance have been reported (34). The assays were undertaken in a commercial laboratory with extensive experience of the μTASWako i30 auto analyzer; the operators had no knowledge of the diagnosis associated with the patient sample. There were no adverse events attributable to the biomarker tests.

Statistical methods

Model development.

Continuous measurements are presented as medians (ranges) and categorical measurements are presented as frequencies. Odds ratios (ORs) are calculated using logistic regression for univariate and multivariate analyses to assess the strength of the association with hepatocellular carcinoma. Age, sex, albumin, bilirubin, AFP, DCP, and AFP-L3 were considered for inclusion in the multivariable models. Complete data for the GALAD score were available for more than 95% of cases. Patients with missing data were dropped from the statistical analysis. Factors such as symptoms and performance score were excluded to limit subjectivity in the model. A log transformation was made to AFP and DCP due to extreme skewness. Logistic regression analyses were based on a complete case analysis using a parsimonious forward–backward stepwise approach, keeping variables significant at the 1% level but that also increase AUROC. Fractional polynomials (35) were also used to investigate whether a more sophisticated transformation than the log transformation could improve the prediction.

Model accuracy is presented as sensitivity, specificity, proportion of false positives and negatives, and overall percentage of correct predictions. Having developed the model as described above, three cutoff points for classifying patients to the hepatocellular carcinoma group were used. The first optimized for maximum sensitivity while maintaining a prespecified specificity, the second for maximum specificity while maintaining a prespecified sensitivity, and the third for the maximum of the sum of specificity and sensitivity. Patients can then be classified by the model as being predicted to have hepatocellular carcinoma or not and this can then be directly compared against true diagnosis.

Model validation.

Model validation is carried out on independent data sets in which again patients were diagnosed as hepatocellular carcinoma or CLD. The predictive score for each patient, based on fitted models, is used to classify patients as having hepatocellular carcinoma or not, and this is then directly compared against true diagnosis.

Of the 331 patients with hepatocellular carcinoma, 283 (85.5%) had clear evidence of associated CLD, 37 (11.2%) seemed to have no underlying benign liver disease, and in 11 (3.3%) the presence or absence of underlying CLD could not be ascertained with certainty. The corresponding figures for the CLD group were 96%, 2.6%, and 1.8%, respectively. Among all data considered in the derived statistical models, data completeness was greater than 98%.

The median values for log(AFP), log(DCP), and AFP-L3 were significantly higher in the patients with hepatocellular carcinoma than in those with CLD, and both groups had median values higher than those for healthy control subjects (Fig. 1). All three biomarkers showed considerable discriminatory ability for distinguishing between hepatocellular carcinoma and CLD (AUROCs: log(AFP) 0.88, AFP-L3 0.84, and log(DCP) 0.90; Fig. 2A).

Figure 1.

Log AFP, log AFP-L3, and log DCP values in the hepatocellular carcinoma, CLD, and healthy control patient cohorts, showing the median value and 25th and 75th percentiles. Outliers are shown as empty circles.

Figure 1.

Log AFP, log AFP-L3, and log DCP values in the hepatocellular carcinoma, CLD, and healthy control patient cohorts, showing the median value and 25th and 75th percentiles. Outliers are shown as empty circles.

Close modal
Figure 2.

A–C, ROC curves showing the performance of model 3 (based on all Birmingham data) in comparison with individual biomarkers (A), patients with early disease as classified by TNM 6 (B), and in patients with late disease also classified by TNM 6 (C).

Figure 2.

A–C, ROC curves showing the performance of model 3 (based on all Birmingham data) in comparison with individual biomarkers (A), patients with early disease as classified by TNM 6 (B), and in patients with late disease also classified by TNM 6 (C).

Close modal

The optimal model (model 1), built on the discovery data set, included log(DCP), log(AFP), and AFP-L3, as well as age and sex, and had an AUC of 0.97. The Child–Pugh score was included in the logistic regression analysis but proved not to be a significant factor in the models. The model utility was maintained irrespective of the Child–Pugh class. Application of functional polynomials leads to a model incorporating AFP-L3, DCP(–0.5), and AFP(0.5) (model 2).

Table 2 shows the estimated coefficients (SE) and OR (95% confidence intervals, CI) from the univariate analyses as well as from the multivariate analysis using the discovery set data based on model 1. Table 3 shows the true positives/negatives, false positives/negatives, sensitivity, specificity, and proportion correctly classified when the multivariate model is used on the discovery data set (discovery) and on the validation data set (internal validation). For the validation set, AUROC = 0.98. Also shown are subsets of the results for the fractional polynomial (model 2).

Table 2.

