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Cancer Communications

, 39:55 | Cite as

A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy

  • Yang-Xun Pan
  • Jian-Cong Chen
  • Ai-Ping Fang
  • Xiao-Hui Wang
  • Jin-Bin Chen
  • Jun-Cheng Wang
  • Wei He
  • Yi-Zhen Fu
  • Li Xu
  • Min-Shan Chen
  • Yao-Jun Zhang
  • Qi-Jiong LiEmail author
  • Zhong-Guo ZhouEmail author
Open Access
Original article
  • 79 Downloads

Abstract

Background

Patients with hepatocellular carcinoma (HCC) undergoing surgical resection still have a high 5-year recurrence rate (~ 60%). With the development of laparoscopic hepatectomy (LH), few studies have compared the efficacy between LH and traditional surgical approach on HCC. The objective of this study was to establish a nomogram to evaluate the risk of recurrence in HCC patients who underwent LH.

Methods

The clinical data of 432 patients, pathologically diagnosed with HCC, underwent LH as initial treatment and had surgical margin > 1 cm were collected. The significance of their clinicopathological features to recurrence-free survival (RFS) was assessed, based on which a nomogram was constructed using a training cohort (n = 324) and was internally validated using a temporal validation cohort (n = 108).

Results

Hepatitis B surface antigen (hazard ratio [HR], 1.838; P = 0.044), tumor number (HR, 1.774; P = 0.003), tumor thrombus (HR, 2.356; P = 0.003), cancer cell differentiation (HR, 0.745; P = 0.080), and microvascular tumor invasion (HR, 1.673; P  =0.007) were found to be independent risk factors for RFS in the training cohort, and were used for constructing the nomogram. The C-index for RFS prediction in the training cohort using the nomogram was 0.786, which was higher than that of the 8th edition of the American Joint Committee on Cancer TNM classification (C-index, 0.698) and the Barcelona Clinic Liver Cancer staging system (C-index, 0.632). A high consistency between the nomogram prediction and actual observation was also demonstrated by a calibration curve. An improved predictive benefit in RFS and higher threshold probability of the nomogram were determined by receiver operating characteristic curve analysis, which was also confirmed in the validation cohort compared to other systems.

Conclusions

We constructed and validated a nomogram able to quantify the risk of recurrence after initial LH for HCC patients, which can be clinically implemented in assisting the planification of individual postoperative surveillance protocols.

Keywords

Hepatocellular carcinoma Laparoscopic hepatectomy Recurrence Nomogram American Joint Committee on Cancer TNM classification Barcelona Clinic Liver Cancer staging system Hepatitis B surface antigen Tumor thrombus Tumor invasion 

Abbreviations

HCC

hepatocellular carcinoma

LH

laparoscopic hepatectomy

RFS

recurrence-free survival

AJCC

American Joint Committee on Cancer

BCLC

Barcelona Clinic Liver Cancer

SYSUCC

Sun Yat-sen University Cancer Center

HBsAg

hepatitis B surface antigen

ALT

alanine aminotransferase

AST

aspartate transaminase

ALB

albumin

AFP

alpha fetoprotein

CA 19-9

carbohydrate antigen 19-9

HGB

hemoglobin

MVI

microvascular tumor invasion

CT

computed tomography

MRI

magnetic resonance imaging

ROC

receiver operating characteristic

IQR

interquartile range

HR

hazard ratio

IC

confidence interval

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally, ranked as the fifth common malignancy and the second leading cause of cancer-related mortality [1, 2]. According to several guidelines, hepatectomy is recommended as a curative treatment for patients with solitary liver cancer and well-preserved liver function [3, 4]. Despite curative resection, the long-term prognosis of HCC patients is still unsatisfactory, with an extremely high recurrence rate exceeding 70% at 5 years, even in patients with HCC of size ≤ 5 cm [5, 6]. It is believed that the limitations of hepatectomy result in potential HCC recurrence due to residual cancer in the remnant liver or the possibility of de novo HCC recurrence induced by hepatitis B virus infection [7]. To ensure the appropriate management of patients after HCC resection for optimal survival prolongation, data on clinical, surgical, and pathological characteristics should be conjointly used for accurate survival prognostication to optimize individualized treatment planning.

