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Construction of Predictive and Prognostic Nomograms for Distant Metastases in Hepatocellular Carcinoma Based on SEER Database

  • Guo-Le Nie1,#,
  • Wei Luo2,#,
  • Jun Yan1,2,3,
  • Hai-Ping Wang1,2,3 and
  • Xun Li1,2,3,*
 Author information
Cancer Screening and Prevention 2022;():-

doi: 10.14218/CSP.2022.00006

Abstract

Background and objectives

The most often encountered kind of liver cancer is hepatocellular carcinoma (HCC), in which distant metastases (DM) continue to result in a worse prognosis. The present study aims to determine the prognostic and predictive factors of DM in patients with HCC, and generate two nomograms to assess the occurrence probability of DM and prognosis of HCC patients with DM.

Methods

From 2010 to 2015, the data of patients who were diagnosed with HCC in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively reviewed. The multivariate and univariate logistic regression analyses were assessed to determine the risk factors correlated to the occurrence of DM in patients with HCC. Then, the multivariate and univariate analyses of the Cox regression were assessed to determine prognostic risk factors of DM in HCC patients, and two nomograms were constructed.

Results

The present study included 14,508 participants with HCC. Gender, T stage, N stage and tumor size were determined as independent risk factors correlated to the occurrence of DM in HCC patients, while T stage, N stage, surgery, radiation and chemotherapy were determined as independent prognostic factors in HCC patients with DM. The AUC for the diagnostic nomogram was 0.766 in the training group and 0.776 in the testing group. At 6, 9 and 12 months, the AUC for the prognostic nomogram was 0.732, 0.727 and 0.719, respectively, in the training group, and 0.697, 0.722 and 0.731, respectively, in the testing group.

Conclusions

The two nomograms developed to assess the occurrence of DM in HCC patients and prognosis of HCC patients with DM may benefit clinician decision-making and clinical patient prognosis.

Keywords

Hepatocellular carcinoma, Distant metastasis, Nomogram, Prognosis, Diagnosis

Introduction

The most frequent kind of cancer and the third leading cause of death from cancer worldwide is hepatocellular carcinoma (HCC).1 Approximately 14.0–36.7% of patients have distant metastases at the time of first diagnosis, and the five-year overall survival (OS) for HCC patients remains below 20%.2,3 Furthermore, the lungs, brain and bones are the most often extrahepatic metastasized locations in HCC. Although the clinical treatment and diagnosis of HCC patients have considerably improved in the last few years, the prognosis for patients with HCC, who also have distant metastases (DM), remains dismal.4 As a result, predicting models for estimating DM in HCC patients and the prognosis of HCC patients with DM are required. Numerous risk factors and prognostic variables have been identified in prior studies, such as higher T stage, surgery, tumor size, etc.5,6 However, limited studies have focused on the predictive and prognostic nomograms of DM in HCC patients. A nomogram is a convenient and effective tool for clinical management and diagnostics, because this allows for the prediction and quantification of the probability of clinical patient outcomes.7 The present study aims to construct diagnostic and prognostic nomograms for DM in HCC patients based on data obtained from the SEER database.

Materials and methods

Patients and study design

The data of HCC patients were extracted from 18 registry studies in the SEER database from 2010 to 2015. Inclusion criteria: (1) patients with a pathological diagnosis of HCC, with the liver as the primary site; (2) the demographic data of patients, such as race, age and gender, is accessible; (3) the tumor features, such as T stage, M stage, N stage and tumor size, are available. Exclusion criteria: missing information on the demographic data, T stage, N stage and M stage of the tumor, and missing or less than one month OS. Finally, a total of 14,508 HCC patients, which included 1,638 patients diagnosed with DM, was included for the present study. In order to build the diagnostic nomogram, all patients were examined to identify the risk factors for DM. In addition, all HCC patients with DM were evaluated to determine the prognostic factors, and construct the prognostic nomogram.

All HCC patients and HCC patients with DM were randomly divided into two groups, at a ratio of 3:2: training group and testing group. For the predictive and prognostic risk factors, the training group was used to construct the nomogram, and the testing group was used to verify the nomogram.