Parameter estimates (SE) and OR (95% CI) of variables based on model 1

Variableβ (SE)OR (95% CI)χ2P
BRM discovery data set 
Constant –10.25 (1.46)    
Age 0.10 (0.02) 1.11 (1.07–1.15) 29.63 <0.001 
Sex 1.34 (0.46) 3.83 (1.56–9.42) 8.57 0.003 
Log(AFP) 2.36 (0.48) 10.57 (4.16–26.86) 24.57 <0.001 
AFP-L3 0.05 (0.02) 1.05 (1.00–1.09) 4.36 0.037 
Log(DCP) 1.55 (0.24) 4.73 (2.93–7.63) 40.31 <0.001 
Full BRM data set 
Constant −10.32 (1.24)    
Age 0.10 (0.02) 1.11 (1.07–1.14) 39.97 <0.001 
Sex 1.39 (0.38) 4.01 (1.89–8.49) 13.14 <0.001 
Log(AFP) 2.43 (0.38) 11.37 (5.35–24.14) 40.04 <0.001 
AFP-L3 0.04 (0.02) 1.04 (1.00–1.08) 4.66 0.031 
Log(DCP) 1.45 (0.20) 4.28 (2.92–6.27) 55.33 <0.001 
All data: BRM and NCL 
Constant −10.08 (1.08)    
Age 0.09 (0.01) 1.10 (1.07–1.13) 44.87 <0.001 
Sex 1.67 (0.33) 5.30 (2.79–10.07) 25.89 <0.001 
Log(AFP) 2.34 (0.33) 10.34 (5.40–19.79) 49.73 <0.001 
AFP-L3 0.04 (0.01) 1.04 (1.01–1.07) 8.66 0.003 
Log(DCP) 1.33 (0.17) 3.77 (2.73–5.21) 64.56 <0.001 
Variableβ (SE)OR (95% CI)χ2P
BRM discovery data set 
Constant –10.25 (1.46)    
Age 0.10 (0.02) 1.11 (1.07–1.15) 29.63 <0.001 
Sex 1.34 (0.46) 3.83 (1.56–9.42) 8.57 0.003 
Log(AFP) 2.36 (0.48) 10.57 (4.16–26.86) 24.57 <0.001 
AFP-L3 0.05 (0.02) 1.05 (1.00–1.09) 4.36 0.037 
Log(DCP) 1.55 (0.24) 4.73 (2.93–7.63) 40.31 <0.001 
Full BRM data set 
Constant −10.32 (1.24)    
Age 0.10 (0.02) 1.11 (1.07–1.14) 39.97 <0.001 
Sex 1.39 (0.38) 4.01 (1.89–8.49) 13.14 <0.001 
Log(AFP) 2.43 (0.38) 11.37 (5.35–24.14) 40.04 <0.001 
AFP-L3 0.04 (0.02) 1.04 (1.00–1.08) 4.66 0.031 
Log(DCP) 1.45 (0.20) 4.28 (2.92–6.27) 55.33 <0.001 
All data: BRM and NCL 
Constant −10.08 (1.08)    
Age 0.09 (0.01) 1.10 (1.07–1.13) 44.87 <0.001 
Sex 1.67 (0.33) 5.30 (2.79–10.07) 25.89 <0.001 
Log(AFP) 2.34 (0.33) 10.34 (5.40–19.79) 49.73 <0.001 
AFP-L3 0.04 (0.01) 1.04 (1.01–1.07) 8.66 0.003 
Log(DCP) 1.33 (0.17) 3.77 (2.73–5.21) 64.56 <0.001 

Abbreviations: BRM, Birmingham; NCL, Newcastle.

Table 3.