Recent studies on laparoscopic surgery have consistently shown comparable outcomes to conventional surgery for hepatectomy in treating HCC, meanwhile, laparoscopic hepatectomy (LH) also possesses the advantages of having minimal surgical invasiveness and faster recovery [8, 9]. Although the recurrence rates of patients who underwent laparoscopic and conventional hepatectomy are similar, with 3-year disease-free survival rates ranging from 72.5% to 50%, prognostic factors for recurrence in these two groups of patients differ because of differentiation in patient selection for surgery and the operating techniques used [8]. To this end, Li et al. [9], Nakagawa et al. [10], and Umeda et al. [11] developed scoring systems for patients subjected to conventional hepatectomy to evaluate their recurrence probabilities, based on clinical and pathological variables. To develop the most cost-effective, postoperative surveillance protocol, there is a need to stratify the risks of recurrence in HCC patients after LH. However, few scoring systems have focused on laparoscopic hepatectomy (LH) for predicting recurrence-free survival (RFS).

Currently, the 8th edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system and the Barcelona Clinic Liver Cancer (BCLC) Classification are based on the pathological information, and the treatment regimens for HCC patients are established according to these staging systems [10, 11]. Several studies pointed out that patients who were allocated to the same treatment according to similar disease characteristics had completely different clinical outcomes [10, 12]. This indicated that the present staging systems are inadequate for predicting recurrence and do not accurately reflect the biological heterogeneity of HCC patients. Therefore, a comprehensive, easy-to-use tool able to estimate individual risk by incorporating pathological and clinical factors could serve as a valuable decision-making tool for clinicians.

The aim of this study was to formulate and validate a predictive model capable of predicting the RFS of HCC patients after LH which in turn can be used to guide individualized post-LH surveillance protocols.

Methods

Patients and clinicopathologic data

Clinical records of patients with primary HCC diagnosed pathologically between January 2013 and January 2018 were retrieved from the information system of our cancer center. All patients had received LH. The clinical characteristics, liver function tests, intraoperative and pathological outcomes were recorded during hospitalization. The inclusion criteria were as follows: (1) patients who underwent LH with surgical margin > 1 cm as initial treatment and did not receive any preoperative treatment [13]; (2) patients with clear pathological diagnosis of HCC after LH; (3) patients with complete clinicopathological and follow-up data; and (4) patients who recovered from the operation and survived for > 1 month postoperatively. In addition, patients were excluded if they met the following exclusion criteria: (1) the surgical margin < 1 cm; (2) non-HCC diagnosis according to postoperative pathology; (3) had LH conversion to open hepatectomy during the LH operation; (4) perioperative death; and (5) had missing clinical data. The tumor stage was evaluated according to the 8th edition of the AJCC TNM classification and the BCLC staging system [10, 14]. The study protocol was approved by the Clinical Research Ethics Committee of Sun Yat-sen University Cancer Center (SYSUCC, Guangzhou, China), and all patients provided written informed consent (B2019-129-01).

Clinicopathological data, including age, gender, hepatitis B surface antigen (HBsAg), total bilirubin, alanine aminotransferase (ALT), aspartate transaminase (AST), albumin (ALB), alpha-fetoprotein (AFP), carbohydrate antigen 19-9 (CA19-9), hemoglobin (HGB), platelet count, international normalized ratio for prothrombin time, hepatitis B virus-DNA (HBV-DNA) copy number, liver macronodular cirrhosis (irregular nodules with a variation greater than 3 mm in diameter), intraoperative blood loss, portal vein embolization, surgical procedure, tumor size, tumor multiplicity, tumor encapsulation, tumor boundary, tumor thrombus, cancer cell differentiation, 8th AJCC TNM stage, microvascular tumor invasion (MVI), BCLC stage, Child–Pugh score, hospital stay, and operative time, were collected.

Follow-up and study endpoints

All HCC patients were advised to receive regular follow-ups after completion of the primary therapy according to clinical guidelines [3]. Patients were generally followed up every 3 months in the first 2 years and every 6 months thereafter if no evidence of recurrence appeared in the following 3 to 5 years. For each follow-up, serological and imaging examinations were performed, including serum AFP, liver function test, routine blood test, computed tomography (CT), to monitor lung metastasis, and magnetic resonance imaging (MRI), to monitor intrahepatic recurrence. RFS was defined as the time interval between the date of operation and the date of the diagnosis of recurrence. For patients without any evidence of recurrence, the last follow-up date was December 31, 2018.