Data collection

In the present study, the characteristics used to detect the risk factors for developing DM in patients with HCC included the following: age, gender, race, T stage, N stage, histological type, and tumor size. In addition, three treatment-related variables (radiotherapy, surgery and chemotherapy) were included in the survival analysis. The major outcome measure in this section was OS, which was described as the gap of time between the diagnosis date and death date from any cause.

Statistical analysis

The SPSS 19.0 and R software (version 4.1.2) were applied to conduct all statistical analyses in the present study. Chi-square test was utilized to compare the distribution of characteristics between the testing and training groups. The statistical significance was defined as p < 0.05 (both sides).

Univariate logistic regression analysis was performed to determine the factors linked with DM in the diagnostic group, and variables with p < 0.05 were included in the multivariate logistic regression analysis to identify the risk factors correlated to DM in patients with HCC. The multivariate Cox regression analysis included the significant variables from the univariate Cox regression analysis, and determined the correlated prognostic factors for HCC patients with DM.

The prognostic and predictive nomograms were established using the “rms” package in the R software. In addition, the receiver operating characteristic (ROC) curve for the nomogram was drawn, the independent prognostic risk factors were identified, and the corresponding area under the curve (AUC) was used to determine the discrimination of the nomogram and risk factors.8 In order to further assess the nomograms, decision curve analysis (DCA) was used, and the calibration curve was plotted.9 Finally, all HCC patients with DM were divided into two groups based on their median risk score: high-risk and low-risk groups. Then, the Kaplan-Meier (K-M) survival curve was plotted, and log-rank test was performed to determine the difference in OS between the two groups.10

Results

Clinical and pathological characteristics of the study population

A total of 14,508 HCC patients were included for the present study. These patients were divided into two groups: training group (8,705 patients) and testing group (5,803 patients). The baseline clinical and pathological characteristics of HCC patients are presented in Table 1. The Chi-square test demonstrated that the difference in the two groups was entirely random (p > 0.05).

Table 1

Demographic and pathological information of HCC patients

CharacteristicsTraining group (n = 8,705)Testing group (n = 5,803)X2p
Age, years1.2060.272
  ≤654,987 (57.3%)3,271 (56.37%)
  >653,718 (42.7%)2,532 (43.63%)
Gender0.0890.766
  Female2,035 (23.4%)1,369 (23.59%)
  Male6,670 (76.6%)4,434 (76.41%)
Race1.8340.400
  Black1,219 (14%)769 (13.25%)
  Other1,535 (17.6%)1,017 (17.53%)
  White5,951 (68.4%)4,017 (69.22%)
T0.1960.987
  T14,292 (49.3%)2,867 (49.41%)
  T22,040 (23.4%)1,372 (23.64%)
  T32,092 (24%)1,378 (23.75%)
  T4281 (3.2%)186 (3.21%)
N1.1110.292
  N08,116 (93.2%)5,384 (92.78%)
  N1589 (6.8%)419 (7.22%)
M0.0000.992
  M07,722 (88.7%)5,148 (88.71%)
  M1983 (11.3%)655 (11.29%)
Tumor size, mm0.3920.822
  ≤201,056 (12.1%)712 (12.27%)
  >503,883 (44.6%)2,558 (44.08%)
  20–503,766 (43.3%)2,533 (43.65%)
Histological type0.0200.887
  81708,522 (97.9%)5,683 (97.93%)
  Other183 (2.1%)120 (2.07%)

Risk factors of distant metastasis in HCC patients

In the present study, 1,638 patients were diagnosed with DM (11.29%) and 12,870 patients (88.71%) were diagnosed without DM. The six predictors screened by the univariate logistic analysis (p < 0.05) were further analyzed using multivariate logistic analysis, and four DM-related characteristics were identified: gender, T stage, N stage and tumor size (Table 2 and Fig. 1a).