Model performance on the discovery and internal and external validation sets

SetTrue HCCTrue non-HCCFalse HCCFalse non-HCCSensitivitySpecificityCorrectly classifiedCutoff
Discovery Max. sens. (spec. = 0.80) 200 198 49 97 80 88 −1.58 
Model 1 Max. spec. (sens. = 0.80) 166 238 41 80 96 89 0.86 
 Max. sens.+ spec. 192 226 21 15 93 91 92 −0.44 
Internal validation Max. sens. (spec. = 0.80) 110 68 24 98 74 87 −1.58 
Model 1 Max. spec. (sens. = 0.80) 98 86 14 88 93 90 0.86 
 Max. sens.+ spec. 106 79 13 95 86 91 −0.44 
All Birmingham Max. sens. (spec. = 0.80) 309 271 68 10 97 80 88 −1.55 
Model 3 Max. spec. (sens. = 0.80) 256 329 10 63 80 97 89 1.10 
 Max. sens.+ spec. 297 305 34 22 93 90 91 −0.48 
External validation Max. sens. (spec. = 0.80) 62 59 36 98 62 77 −1.55 
Model 3 Max. spec. (sens. = 0.80) 51 90 12 81 95 89 1.10 
 Max. sens.+ spec. 59 79 16 94 83 87 −0.48 
All data: BRM and NCL Max. sens. (spec. = 0.80) 367 347 87 15 96 80 88 −1.36 
Model 5 Max. spec. (sens. = 0.80) 306 420 14 76 80 97 89 0.88 
 Max. sens.+ spec. 356 385 49 26 93 89 91 −0.63 
Fractional polynomial Max. sens.+ spec. discovery 195 220 27 12 94 89 91 −0.64 
Model 2 Max. sens.+ spec. internal validation 107 76 16 96 83 90 −0.64 
Model 4 Max. sens.+ spec. All BRM 296 306 33 23 93 90 91 −0.40 
Model 4 Max. sens.+ spec. external validation 58 80 15 92 84 87 −0.40 
SetTrue HCCTrue non-HCCFalse HCCFalse non-HCCSensitivitySpecificityCorrectly classifiedCutoff
Discovery Max. sens. (spec. = 0.80) 200 198 49 97 80 88 −1.58 
Model 1 Max. spec. (sens. = 0.80) 166 238 41 80 96 89 0.86 
 Max. sens.+ spec. 192 226 21 15 93 91 92 −0.44 
Internal validation Max. sens. (spec. = 0.80) 110 68 24 98 74 87 −1.58 
Model 1 Max. spec. (sens. = 0.80) 98 86 14 88 93 90 0.86 
 Max. sens.+ spec. 106 79 13 95 86 91 −0.44 
All Birmingham Max. sens. (spec. = 0.80) 309 271 68 10 97 80 88 −1.55 
Model 3 Max. spec. (sens. = 0.80) 256 329 10 63 80 97 89 1.10 
 Max. sens.+ spec. 297 305 34 22 93 90 91 −0.48 
External validation Max. sens. (spec. = 0.80) 62 59 36 98 62 77 −1.55 
Model 3 Max. spec. (sens. = 0.80) 51 90 12 81 95 89 1.10 
 Max. sens.+ spec. 59 79 16 94 83 87 −0.48 
All data: BRM and NCL Max. sens. (spec. = 0.80) 367 347 87 15 96 80 88 −1.36 
Model 5 Max. spec. (sens. = 0.80) 306 420 14 76 80 97 89 0.88 
 Max. sens.+ spec. 356 385 49 26 93 89 91 −0.63 
Fractional polynomial Max. sens.+ spec. discovery 195 220 27 12 94 89 91 −0.64 
Model 2 Max. sens.+ spec. internal validation 107 76 16 96 83 90 −0.64 
Model 4 Max. sens.+ spec. All BRM 296 306 33 23 93 90 91 −0.40 
Model 4 Max. sens.+ spec. external validation 58 80 15 92 84 87 −0.40 

Abbreviations: BRM, Birmingham; HCC, hepatocellular carcinoma; NCL, Newcastle; Max. sens., maximum sensitivity; Max. spec., maximum specificity.

Because the validation results were extremely supportive of the model, the discovery and validation data sets (subsequently referred to as “Birmingham data”) were combined and a new model was fitted (model 3).

For this model, AUROC = 0.97; parameter estimates (SE) and OR (95% CI) for the model variables are shown in Table 2. When the model was used on the Newcastle data set (external validation), AUROC = 0.95. A fractional polynomial model was also fitted (model 4).

Given the homogeneity of the Newcastle and Birmingham data, a final model based on all these data was found:

formula

(model 5), where Pr(HCC) = exp(Z)/(1 + exp(Z)) is the probability of hepatocellular carcinoma in a patient.

Table 3 shows the performance of models 1 and 2 on the Birmingham discovery and internal validation set, models 3 and 4 on all the Birmingham data and the external validation set, and model 5 on the total Birmingham and Newcastle data. Comparative figures are presented for models when maximized for either sensitivity and specificity or both in the discovery set, and then used in the validation sets. For example, using model 1, maximum sensitivity for specificity of 80% was achieved at a cutoff point of −1.58 and resulted in figures of 97% sensitivity in the discovery set and 98% sensitivity/74% specificity in the internal validation set. The corresponding cutoff point was −1.55 for the whole of the Birmingham data, leading to 97% sensitivity in the Birmingham data and 98% sensitivity/62% specificity in the external validation set. Note that the application of fractional polynomials did not improve the results.

The overall percentage of patients classified as having early disease varied widely depending on the definition applied (Table 4). Using BCLC, the percentage with early stage was 10%; with TNM 6 the percentage was 52%, 51% based on tumor size and 20% based on potentially curative therapies after multidisciplinary team review. Table 4 shows how model 5 performs on the early and late groups according to different classifications. Figure 2B and C show AUROC curves showing the performance of model 3 for the early and late groups (defined in terms of TNM 6) compared with controls.