Statistical analysis

The prognostic factors for RFS were identified using the R software (version 3.5.2; https://www.r-project.org/). The difference between the training cohort and the validation cohort was compared. Chi-square test or Fisher’s exact test was used to compare categorical variables. Continuous variables with normal distribution were compared using the Student’s t test, or the Mann–Whitney U test was used for variables with abnormal distribution. RFS curves were depicted using the Kaplan–Meier method and compared using the log-rank test. Variables were converted to categorized variables for univariable analysis, and the factors that showed significant associations with recurrence in the univariate logistic models were subsequently included in the multivariate Cox regression model to identify independent prognostic factors through backward selection. All reported P values are two-sided, and P < 0.05 was considered significant, unless stated.

Nomogram

The patients were divided into the training and validation cohorts. After significant factors related to RFS in the training cohort were identified through multivariate analyses (P < 0.10), a nomogram for predicting the 1-, 2-, and 3-year RFS was constructed using the package of rms in R version 3.5.2 (http://www.r-project.org/). To quantify the discrimination performance of the proposed nomogram, the Harrell’s C-index was evaluated. In brief, a C-index value greater than 0.750 is considered to represent relatively good discrimination of the model. Calibration was performed by comparing the RFS probability with the Kaplan–Meier estimator. In the validation cohort, according to the established nomogram, the C-index and calibration curve were derived based on regression analyses. The receiver operating characteristic (ROC) curve was used to compare the proposed prediction model with the 8th AJCC TNM classification [14] and the BCLC staging system [11].

Results

Clinicopathologic characteristics of training and validation cohorts

In total, 432 patients who underwent LH for primary HCC were included in this study, and 9 patients were excluded because of intraoperative laparoscopic conversion to open hepatectomy. The median follow-up time for the entire cohort was 14.38 (4.60–29.38) months. For the nomogram construction and validation, we assigned patients treated between September 2014 and January 2018 (n = 324) to the training cohort and patients treated between January 2013 and August 2014 (n = 108) to the validation cohort. The clinical demographics of the training and validation cohorts during the perioperative period are summarized in Table 1. As for the overall cohort, 87.73% of the patients were male, the mean age was 52.00 (43.00–60.00) years, 99.07% patients were classified as Child–Pugh class A (scores 5 and 6), and 85.42% patients were HBsAg-positive, 74.31% patients were diagnosed as having liver macronodular cirrhosis with “light” as the predominant cirrhosis level (n = 304, 70.37%). The mean AFP and CA19-9 levels were 91.45 (6.39–1705.00) ng/mL and 19.24 (10.44–33.99) ng/mL, respectively. The mean operation time was 157.32 ± 58.95 min, and 143 (33.10%) patients received hepatic portal vein embolization to reduce bleeding. There were no significant differences in clinicopathological characteristics between the training and validation cohorts (Table 1).
Table 1

Baseline clinicopathologic characteristics for the training and validation cohorts of patients who underwent laparoscopic hepatectomy for hepatocellular carcinoma

Variable

Overall cohort (n = 432)

Training cohort (n = 324)

Validation cohort (n = 108)

P value

Age [years; median (IQR)]

52.00 (43.00–60.00)

51.00 (43.00–60.00)

52.50 (44.00–60.25)

0.683

Sex [cases (%)]

0.933

 Male

379 (87.73)

285 (87.96)

94 (87.04)

 

 Female

53 (12.27)

39 (12.04)

14 (12.96)

 

Hepatitis B surface antigen [cases (%)]

0.937

 Present

369 (85.42)

276 (85.19)

93 (86.11)

 

 Absent

63 (14.58)

48 (14.81)

15 (13.89)

 

Child–Pugh score [cases (%)]

0.931

 5

359 (83.10)

268 (82.72)

91 (84.26)

 

 6

69 (15.97)

53 (16.36)

16 (14.81)

 

 7

4 (0.93)

3 (0.93)

1 (0.93)

 

AFP [ng/mL; median (IQR)]

91.45 (6.39–1705.00)

93.89 (6.79–1573.50)

78.82 (5.00–2231.75)

0.482

CA19-9 [ng/mL; median (IQR)]

19.24 (10.44–33.99)

20.18 (11.05–34.42)

15.96 (8.58–30.32)

0.692

AST (IU/L; mean ± SD)

41.50 ± 33.02

41.88 ± 35.45

40.36 ± 24.43

0.680

ALT (IU/L; mean ± SD)