Table 2

Univariate and multivariate logistic analysis of distant metastasis in HCC patients

CharacteristicsUnivariate analysis
Multivariate analysis
OR95% CIpOR95% CIp
Age, years
  ≤65Reference
  >650.9410.848–1.0440.252
Gender
  FemaleReferenceReference
  Male1.3441.180–1.5300.0001.2321.072–1.4170.003
Race
  BlackReferenceReference
  Other0.7580.630–0.9110.0030.8360.686–1.0190.076
  White0.8660.749–1.0020.0530.9350.799–1.0940.400
T
  T1ReferenceReference
  T21.2501.066–1.4640.0061.4041.186–1.6620.000
  T34.2603.759–4.8270.0002.2341.933–2.5810.000
  T48.4776.858–10.4790.0004.5153.576–5.7010.000
N
  N0ReferenceReference
  N19.3158.118–10.6880.0006.1375.305–7.0980.000
Tumor size, mm
  ≤20ReferenceReference
  20–501.4261.110–1.8320.0061.2820.994–1.6550.056
  >504.9103.874–6.2230.0002.6522.052–3.4270.000
Histological type
  8170ReferenceReference
  Other1.6481.217–2.2320.0011.1610.823–1.6390.395
Forest plot for the multivariate logistic (a) and multivariate cox (b) analysis.
Fig. 1  Forest plot for the multivariate logistic (a) and multivariate cox (b) analysis.

Construction and validation of the predictive nomogram

Gender, T stage, N stage and tumor size were identified as independent risk factors correlated to DM in the multivariate logistic regression analysis. Based on these independent characteristics, a nomogram for the DM risk assessment of HCC patients was constructed (Fig. 2a). Meanwhile, the ROC curve was plotted for the training and testing groups, and the AUC for the nomogram in the training and testing groups was 0.766 and 0.776, respectively (Fig. 2b, e). Moreover, we also plotted the). Furthermore, ROC curves were plotted for each independent risk factor (Fig. 2). The calibration curves revealed the excellent calibration of the nomogram in both groups (Fig. 2c, f). Furthermore, the DCA indicated that the nomogram is a robust clinical decision-making tool for DM in HCC patients (Fig. 2d, g). Moreover, the AUCs for all risk factors were lower than that for the nomogram, in both the training (Fig. 3a) and testing (Fig. 3b) groups.

The nomogram to evaluate the risk of DM in HCC patients (a); The ROC (b), calibration curve (c) and DCA (d) for the training group, and the ROC (e), calibration curve (f) and DCA (g) for the testing group.
Fig. 2  The nomogram to evaluate the risk of DM in HCC patients (a); The ROC (b), calibration curve (c) and DCA (d) for the training group, and the ROC (e), calibration curve (f) and DCA (g) for the testing group.
Comparison of AUCs between the nomogram and all independent risk factors, including gender, race, T stage, N stage and tumor size, in the training group (a) and testing group (b).
Fig. 3  Comparison of AUCs between the nomogram and all independent risk factors, including gender, race, T stage, N stage and tumor size, in the training group (a) and testing group (b).

Prognostic factors for HCC patients with DM

The prognostic factors for HCC with DM were investigated in the present study by evaluating 1,638 patients. Among these patients, there were more male patients (80.95%) than female patients (19.05%). Furthermore, 68.80% of these patients were white, 15.57% of these patients were black, and 15.63% of these patients were of other race. The details of all HCC patients with DM are presented in Table 3. The multivariate and univariate Cox regression results are presented in Table 4 and Figure 1b. T stage, N stage, surgery, radiation and chemotherapy were identified as independent prognostic variables for DM in patients with HCC.