Table 4.

Performance of model 5 on early- and late-stage patients

Sensitivity
Maximum sensitivityMaximum specificityMaximum both
Staging system/treatment typeCriteria for early or late diseaseNumber of patientsCutoff = −1.36Cutoff = 0.88Cutoff = −0.63
BCLC 
Early 0 and A 42 93 55 86 
Late B, C, and D 327 96 83 94 
TNM 6 
Early 1 and 2 154 93 70 89 
Late 3 and 4 143 99 90 97 
Tumor size 
Early ≤5 cm 169 92 67 88 
Late >5 cm 166 99 92 98 
Treatment intent 
Early Curative 61 85 56 75 
Late Palliative 252 98 86 98 
Sensitivity
Maximum sensitivityMaximum specificityMaximum both
Staging system/treatment typeCriteria for early or late diseaseNumber of patientsCutoff = −1.36Cutoff = 0.88Cutoff = −0.63
BCLC 
Early 0 and A 42 93 55 86 
Late B, C, and D 327 96 83 94 
TNM 6 
Early 1 and 2 154 93 70 89 
Late 3 and 4 143 99 90 97 
Tumor size 
Early ≤5 cm 169 92 67 88 
Late >5 cm 166 99 92 98 
Treatment intent 
Early Curative 61 85 56 75 
Late Palliative 252 98 86 98 

We have developed a model, now referred to as “GALAD,” that generates a figure for the probability of an individual patient with CLD having hepatocellular carcinoma. The physician can then determine at what level of likelihood further investigation, in the form of CT or MRI scanning, should be instituted. The model performance was only slightly poorer for early compared with late hepatocellular carcinoma however these terms were defined.

AFP used on its own has been the most widely used biomarker for hepatocellular carcinoma but fluctuating, albeit low levels in patients with CLD (without hepatocellular carcinoma) and low sensitivity in patients with early hepatocellular carcinoma have resulted in the recommendation that it should not be used in the screening setting, although this view remains contentious (1, 2, 8, 20, 36–38). The development of the AFP-L3 assay has increased the sensitivity and specificity of AFP (39) because it retains significant discriminatory ability even at low levels of total AFP (13, 40–43). The recent development of highly sensitive AFP-L3 using an automated microfluidic based assay has made the isoform even more sensitive and specific (13, 34). Because AFP, high-sensitivity AFP-L3, and DCP can now be measured on a single platform (44), the risk figure could be routinely reported. AFP-L3 and DCP have been approved by the Food and Drug Administration (FDA) in the United States and European Medicines Agency (EMEA) in Europe for the diagnosis of hepatocellular carcinoma. In Japan, all three markers are approved by the Japanese FDA (Korou-sho).

These markers have been investigated in Western populations using a study design similar to that applied here (45). Using receiver operating characteristic (ROC) analysis, Durazo and colleagues (45) determined optimal cutoff points for sensitivity, specificity, and positive predictive values, and concluded that DCP had optimal performance and that combining the markers did not achieve an additional predictive value to differentiate patients with hepatocellular carcinoma from those without hepatocellular carcinoma. In contrast, Carr and colleagues (46) considered that the combination of all three markers was superior to the individual although, unlike in the present study, formal statistical models were not proposed (47). The study most closely aligned to ours is that reported by Marrero and colleagues (47), in that a large number of Western patients were included, the disease etiologies were broadly similar, and account was taken of disease stage. Because overall the test performances will be affected by the methods of disease staging (discussed below), and Marrero and colleagues used a cut off point for individual markers rather than, as in our study, an overall model, direct comparison of results is not possible.

The three markers have also been studied in patients with cirrhosis or CLD who were followed-up and in whom hepatocellular carcinoma developed, although in neither study was biomarker validation the primary objective (45, 48). Two of the markers were investigated in patients in the Halt C trial using a nested case–control design. This involved 37 patients with hepatocellular carcinoma and 79 control subjects, all with HCV infection in the setting of a prospective study. The authors concluded that at the time of diagnosis, the combination provided a sensitivity of 91% and a specificity of 71% (at defined cutoff points; refs. 45, 48). In a second large prospective study, Kumada and colleagues (49) reported a series in which 623 patients with HCV-related CLD were prospectively followed-up and showed clearly that increased levels of AFP and AFP-L3 were closely associated with an increased incidence of hepatocellular carcinoma. On this basis, they suggested that patients with ≥10 ng/mL AFP levels or AFP-L3 ≥5% should receive intensive imaging at 3 to 6 month intervals. Our study included a third biomarker AFP-L3, a discovery, and two validation sets. The external validation set was not ideal because the cohort was derived from patients with fatty liver disease (alcoholic and nonalcoholic) and, as such, had a different spectrum of etiologies to that on which the model was developed and validated. Ultimately, the model will require validation on larger external data sets with differing etiologies.