41.93 ± 34.14

43.25 ± 37.22

38.00 ± 22.18

0.167

Hemoglobin (g/L; mean ± SD)

154.56 ± 193.60

157.11 ± 223.40

146.93 ± 13.97

0.636

Albumin (g/dL; mean ± SD)

43.28 ± 3.34

43.22 ± 3.41

43.47 ± 3.11

0.498

Bilirubin (mg/dL; mean ± SD)

13.32 ± 4.94

13.31 ± 4.95

13.36 ± 4.96

0.916

Platelet count (× 103/mm3; mean ± SD)

185.62 ± 72.00

184.02 ± 67.22

190.45 ± 84.93

0.422

Prothrombin time (INR; mean ± SD)

1.01 ± 0.08

1.02 ± 0.08

1.01 ± 0.08

0.264

HBV-DNA copy number (Log; mean ± SD)

2.46 ± 2.41

2.54 ± 2.42

2.22 ± 2.38

0.233

Bleeding [mL; median (IQR)]

200 (100–400)

200.00 (100–400)

275.00 (100–500)

0.834

Hospital stay (days; mean ± SD)

11.64 ± 3.87

11.67 ± 3.83

11.55 ± 3.98

0.774

Operation time (min; mean ± SD)

157.32 ± 58.95

155.02 ± 57.54

164.23 ± 62.77

0.160

Portal vein embolization [cases (%)]

1.000

 Yes

143 (33.10)

107 (33.02)

36 (33.33)

 

 No

289 (66.90)

217 (66.98)

72 (66.67)

 

Tumor size [cm; median (IQR)]

5.00 (3.00–8.00)

5.00 (2.95–8.00)

4.00 (3.00–8.00)

0.932

Tumor location [cases (%)]a

0.066

 Central

270 (62.5)

194 (59.9)

76 (70.4)

 

 Non-central

162 (37.5)

130 (40.1)

32 (29.6)

 

Tumor lesions [cases (%)]

0.375

 1

320 (74.07)

236 (72.84)

84 (77.78)

 

 2–3

112 (25.93)

88 (27.16)

24 (22.22)

 

Liver macronodular cirrhosis [cases (%)]

0.248

 None

111 (25.69)

87 (26.85)

24 (22.22)

 

 Light

304 (70.37)

226 (69.75)

78 (72.22)

 

 Medium

14 (3.24)

8 (2.47)

6 (5.56)

 

 Heavy

3 (0.69)

3 (0.93)

0 (0.00)

 

Cancer cell differentiation [cases (%)]

0.811

 Low

213 (49.31)

162 (50.00)

51 (47.22)

 

 Medium

205 (47.45)

151 (46.60)

54 (50.00)

 

 High

14 (3.24)

11 (3.40)

3 (2.78)

 

Tumor thrombus [cases (%)]

0.546

 Present

36 (8.33)

29 (8.95)

7 (6.48)

 

 Absent

396 (91.67)

295 (91.05)

101 (93.52)

 

MVI [cases (%)]

0.336

 Present

175 (40.51)

136 (41.98)

39 (36.11)

 

 Absent

257 (59.49)

188 (58.02)

69 (63.89)

 

Surgical procedure [cases (%)]

0.608

 Irregular

263 (60.88)

200 (61.73)

63 (58.33)

 

 Regular

169 (39.12)

124 (38.27)

45 (41.67)

 

Tumor encapsulation [cases (%)]

0.586

 No

153 (35.42)

119 (36.73)

34 (31.48)

 

 Incomplete

109 (25.23)

79 (24.38)

30 (27.78)

 

 Complete

170 (39.35)

126 (38.89)

44 (40.74)

 

Tumor boundary [cases (%)]

0.546

 Clear

396 (91.67)

295 (91.05)

101 (93.52)

 

 Unclear

36 (8.33)

29 (8.95)

7 (6.48)

 

8th AJCC T stage [cases (%)]

0.320

 T1a

66 (15.28)

44 (13.58)

22 (20.37)

 

 T1b

142 (32.87)

104 (32.10)

38 (35.19)

 

 T2

157 (36.34)

123 (37.96)

34 (31.48)

 

 T3

65 (15.05)

51 (15.74)

14 (12.96)

 

 T4

2 (0.46)

2 (0.62)

0 (0.00)

 

BCLC stage [cases (%)]