Table 3

Demographic and pathological information of HCC patients with DM

CharacteristicsTraining group (n = 984)Testing group (n = 654)X2p
Age, years2.7120.100
  ≤65557 (56.6%)397 (60.7%)
  >65427 (43.4%)257 (39.3%)
Gender1.1730.279
  Female179 (18.2%)133 (20.34%)
  Male805 (81.8%)521 (79.66%)
Race1.0900.580
  Black146 (14.8%)109 (16.67%)
  Other153 (15.5%)103 (15.75%)
  White685 (69.6%)442 (67.58%)
T0.7560.860
  T1271 (27.5%)174 (26.61%)
  T2161 (16.4%)100 (15.29%)
  T3454 (46.1%)310 (47.4%)
  T498 (10%)70 (10.7%)
N0.7070.401
  N0708 (72%)458 (70.03%)
  N1276 (28%)196 (29.97%)
Tumor size, mm0.1470.929
  ≤2045 (4.6%)32 (4.89%)
  20–50229 (23.3%)155 (23.7%)
  >50710 (72.2%)467 (71.41%)
Histological type0.4150.520
  8170955 (97.1%)631 (96.48%)
  Other29 (2.9%)23 (3.52%)
Surgery0.1100.740
  No916 (93.1%)606 (92.66%)
  Yes68 (6.9%)48 (7.34%)
Radiation1.5170.218
  No732 (74.4%)504 (77.06%)
  Yes252 (25.6%)150 (22.94%)
Chemotherapy0.0040.947
  No450 (45.7%)298 (45.57%)
  Yes534 (54.3%)356 (54.43%)
Table 4

Univariate and multivariate Cox analysis for HCC patients with DM

CharacteristicsUnivariate analysis
Multivariate analysis
HR95% CIpHR95% CIp
Age, years
  ≤65Reference
  >651.0370.939–1.1470.473
Gender
  FemaleReference
  Male1.0340.911–1.1730.606
Race
  BlackReference
  Other1.0220.855–1.220.813
  White0.9890.861–1.1350.871
T
  T1ReferenceReference
  T21.0800.924–1.2620.3351.1850.996–1.4110.056
  T31.3611.207–1.5360.0001.2371.089–1.4050.001
  T41.2331.028–1.480.0241.0950.908–1.320.344
N
  N0ReferenceReference
  N11.2671.137–1.4130.0001.1601.037–1.2970.009
Tumor size, mm
  ≤20ReferenceReference
  20–501.0400.806–1.3420.7601.0350.801–1.3360.794
  >501.3241.041–1.6860.0221.2550.967–1.630.087
Histological type
  8170Reference
  Other0.7970.601–1.0560.114
Surgery
  NoReferenceReference
  Yes0.3720.301–0.4590.0000.3270.264–0.4050.000
Radiation
  NoReferenceReference
  Yes0.7620.679–0.8550.0000.7680.684–0.8640.000
Chemotherapy
  NoReferenceReference
  Yes0.6500.588–0.7180.0000.5830.526–0.6450.000

Construction of the prognostic nomogram for HCC patients with DM

According to the independent prognostic factors, the nomogram for the prognosis of HCC patients with DM was constructed (Fig. 4). Then, the ability of the nomogram, and the independent factors to discriminate the prognosis of patients in the training (Fig. 5a–c) and testing group (Fig. 5d–f), and thegroups were assessed. The AUC for the nomogram was significantly greater, when compared to that for all independent prognostic factors, at 6, 9 and 12 months. Furthermore, the 6-, 9- and 12-month OS probability calibration curves demonstrated a strong agreement between the predicted OS of the nomogram and the actual result for HCC patients with DM, both in the training and testing groups (Figs. 6a–c and 7a–c). Moreover, the DCA curves revealed that the nomogram has good predictive efficiency for OS in HCC patients with DM in the training and testing groups (Fig. 6d–f and Fig. 7d–f). The AUC for the nomogram was 0.732, 0.727 and 0.719 (Fig. 8a) in the training group and 0.697, 0.722 and 0.731 (Fig. 8c) in the testing group at 6, 9 and 12 months, respectively. According to the K-M survival curves, patients categorized as high-risk have a poorer prognosis, when compared to those classified as low-risk (Fig. 8b, d).