All our patients had a minimum follow-up of 6 months to exclude occult hepatocellular carcinoma in the CLD cohort. We did not focus entirely on patients with cirrhosis because the risk of hepatocellular carcinoma is associated with CLD rather than just at the stage of cirrhosis, and we aimed to set our study as close to a real clinical situation as possible (36).

Precise estimation of sensitivity and specificity demands a rigid “gold standard,” and it is apparent that no definitive diagnosis of hepatocellular carcinoma or CLD without hepatocellular carcinoma is available. Thus, although current guidelines give figures of 85% and >95% for sensitivity and specificity, respectively, radiological diagnosis by CT or MRI imaging is still recognized to be “not infallible” (1, 2), especially in small and hypovascular tumors (50), Similarly, it is conceivable that a serological test might detect hepatocellular carcinoma in a cirrhotic liver before it is detectable on MRI scanning, thus resulting in the positive serological test being regarded as “false,” and decreasing its apparent sensitivity. It would be surprising, therefore, if any new (in this case serologically based) test could achieve 100% sensitivity and specificity; if the three patients in the control group who developed hepatocellular carcinoma between 6 and 12 months into the study had been reclassified as being in the hepatocellular carcinoma group, the results of our study would have been even better.

The role of surveillance in the early detection of hepatocellular carcinoma is widely accepted, and it has been estimated that approximately 70% of patients who are detected when the lesion is <5 cm or has three tumors each less than 5 cm can receive potentially curative therapy (1, 2, 51). The optimal approach to surveillance remains contentious, some arguing that ultrasound alone should be the primary procedure, others arguing that ultrasound should be combined with AFP estimation (6, 52, 53).

Our study does not determine how such a model will perform in a prospective screening setting or in a routine practice outside specialist units, but we believe that these results are sufficiently encouraging to warrant a prospective study of this model to be run in parallel with conventional staging with USS with the aim of supplanting or, more likely, enhancing USS as an effective screening procedure for hepatocellular carcinoma.

No potential conflicts of interest were disclosed.

Conception and design: P.J. Johnson, D. Palmer, S. Hussain, H. Reeves, S. Satomura

Development of methodology: P.J. Johnson, S.J. Pirrie, T.F. Cox, M. Teng, S. Hussain, S. Satomura

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): P.J. Johnson, S. Berhane, M. Teng, D. Palmer, J. Morse, G. Patman, S. Hussain, H. Reeves

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): P.J. Johnson, S.J. Pirrie, T.F. Cox, S. Berhane, M. Teng, D. Palmer, S. Hussain, J. H. Reeves, S. Satomura

Writing, review, and/or revision of the manuscript: P.J. Johnson, S.J. Pirrie, T.F. Cox, D. Palmer, S. Hussain, H. Reeves, S. Satomura

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): P.J. Johnson, S.J. Pirrie, M. Teng, D. Palmer, J. Morse, D. Hull, C. Kagebayashi, J. Graham, H. Reeves, S. Satomura