0.731

 0

31 (7.18)

23 (7.10)

8 (7.41)

 

 A1

289 (66.90)

213 (65.74)

76 (70.37)

 

 A2

18 (4.17)

15 (4.63)

3 (2.78)

 

 B

94 (21.76)

73 (22.53)

21 (19.44)

 

Follow-up time [months; median (IQR)]

14.38 (4.60–29.38)

13.58 (4.49–26.41)

23.10 (6.63–32.79)

0.012

SD standard deviation, IQR interquartile range, AFP alpha fetoprotein, CA19-9 carbohydrate antigen 19-9, AST aspartate transaminase, ALT aminotransferase, INR international normalized ratio, HBV hepatitis B virus, MVI microvascular tumor invasion, AJCC American Joint Committee on Cancer, BCLC Barcelona Clinic Liver Cancer

The difference between the training cohort and the validation cohort was compared using the Independent Samples t test or Mann–Whitney U test

aCentral = section I, IV, V, VIII; Non-central = section II, III, VI, VII

Development and validation of the nomogram model

Kaplan–Meier estimates of RFS in the training and validation cohorts are presented in Fig. 1. In the training cohort, 156 (48.15%) patients developed recurrence during a median follow-up of 13.58 months (interquartile range [IQR], 4.49–26.41 months), and the 1-, 2-, and 3-year RFS rates were 62.1%, 49.0%, and 42.5% with a median RFS after primary LH of 23.6 months (Fig. 1a).
Fig. 1

Kaplan–Meier estimates of recurrence-free survival in the training cohort (a) and the validation cohort (b) of patients who underwent laparoscopic hepatectomy for hepatocellular carcinoma

Univariate analyses (Table 2) revealed that positive HBsAg (P =0.008), presence of liver macronodular cirrhosis (P =0.006), elevated AFP (P <0.001), increased AST (P <0.001) and ALT (P =0.018), greater amount of bleeding (P =0.002), larger tumor size (P <0.001), 2–3 lesions (P <0.001), low cancer cell differentiation (P =0.001), presence of tumor thrombus (P <0.001), MVI (P <0.001), absence of tumor encapsulation (P =0.010), and unclear tumor boundary (P <0.001) were identified as significant prognostic factors for RFS. In multivariate analysis, HBsAg (hazard ratio [HR], 1.838; 95% confidence interval [CI] 1.016–3.327; P =0.044), tumor number (HR, 1.774; 95% CI 1.223–2.573; P =0.003), tumor thrombus (HR, 2.356; 95% CI 1.344–4.130; P =0.003), cancer cell differentiation (HR, 0.745; 95% CI 0.535–1.036; P =0.080), and the presence of MVI (HR, 1.673; 95% CI 1.150–2.433; P =0.007) were identified as independent predictors for RFS (Table 2).
Table 2

Cox proportional hazards regression analyses of recurrence in the training cohort

Variable

Total (cases)

Events (cases)

Univariate analysis

Multivariate analysis

HR (95% CI)

P value

Estimated coefficient

HR (95% CI)

P value

Age (years)

  

0.738 (0.471–1.156)

0.233

   

 < 65

281

139

     

 ≥ 65

43

17

     

Sex

  

0.966 (0.580–1.609)

0.895

   

 Male

285

140

     

 Female

39

16

     

Hepatitis B surface antigen

  

2.070 (1.363–3.146)

0.008

0.609

1.838 (1.016–3.327)

0.044

 Yes

276

142

     

 No

48

14

     

Liver macronodular cirrhosis

  

1.741 (1.235–2.455)

0.006

0.125

1.133 (0.814–1.575)

0.460

 None

87

33

     

 Light

226

121

     

 Medium

8

1

     

 Heavy

3

1

     

AFP (ng/mL)

  

1.712 (1.242–2.361)

< 0.001

0.128

1.137 (0.793–1.629)

0.485

 < 200

182

74

     

 ≥ 200

142

82

     

CA19-9 (ng/mL)

  

1.359 (0.920–2.009)

0.092

   

 < 35

248

116

     

 ≥ 35

76

40

     

AST (IU/L)

  

1.930 (1.226–3.039)

< 0.001

0.122

1.129 (0.673–1.895)

0.646

 < 50

266

119

     

 ≥ 50

58

37

     

ALT (IU/L)

  

1.516 (1.039–2.213)

0.018

0.285

1.329 (0.866–2.041)