A prognostic nomogram for predicting the OS of HCC patients with DM at 6, 9 and 12 months.
Fig. 4  A prognostic nomogram for predicting the OS of HCC patients with DM at 6, 9 and 12 months.
The time-dependent ROC curve analysis for the nomogram and independent prognostic factors at 6, 9 and 12 months in the training group (a–c) and testing group (d–f).
Fig. 5  The time-dependent ROC curve analysis for the nomogram and independent prognostic factors at 6, 9 and 12 months in the training group (a–c) and testing group (d–f).
Calibration curves for the nomogram at 6 (a), 9 (b) and 12 months (c) in the training group; The DCA for the nomogram at 6 (d), 9 (e) and 12 months (f) in the training group.
Fig. 6  Calibration curves for the nomogram at 6 (a), 9 (b) and 12 months (c) in the training group; The DCA for the nomogram at 6 (d), 9 (e) and 12 months (f) in the training group.
Calibration curves for the nomogram at 6 (a), 9 (b) and 12 (c) months in the testing group; The DCA for the nomogram at 6 (d), 9 (e) and 12 (f) months in the testing group.
Fig. 7  Calibration curves for the nomogram at 6 (a), 9 (b) and 12 (c) months in the testing group; The DCA for the nomogram at 6 (d), 9 (e) and 12 (f) months in the testing group.
The time-dependent ROC curve analysis for the nomogram at 6, 9 and 12 months in the training group (a) and testing group (c); The Kaplan-Meier survival curves for patients in the training group (b) and testing group (d).
Fig. 8  The time-dependent ROC curve analysis for the nomogram at 6, 9 and 12 months in the training group (a) and testing group (c); The Kaplan-Meier survival curves for patients in the training group (b) and testing group (d).

Discussion

HCC is a common malignant tumor that is highly aggressive and susceptible to DM. The incidence of DM from HCC is 14–37%, and the existence of DM is commonly coupled with a poor clinical prognosis.11 The intra-abdominal lymph nodes, peritoneum, lungs and bones are all common metastasis sites in HCC. Although surgical resection, locoregional, and systemic treatments can improve the OS of HCC patients, the prognosis of these patients remains poor.12,13 Therefore, the prognostic and risk factors must be identified to establish the early diagnosis, and estimate the prognosis of DM in HCC patients. Nomograms were developed and assessed to determine the occurrence possibility of DM in HCC patients, and a prognostic nomogram was constructed for HCC patients with DM. The total score may be derived from the nomogram by acquiring a number of easily accessible criteria from each HCC patient. Scores associated with the prognosis can be estimated using a prognostic nomogram. These nomograms can be facilitated for personalized clinical decisions and clinical management.

Even though the OS was low in HCC patients with DM, earlier detection and prevention can significantly improve the prognosis. Therefore, it is critical to determine the clinical and molecular risk factors for HCC with DM. The expression of FOXC1,14 LncRNA CDKN2BAS,15 and exosomal circRNA-10033816 were determined to be correlated with DM in HCC, and serum long noncoding RNAs were constructed for the early detection of metastasis in HCC.17 However, these molecules are difficult to apply in clinical decision-making and treatment. In a clinical and pathological features research, Yan et al. reported that hepatitis B virus (HBV) DNA loading, portal hypertension, Barcelona liver clinic (BCLC) stage, Child-Pugh classification, and computerized tomography (CT) imaging features are independent risk factors for metastasis in patients with HCC.18 Furthermore, for HCC patients, Hu et al. reported that gender, histological grade, T stage and N stage are independent risk factors of bone metastasis.19 In the present study, the most recent thorough clinical data were collected from a large sample obtained from the SEER database, and it was determined that the prevalence of DM was 11.29%. Furthermore, the findings of the present study revealed that gender, T stage, N stage and tumor size are independent risk factors for DM in HCC. Earlier studies have established a link between DM in HCC patients and these factors. The feature of the nomogram is that it has a larger discriminating power, when compared to any single predictor, demonstrating the necessity of an integrated prediction model.