Study supervision: P.J. Johnson, H. Reeves, S. Satomura

The authors thank the Experimental Cancer Medicine Centre and Biomedical Research Unit, University of Birmingham, Birmingham, UK. The authors also thank colleagues in the Liver Unit, Cancer Centre and Chemical Pathology at University Hospitals Birmingham NHS Foundation Trust for their help in management of patients involved in this study.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Bruix
J
,
Sherman
M
. 
Management of hepatocellular carcinoma
.
Hepatology
2005
;
42
:
1208
36
.
2.
Bruix
J
,
Sherman
M
. 
Management of hepatocellular carcinoma: an update
.
Hepatology
2011
;
53
:
1020
2
.
3.
Lee
J
,
Yoon
JH
,
Joo
I
,
Woo
H
. 
Recent advances in CT and MR imaging for evaluation of hepatocellular carcinoma
.
Liver Cancer
2012
;
1
:
22
40
.
4.
Silva
MA
,
Hegab
B
,
Hyde
C
,
Guo
B
,
Buckels
JA
,
Mirza
DF
. 
Needle track seeding following biopsy of liver lesions in the diagnosis of hepatocellular cancer: a systematic review and meta-analysis
.
Gut
2008
;
57
:
1592
6
.
5.
Stigliano
R
,
Marelli
L
,
Yu
D
,
Davies
N
,
Patch
D
,
Burroughs
AK
. 
Seeding following percutaneous diagnostic and therapeutic approaches for hepatocellular carcinoma. What is the risk and the outcome? Seeding risk for percutaneous approach of HCC
.
Cancer Treat Rev
2007
;
33
:
437
47
.
6.
Bolondi
L
,
Sofia
S
,
Siringo
S
,
Gaiani
S
,
Casali
A
,
Zironi
G
, et al
Surveillance programme of cirrhotic patients for early diagnosis and treatment of hepatocellular carcinoma: a cost effectiveness analysis
.
Gut
2001
;
48
:
251
9
.
7.
Johnson
PJ
. 
The role of serum alpha-fetoprotein estimation in the diagnosis and management of hepatocellular carcinoma
.
Clin Liver Dis
2001
;
5
:
145
59
.
8.
Marrero
JA
,
El-Serag
HB
. 
Alpha-fetoprotein should be included in the hepatocellular carcinoma surveillance guidelines of the American Association for the Study of Liver Diseases
.
Hepatology
2011
;
53
:
1060
1
;
author reply 1-2
.
9.
Trevisani
F
,
D'Intino
PE
,
Morselli-Labate
AM
,
Mazzella
G
,
Accogli
E
,
Caraceni
P
, et al
Serum alpha-fetoprotein for diagnosis of hepatocellular carcinoma in patients with chronic liver disease: Influence of HBsAg and anti-HCV status
.
J Hepatol
2001
;
34
:
570
5
.
10.
Liebman
HA
. 
Isolation and characterization of a hepatoma-associated abnormal (des-gamma-carboxy)prothrombin
.
Cancer Res
1989
;
49
:
6493
7
.
11.
Ikoma
J
,
Kaito
M
,
Ishihara
T
,
Nakagawa
N
,
Kamei
A
,
Fujita
N
, et al
Early diagnosis of hepatocellular carcinoma using a sensitive assay for serum des-gamma-carboxy prothrombin: a prospective study
.
Hepatogastroenterology
2002
;
49
:
235
8
.
12.
Oda
K
,
Ido
A
,
Tamai
T
,
Matsushita
M
,
Kumagai
K
,
Mawatari
S
, et al
Highly sensitive lens culinaris agglutinin-reactive alpha-fetoprotein is useful for early detection of hepatocellular carcinoma in patients with chronic liver disease
.
Oncol Rep
2011
;
26
:
1227
33
.
13.
Toyoda
H
,
Kumada
T
,
Tada
T
. 
Highly sensitive lens culinaris agglutinin-reactive alpha-fetoprotein: a new tool for the management of hepatocellular carcinoma
.
Oncology
2011
;
81Suppl 1
:
61
5
.
14.
Tang
A
,
Cruite
I
,
Sirlin
CB
. 
Toward a standardized system for hepatocellular carcinoma diagnosis using computed tomography and MRI
.
Expert Rev Gastroenterol Hepatol
2013
;
7
:
269
79
.
15.
Gambarin-Gelwan
M
,
Wolf
DC
,
Shapiro
R
,
Schwartz
ME
,
Min
AD
. 
Sensitivity of commonly available screening tests in detecting hepatocellular carcinoma in cirrhotic patients undergoing liver transplantation
.
Am J Gastroenterol
2000
;
95
:
1535
8
.
16.
Libbrecht
L
,
Bielen
D
,
Verslype
C
,
Vanbeckevoort
D
,
Pirenne
J
,
Nevens
F
, et al
Focal lesions in cirrhotic explant livers: pathological evaluation and accuracy of pretransplantation imaging examinations