0.193

 < 50

246

109

     

 ≥ 50

78

47

     

Hemoglobin (g/L)

  

0.733 (0.472–1.140)

0.124

   

 < 130

55

30

     

 ≥ 130

269

126

     

Albumin (g/dL)

  

0.819 (0.176–3.812)

0.778

   

 < 35

5

2

     

 ≥ 35

319

154

     

Bilirubin (mg/dL)

  

1.157 (0.770–1.737)

0.461

   

 < 17.1

260

124

     

 ≥ 17.1

64

32

     

Platelet count (*103/mm3)

  

0.610 (0.250–1.490)

0.168

   

 < 80

14

8

     

 ≥ 80

310

148

     

Prothrombin time (INR)

  

0.070

   

 ≤ 0.85

3

0

     

 0.85–1.2

313

151

     

 > 1.2

8

5

     

HBV-DNA copy number (Log)

  

1.183 (0.863–1.621)

0.301

   

 < 2

142

64

     

 ≥ 2

182

92

     

Bleeding (mL)

  

2.139 (1.090–4.197)

0.002

0.550

1.734 (0.872–3.446)

0.117

 < 800

299

138

     

 ≥ 800

25

18

     

Operation time (min)

  

1.194 (0.769–1.855)

0.400

   

 < 200

273

129

     

 ≥ 200

51

27

     

Portal vein embolization

  

1.407 (1.000–1.980)

0.400

   

 Yes

107

59

     

 No

217

97

     

Tumor size (cm)

  

2.175 (1.570–3.012)

< 0.001

0.208

1.231 (0.809–1.872)

0.332

 < 5

184

69

     

 ≥ 5

140

87

     

Tumor number

  

2.324 (1.592–3.392)

< 0.001

0.573

1.774 (1.223–2.573)

0.003

 Single

236

94

     

 2–3 lesions

88

62

     

Cancer cell differentiation

  

0.615 (0.446–0.849)

0.001

− 0.295

0.745 (0.535–1.036)

0.080

 Low

162

90

     

 Medium

151

64

     

 High

11

2

     

Tumor thrombus

  

4.126 (1.856–9.170)

< 0.001

0.857

2.356 (1.344–4.130)

0.003

 Yes

29

23

     

 No

295

133

     

MVI

  

2.291 (1.646–3.189)

< 0.001

0.514

1.673 (1.150–2.433)

0.007

 Yes

136

85

     

 No

188

71

     

Surgical procedure

  

1.108 (0.444–2.764)

0.718

   

 Irregular

200

97

     

 Regular

124

59

     

Tumor encapsulation

  

0.978 (0.639–1.498)

0.010

− 0.079

0.924 (0.753–1.135)

0.453

 Absent

119

63

     

 Incomplete

79

43

     

 Complete

126

50

     

Tumor boundary

  

2.343 (1.204–4.559)

< 0.001

− 0.016

0.985 (0.535–1.814)

0.960

 Clear

295

136

     

 Unclear

29

20

     

HR hazard ratio, CI confidence interval, AFP = alpha fetoprotein, CA19-9 carbohydrate antigen 19-9, AST aspartate transaminase, ALT aminotransferase, INR international normalized ratio, HBV hepatitis B virus, MVI microvascular tumor invasion

The prognostic nomogram that integrated all the independent prognostic factors for RFS derived from the training cohort is shown in Fig. 2. The C-index for RFS prediction in the training and validation cohorts were 0.703 (95% CI 0.747–0.659) and 0.789 (95% CI 0.858–0.719), respectively. The calibration plot for the RFS probability in the training and validation cohorts at 3 years after LH showed acceptable consistency between the prediction by nomogram and actual observation (Fig. 3).
Fig. 2

Nomogram depicting 1-, 2- and 3-year recurrence-free survival probability. By drawing a line between each variable and the uppermost component points, the appropriate points can be assigned to five variables. The sum of these five points can be expressed on the total point line. The 1-, 2-, and 3-year recurrence-free survival probability can be calculated by connecting each point to the survival line. The exact values of individual factors are tumor thrombus (100, 0 points), cancer cell differentiation (92, 63, 0 points), HBsAg (68, 0 points), MVI (65, 0 points), and tumor number (63, 0 points). HBsAg hepatitis B surface antigen, MVI microvascular tumor invasion, RFS recurrence-free survival