In addition, the present study revealed that HCC patients with DM, who had a higher T stage, local lymph node metastasis, or no surgery, radiation, or chemotherapy, had the worse OS. Based on the independent prognostic factors, a prognostic nomogram was constructed, and these exhibited a higher discriminatory power, when compared to any of the independent prognostic variables. The prognostic nomogram may be effective in identifying the survival probability of HCC patients with poorer OS. Similarly, earlier studies have established a link between tumor size, stage and prognosis in patients with HCC. Liang et al. reported that a tumor diameter of more than 5 cm is an independent risk factor for tumor recurrence and long-term survival in patients with HCC.20 Furthermore, Zhang et al. reported that tumor size, but not small vessel infiltration, can be applied to predict the survival of HCC patients.21 In the present study, tumor size was a prognostic factor, rather than an independent characteristic.

The present study revealed that radiotherapy is an independent prognostic factor for HCC patients with DM. Stereotactic body radiation therapy (SBRT) is one of the treatment modalities for small HCC. Related studies have revealed that the OS rate for SBRT in small HCC is approximately 60–70%.22,23 SBRT has a similar OS rate for small HCC, when compared to radiofrequency ablation and surgery, and has a lower local recurrence rate, when compared to radiofrequency ablation.24,25 For bone or soft tissue metastases,26 lung metastases,27 and brain metastases28 in HCC, external radiotherapy can benefit clinical patients by reducing the size of the metastases and relieving symptoms.

Previous prediction nomograms for HCC patients have focused on elderly or young HCC patients.29,30 However, the sample size of these studies wass small. Furthermore, tumor progression-associated genes may play an important role in the prognosis of HCC patients.3133 Although similar studies have been reported, the two nomograms in the present study, which were constructed on the basis of several characteristics that can be easily obtained from clinical records, can be used as convenient tools for assessing the prognosis of HCC patients in clinical practice. However, the present study had some limitations. First, the limited number of DM patients (n = 1 638) may have led to possible errors. Second, the SEER database does not provided access to complete laboratory information, which would be explored in future studies. Third, the present study was retrospective in nature, with unavoidable selection bias.

Conclusions

The present study revealed that gender, race, T stage, N stage and tumor size are risk factors for DM in HCC. For HCC patients with DM, T stage, N stage, surgery, radiation and chemotherapy were determined as independent prognostic factors. The two established nomograms may be useful and effective visual tools for the prognostic assessment of DM in patients with HCC.

Abbreviations

AUC: 

area under the curve

BCLC: 

Barcelona liver clinic

CT: 

computerized tomography

DCA: 

decision curve analysis

DM: 

distant metastases

HBV: 

hepatitis B virus

HCC: 

hepatocellular carcinoma

OS: 

overall survival

ROC: 

receiver operating characteristic

SBRT: 

stereotactic body radiation therapy

SEER: 

surveillance, epidemiology and end results

Declarations

Acknowledgement

We appreciate the dedication and efforts of the staff involved in the SEER database.

Ethical statement

The study does not contain any experiments on humans or animals, and/or the use of human tissue samples performed by any of the authors. Medical ethical approval and informed permission were waived for the analysis of data not recognized in the SEER database.

Data sharing statement

The dataset obtained from the SEER database, which was generated and/or analyzed in the study, is available in the SEER dataset repository (https://seer.cancer.gov/). No additional information is available.

Funding

The study was supported in part by grants from the National Natural Science Foundation of China (No. 82060119), the Health Sector Plan of Gansu Province (GSWSKY2016-27), and the Lanzhou University Basic Research Business Fund of Central Universities (lzujbky-2021-kb37).

Conflict of interest

Prof. Xun Li has been an editorial board member of Cancer Screening and Prevention since March 2022. The authors declare no other competing interests.

Authors’ contributions

GLN and WL conceived and designed the study. JY and GLN performed the literature search. GLN and HPW generated the figures and tables, and analyzed the data. GLN wrote the manuscript, JY critically reviewed the manuscript, and XL supervised the research.

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  • Cancer Screening and Prevention
  • pISSN 2993-6314
  • eISSN 2835-3315
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Construction of Predictive and Prognostic Nomograms for Distant Metastases in Hepatocellular Carcinoma Based on SEER Database

Guo-Le Nie, Wei Luo, Jun Yan, Hai-Ping Wang, Xun Li
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