.
Liver Transpl
2002
;
8
:
749
61
.
17.
Teefey
SA
,
Hildeboldt
CC
,
Dehdashti
F
,
Siegel
BA
,
Peters
MG
,
Heiken
JP
, et al
Detection of primary hepatic malignancy in liver transplant candidates: prospective comparison of CT, MR imaging, US, and PET
.
Radiology
2003
;
226
:
533
42
.
18.
Tong
MJ
,
Blatt
LM
,
Kao
VW
. 
Surveillance for hepatocellular carcinoma in patients with chronic viral hepatitis in the United States of America
.
J Gastroenterol Hepatol
2001
;
16
:
553
9
.
19.
Yao
FY
,
Ferrell
L
,
Bass
NM
,
Watson
JJ
,
Bacchetti
P
,
Venook
A
, et al
Liver transplantation for hepatocellular carcinoma: expansion of the tumor size limits does not adversely impact survival
.
Hepatology
2001
;
33
:
1394
403
.
20.
Yu
NC
,
Chaudhari
V
,
Raman
SS
,
Lassman
C
,
Tong
MJ
,
Busuttil
RW
, et al
CT and MRI improve detection of hepatocellular carcinoma, compared with ultrasound alone, in patients with cirrhosis
.
Clin Gastroenterol Hepatol
2011
;
9
:
161
7
.
21.
El-Serag
HB
,
Kramer
JR
,
Chen
GJ
,
Duan
Z
,
Richardson
PA
,
Davila
JA
. 
Effectiveness of AFP and ultrasound tests on hepatocellular carcinoma mortality in HCV-infected patients in the USA
.
Gut
2011
;
60
:
992
7
.
22.
Singal
A
,
Volk
M
,
Waljee
A
,
Salgia
R
,
Higgins
P
,
Rogers
M
, et al
Meta-analysis: surveillance with ultrasound for early-stage hepatocellular carcinoma in patients with cirrhosis
.
Aliment Pharmacol Ther
2009
;
30
:
37
47
.
23.
McShane
LM
,
Altman
DG
,
Sauerbrei
W
,
Taube
SE
,
Gion
M
,
Clark
GM
. 
Reporting recommendations for tumor marker prognostic studies (REMARK)
.
J Natl Cancer Inst
2005
;
97
:
1180
4
.
24.
Alonzo
TA
. 
Standards for reporting prognostic tumor marker studies
.
J Clin Oncol
2005
;
23
:
9053
4
.
25.
Beale
G
,
Chattopadhyay
D
,
Gray
J
,
Stewart
S
,
Hudson
M
,
Day
C
, et al
AFP, PIVKAII, GP3, SCCA-1 and follisatin as surveillance biomarkers for hepatocellular cancer in non-alcoholic and alcoholic fatty liver disease
.
BMC Cancer
2008
;
8
:
200
.
26.
Greene
FL
,
Page
DL
,
Fleming
ID
,
Fritz
A
,
Balch
CM
,
Haller
DG
, et al
AJCC cancer staging manual
.
New York
:
Springer
; 
2002
.
27.
Sobin
LH
,
Wittekind
C
. 
TNM classification of malignant tumours
.
International Union Against Cancer
2002
.
28.
Llovet
JM
,
Burroughs
A
,
Bruix
J
. 
Hepatocellular carcinoma
.
Lancet
2003
;
362
:
1907
17
.
29.
Llovet
JM
,
Bru
C
,
Bruix
J
. 
Prognosis of hepatocellular carcinoma: the BCLC staging classification
.
Semin Liver Dis
1999
;
19
:
329
38
.
30.
Bruix
J
,
Llovet
JM
. 
Prognostic prediction and treatment strategy in hepatocellular carcinoma
.
Hepatology
2002
;
35
:
519
24
.
31.
Mazzaferro
V
,
Regalia
E
,
Doci
R
,
Andreola
S
,
Pulvirenti
A
,
Bozzetti
F
, et al
Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis
.
N Engl J Med
1996
;
334
:
693
700
.
32.
Child
CG
,
Turcotte
JG
. 
Surgery and portal hypertension
.
Major Probl Clin Surg
1964
;
1
:
1
85
.
33.
Pugh
RN
,
Murray-Lyon
IM
,
Dawson
JL
,
Pietroni
MC
,
Williams
R
. 
Transection of the oesophagus for bleeding oesophageal varices
.
Br J Surg
1973
;
60
:
646
9
.
34.
Kagebayashi
C
,
Yamaguchi
I
,
Akinaga
A
,
Kitano
H
,
Yokoyama
K
,
Satomura
M
, et al
Automated immunoassay system for AFP-L3% using on-chip electrokinetic reaction and separation by affinity electrophoresis
.
Anal Biochem
2009
;
388
:
306
11
.
35.
Royston
P
,
Altman
DG
. 
Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling
.
Applied Statistics
1994
;
43
:
429
67
.
36.
El-Serag
HB
,
Davila
JA
. 
Surveillance for hepatocellular carcinoma: in whom and how
?
Therap Adv Gastroenterol
2011
;
4
:
5
10
.
37.
Lai
Q
,
Melandro
F
,
Pinheiro
RS
,
Donfrancesco
A
,
Fadel
BA
,
Levi Sandri
GB
, et al
Alpha-fetoprotein and novel tumor biomarkers as predictors of hepatocellular carcinoma recurrence after surgery: a brilliant star raises again