Fig. 3

Calibration plots of recurrence-free survival in the training and validation cohorts. The calibration curves derived from the training (a) and validation (b) cohorts are almost a diagonal line that would represent perfectly reliable prediction

ROC analysis among nomogram, 8th AJCC TNM, and BCLC

We compared the accuracy and probability of our nomograms with the clinically used prognostic models, namely the 8th AJCC TNM classification and the BCLC staging system. ROC curves for the 3-year RFS were plotted for the 108 patients in the validation cohort. The discriminatory ability of the present nomogram model, which had a C-index corresponding to the area under the ROC curve of 0.786 (95% CI 0.698–0.875), was superior to that of the 8th AJCC TNM classification and the BCLC staging system with C-indexes of 0.698 (95% CI 0.596–0.799) and 0.632 (95% CI 0.542–0.722), respectively (Fig. 4). According to the ROC analysis, we observed an improved predictive benefit in RFS and higher threshold probability when using our proposed nomogram as compared to the other predictive systems.
Fig. 4

ROC analysis of recurrence-free survival at 3 years in the validation cohort using the proposed nomogram, the 8th AJCC TNM classification, and the BCLC staging system. The C-index value of the proposed nomogram was superior to the C-index values of the other two systems

Discussion

In the present study, we developed and validated a practical nomogram model, based on clinicopathological characteristics of HCC patients who underwent LH, to predict the 1-, 2-, and 3-year RFS. It demonstrated superior prognostication performance compared with the 8th AJCC TNM classification and the BCLC staging system (C-index, 0.786 vs. 0.698 vs. 0.632, respectively).

It is widely believed that poor liver function and heavy tumor burden are significant prognostic factors that are associated with tumor recurrence after hepatectomy in HCC patients [15, 16, 17]. Compared to non-resection treatment, such as radiofrequency ablation, interventional therapy, and radiotherapy, establishing staging systems that are based on postoperative pathology combined with clinical factors seems more reliable for the prediction of recurrence, as it possesses more accurate and reliable information on tumor profiles as to that provided solely by postoperative pathology. However, almost all previously established staging systems are based on conventional hepatectomy which is much invasive to patients, and predictive model based per-patient is limited [18, 19]. Traditional opinions suggest that the evaluation of conventional hepatectomy is more depended on liver profiles that contribute more for long-term survival [18], whereas laparoscopic hepatectomy has comparable clinical outcomes to conventional hepatectomy and is less invasive, thereby reducing the injury to liver function for patients who are subjected to hepatectomy [20]. Simultaneously, this change of surgical selection increases the role of tumor burden in the prediction of recurrence for patients who are treated with LH. As a result, LH has different intrinsic properties from that of open procedure, and their long-term outcomes need to be separately mapped.

Several studies have emphasized the critical roles of tumor burden, gender, liver function, and performance status in the prognosis of HCC, but few have actually shown the role of detailed information of pathology in prognosis prediction [12, 21]. It is commonly supposed that the 8th AJCC TNM classification is one of the most prevalent staging systems of HCC, which is composed of TNM stage. However, this only classifies tumor burden and is limited in the power of prediction for HCC patients who are subjected to LH [22]. HCC patients who undergo surgical resection rarely suffer from lymph node metastases or distant metastases, and this classification thereby influences the accurate evaluation of RFS. The BCLC staging system takes both the liver function and tumor characteristics into account, including tumor extension, reserved liver function, physical status, and cancer-related symptoms [23]. The notable feature that distinguishes the BCLC staging system from other systems is the treatment recommendations for each stage based on the best treatment options currently available [24]. However, the BCLC class B (intermediate stage) covers a considerable heterogeneous population of HCC patients with varying degree of tumor extension, reserved liver function, and disease etiology, thus resulting in prognostic heterogeneity and preventing the decision of optimal treatment regimen selection. Meanwhile, the guidelines mentioned above are mainly based on preoperative clinical data or pathological information. Moreover, no guideline tailors for resectable HCC patients who were subjected to LH. Therefore, it is urgent to introduce a reliable, practicable, and individualized predictive model for patients who are candidates for surgical hepatectomy, especially LH.