.
Int J Hepatol
2012
;
2012
:
893103
.
38.
Sherman
M
. 
Serological surveillance for hepatocellular carcinoma: time to quit
.
J Hepatol
2010
;
52
:
614
5
.
39.
Leerapun
A
,
Suravarapu
SV
,
Bida
JP
,
Clark
RJ
,
Sanders
EL
,
Mettler
TA
, et al
The utility of Lens culinaris agglutinin-reactive alpha-fetoprotein in the diagnosis of hepatocellular carcinoma: evaluation in a United States referral population
.
Clin Gastroenterol Hepatol
2007
;
5
:
394
402
;
quiz 267
.
40.
Toyoda
H
,
Kumada
T
,
Tada
T
,
Kaneoka
Y
,
Maeda
A
,
Kanke
F
, et al
Clinical utility of highly sensitive Lens culinaris agglutinin-reactive alpha-fetoprotein in hepatocellular carcinoma patients with alpha-fetoprotein < 20 ng/mL
.
Cancer Sci
2011
;
102
:
1025
31
.
41.
Sterling
RK
,
Jeffers
L
,
Gordon
F
,
Sherman
M
,
Venook
AP
,
Reddy
KR
, et al
Clinical utility of AFP-L3% measurement in North American patients with HCV-related cirrhosis
.
Am J Gastroenterol
2007
;
102
:
2196
205
.
42.
Sterling
RK
,
Jeffers
L
,
Gordon
F
,
Venook
AP
,
Reddy
KR
,
Satomura
S
, et al
Utility of Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein and des-gamma-carboxy prothrombin, alone or in combination, as biomarkers for hepatocellular carcinoma
.
Clin Gastroenterol Hepatol
2009
;
7
:
104
13
.
43.
Toyoda
H
,
Kumada
T
,
Osaki
Y
,
Oka
H
,
Kudo
M
. 
Role of tumor markers in assessment of tumor progression and prediction of outcomes in patients with hepatocellular carcinoma
.
Hepatol Res
2007
;
37
Suppl 2
:
S166
71
.
44.
Yamaguchi
I
,
Nakamura
K
,
Kitano
H
,
Masuda
Y
,
Kanke
F
,
Kobatake
S
, et al
Development of des-gamma-carboxy prothrombin (DCP) measuring reagent using the LiBASys clinical analyzer
.
Clin Chem Lab Med
2008
;
46
:
411
6
.
45.
Durazo
FA
,
Blatt
LM
,
Corey
WG
,
Lin
JH
,
Han
S
,
Saab
S
, et al
Des-gamma-carboxyprothrombin, alpha-fetoprotein and AFP-L3 in patients with chronic hepatitis, cirrhosis and hepatocellular carcinoma
.
J Gastroenterol Hepatol
2008
;
23
:
1541
8
.
46.
Carr
BI
,
Kanke
F
,
Wise
M
,
Satomura
S
. 
Clinical evaluation of lens culinaris agglutinin-reactive alpha-fetoprotein and des-gamma-carboxy prothrombin in histologically proven hepatocellular carcinoma in the United States
.
Dig Dis Sci
2007
;
52
:
776
82
.
47.
Marrero
JA
,
Feng
Z
,
Wang
Y
,
Nguyen
MH
,
Befeler
AS
,
Roberts
LR
, et al
Alpha-fetoprotein, des-gamma carboxyprothrombin, and lectin-bound alpha-fetoprotein in early hepatocellular carcinoma
.
Gastroenterology
2009
;
137
:
110
8
.
48.
Lok
AS
,
Sterling
RK
,
Everhart
JE
,
Wright
EC
,
Hoefs
JC
,
Di Bisceglie
AM
, et al
Des-gamma-carboxy prothrombin and alpha-fetoprotein as biomarkers for the early detection of hepatocellular carcinoma
.
Gastroenterology
2010
;
138
:
493
502
.
49.
Kumada
T
,
Toyoda
H
,
Kiriyama
S
,
Tanikawa
M
,
Hisanaga
Y
,
Kanamori
A
, et al
Predictive value of tumor markers for hepatocarcinogenesis in patients with hepatitis C virus
.
J Gastroenterol
2011
;
46
:
536
44
.
50.
Okuda
H
,
Saito
A
,
Shiratori
K
,
Yamamoto
M
,
Takasaki
K
,
Nakano
M
. 
Clinicopathologic features of patients with primary malignant hepatic tumors seropositive for alpha-fetoprotein-L3 alone in comparison with other patients seropositive for alpha-fetoprotein-L3
.
J Gastroenterol Hepatol
2005
;
20
:
759
64
.
51.
Llovet
JM
. 
Updated treatment approach to hepatocellular carcinoma
.
J Gastroenterol
2005
;
40
:
225
35
.
52.
Lin
OS
,
Keeffe
EB
,
Sanders
GD
,
Owens
DK
. 
Cost-effectiveness of screening for hepatocellular carcinoma in patients with cirrhosis due to chronic hepatitis C
.
Aliment Pharmacol Ther
2004
;
19
:
1159
72
.
53.
Zhang
BH
,
Yang
BH
,
Tang
ZY
. 
Randomized controlled trial of screening for hepatocellular carcinoma
.
J Cancer Res Clin Oncol
2004
;
130
:
417
22
.