The present nomogram integrates five independent risk factors for RFS, including HBsAg, tumor thrombus, tumor number, cancer cell differentiation, and MVI. Many studies have indicated that HBV infection, tumor thrombus, and MVI were significant risk factors for recurrence in patients with HCC [25, 26, 27]. The underlying hepatitis background was significantly associated with late recurrence and multicentric carcinogenesis. Tang et al. [27] reported that HBV infection might accelerate hepatocarcinogenesis via the integration of HBV DNA into the host genome, and continuous expression of viral proteins such as HBx might be involved in hepatocarcinogenesis. However, the major source of early recurrence is generally thought as metastasis, which is mainly derived from vascular invasion. Hirokawa et al. [25] indicated that circulating tumor cells were closely related to epithelial–mesenchymal transition and mesenchymal–epithelial transition which are the significant property of cancer stem cells. Given the early diagnosis of HCC, tumor thrombus is rare. Alternately, MVI is another potent parameter indicating vascular invasion for the prediction of recurrence [28]. Additionally, the present study demonstrated that tumor number predicted HCC recurrence, which was consistent with the results of other studies [29, 30]. However, tumor size could not be included in the nomogram proposed for HCC recurrence prediction in the present study, although other studies have indicated its insightful role in prognostic prediction [31, 32]. The possible explanation is that vascular invasion plays a more critical role in recurrence than tumor size, especially for patients with tumor size > 2 cm, according to 8th AJCC TNM classification [14, 33]. Besides, surgical margin was also not included in the nomogram, and it could be explained in the way that the patients included in the present study all had a minimal surgical margin of 1 cm, which indicated better RFS [13]. Interestingly, cancer cell differentiation was found to be a significant prognostic factor, and this was rarely mentioned in other studies. Low cancer cell differentiation has been reported to be the property of cancer progenitor or cancer stem cells which has high malignant biological behavior [34].

As the clinical and pathological factors mentioned in the present study have been validated separately in previous conventional hepatectomy studies, and the present study is the first to combine them together to assess patients who are subjected to laparoscopic hepatectomy. Hence, the proposed nomogram can be used to better guide routine follow-up for patients who have undergone LH as initial therapy. Patients characterized with a high recurrence score on our nomogram could be counseled to receive more high-end imaging examinations and close follow-up. In addition, more aggressive adjuvant therapy might be proposed, even if the results of the latest postoperative examinations indicated no evidence of recurrence. Conversely, the follow-up period for low-risk patients should refer to the clinical guideline [3].

Although our nomogram demonstrated satisfactory performance compared with existing systems used clinically, its related limitations need to be described. First, the nomogram was derived from data collected at a single institution, and the follow-up duration was relatively short for prognosticating long-term survival outcomes. Second, as this is a retrospective study for predicting the anticipated result, our nomogram needs to be confirmed in a prospective cohort. Third, our nomogram is mainly based on pathological outcomes, therefore, it is inapplicable to evaluate non-surgical patients.

Conclusions

We proposed a nomogram for predicting the postoperative RFS for HCC patients who underwent LH, based on easy-to-obtain clinical factors, comprising of HBsAg, tumor thrombus, tumor number, cancer cell differentiation, and MVI. The nomogram demonstrated relatively higher prediction than conventional 8th AJCC TNM and BCLC staging systems, and these findings, after proper validation, can be used as a model to better to guide individualized post-LH surveillance protocols of such patients.

Notes

Acknowledgements

The authors acknowledge and express their deepest gratitude to the participants of this study. We acknowledged the contribution of those who helped in improving the English language writing of this manuscript.

Authors’ contributions

YXP, JCC, and APF designed experiments and drafted the manuscript. XHW, JBC, WH, and JCW were responsible for data collection and statistical analysis. QJL, ZGZ, YZF, LX, MSC, and YJZ revised the manuscript and all authors agreed to be responsible for all aspects of the study. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81602143), National 135 Major Project of China (2018ZX10723204; 2018ZX10302205) and Sun Yat-sen University Cancer Center physician scientist funding (No. 16zxqk04).

Ethics approval and consent to participate

The study protocol was approved by the Clinical Research Ethics Committee of SYSUCC, and all patients provided written informed consent. (B2019-129-01).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer MedicineSun Yat-sen University Cancer CenterGuangzhouP. R. China
  2. 2.Department of Liver SurgerySun Yat-sen University Cancer CenterGuangzhouP. R. China
  3. 3.Department of Public HealthSun Yat-sen UniversityGuangzhouP. R. China
  4. 4.Department of Molecular Medicine California CampusThe Scripps Research InstituteLa JollaUSA

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