
==== Front
World J Surg Oncol
World J Surg Oncol
World Journal of Surgical Oncology
1477-7819
BioMed Central London

3523
10.1186/s12957-024-03523-1
Research
Early identification of hepatocellular carcinoma patients at high-risk of recurrence using the ADV score: a multicenter retrospective study
Cao Shuya 1
Zhou Zheyu 2
Chen Chaobo 3
Li Wenwen 4
Liu Jinsong 5
Xu Jiawei 6
Zhao Chunlong 3
Yuan Yihang 7
Xu Zhenggang 1
Wu Huaiyu 1
Ji Guwei drjgw@njmu.edu.cn

1
Xu Xiaoliang xuxiaoliang1990@yeah.net

8
Wang Ke lancetwk@163.com

1
1 grid.89957.3a 0000 0000 9255 8984 Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, 210029 China
2 grid.428392.6 0000 0004 1800 1685 Department of General Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Graduate School of Peking Union Medical College, Nanjing, 210008 China
3 https://ror.org/05tv5ra11 grid.459918.8 Department of General Surgery, Xishan People’s Hospital of Wuxi City, Wuxi, 214105 China
4 https://ror.org/04523zj19 grid.410745.3 0000 0004 1765 1045 Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing, 210023 China
5 grid.16821.3c 0000 0004 0368 8293 Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092 China
6 grid.428392.6 0000 0004 1800 1685 Department of Hepatobiliary and Transplantation Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008 China
7 grid.428392.6 0000 0004 1800 1685 Department of General Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008 China
8 https://ror.org/03t1yn780 grid.412679.f 0000 0004 1771 3402 Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China
7 9 2024
7 9 2024
2024
22 24022 5 2024
1 9 2024
© The Author(s) 2024
2024
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Background

Postoperative recurrence is a vital reason for poor 5-year overall survival in hepatocellular carcinoma (HCC) patients. The ADV score is considered a parameter that can quantify HCC aggressiveness. This study aimed to identify HCC patients at high-risk of recurrence early using the ADV score.

Methods

The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU) and Nanjing Drum Tower Hospital (NJDTH) were retrospectively reviewed. Based on the status of microvascular invasion and the Edmondson-Steiner grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). In the training cohort (TFAHNJMU), the R package nnet was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups. The Welch’s T-test was used to compare differences in clinical variables in three predicted risk groups. NJDTH served as an external validation center. At last, the confusion matrix was developed using the R package caret to evaluate the diagnostic performance of the model.

Results

350 and 405 patients from TFAHNJMU and NJDTH were included. HCC patients in different risk groups had significantly different liver function and inflammation levels. Density maps demonstrated that the ADV score could best differentiate between the three risk groups. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model, and the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log < medium-risk ≤ 5.7 log, and high-risk > 5.7 log. The sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively.

Conclusion

The ADV score might become a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-024-03523-1.

Keywords

Hepatocellular carcinoma
ADV score
Microvascular invasion
Edmondson-steiner grade
Recurrence
Prediction model
http://dx.doi.org/10.13039/501100016308 Wuxi Health and Family Planning Commission HB2023116 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82102150 82103135 http://dx.doi.org/10.13039/501100004608 Natural Science Foundation of Jiangsu Province BK20210968 http://dx.doi.org/10.13039/501100002949 Government of Jiangsu Province BE2020708 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Hepatocellular carcinoma (HCC) is the fifth most common malignant tumor in China, with the second highest mortality rate of all malignant tumors after lung cancer [1]. Globally, although the incidence and mortality of most cancers are decreasing, those of HCC is still increasing, making it the fourth most common cause of cancer-related death worldwide [2]. Surgical resection is the curative treatment of choice for early-stage HCC patients with good liver reserve function. However, the overall 5-year postoperative recurrence rate is as high as 70% [3]. Therefore, for HCC patients scheduled to undergo hepatectomy, early identification of those at high-risk for recurrence and appropriate clinical decision-making are crucial to improving prognosis and 5-year survival. Efficient biomarkers are needed in this process to guide precision therapy.

Alpha-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) are now commonly used tumor biomarkers for HCC [4]. Serum levels of both are strongly associated with the tumor biology of HCC. Previous studies showed that AFP-positive (≥ 20 ng/mL) patients had a more advanced tumor stage and a suppressive immune microenvironment (including accumulation of tumor-associated macrophages and depletion of different T-cell subsets) compared to AFP-negative patients [5]. Meanwhile, the overproduction of DCP was associated with proliferation, vascular invasion, and intrahepatic metastasis in HCC [6, 7]. This suggested that AFP and DCP are not only diagnostic biomarkers but also capable of responding to the inter-tumor heterogeneity of HCC. Moreover, long noncoding RNAs (lncRNAs) [8], many signaling pathways (e.g., Wnt-β-catenin, Hedgehog, and Hippo) [9], and toll-like receptor-4 (TLR4) single-nucleotide polymorphisms (SNPs) [10] have been proven vital in HCC occurrence and progression, which demonstrated that HCC is a liver mass with complex nature. Thus, the complex nature and heterogeneity of HCC are essential factors for the differences in treatment sensitivity and prognosis of different HCC patients [11].

High-risk recurrence characteristics of patients undergoing hepatectomy include poor tumor differentiation, micro- and macrovascular invasion, number of tumors greater than 3, and tumor size greater than 5 cm [12]. As gadoxetic acid-enhanced magnetic resonance imaging (MRI) improved the diagnostic efficacy of small HCC (< 1 cm) [13], combined with abdominal contrast-enhanced computed tomography (CT) allowed more accurate preoperative assessment of macrovascular invasion (tumor invasion of portal or hepatic vein branches), tumor number, and tumor size. Hence, another vital aspect of early identification of those at high-risk of recurrence may be the prediction of microvascular invasion (MVI) and differentiation status of HCC. The degree of differentiation of HCC is now commonly defined by the Edmondson-Steiner (E-S) grade [14], whereas MVI refers to the presence of HCC cells in the lumen of the endothelium-lined vessels found under the microscope [15]. Many studies have confirmed that E-S grade III-IV and MVI are independent risk factors for poorer disease-free survival (DFS) and overall survival (OS) in HCC [16, 17]. However, E-S grade and MVI are variables that need to be carefully evaluated by pathologists after hepatectomy. Previous studies have established many preoperative models for predicting MVI or E-S grade, including radiomics [17–19]. To the best of our knowledge, no study has yet developed a model to predict these two high-risk recurrence factors simultaneously.

ADV score is an integrated scoring system derived from AFP level, DCP level, and tumor volume (TV). A multicenter study from Korea evaluated the ability of the ADV score to predict prognosis in HCC patients undergoing liver transplantation (LT). The results demonstrated that the ADV-5 log had a comparable prognostic impact with the Milan criteria, and they were both independent risk factors for recurrence-free survival (RFS) and OS [20]. Besides, a study including 9,200 patients who underwent hepatectomy also reported that ADV-5 log significantly affected HCC recurrence and 3-year mortality [21]. Thus, the ADV score may be an effective biomarker reflecting HCC heterogeneity and stratifying prognosis. Since the TV in the ADV score considers both the size and number of HCC [22], its greatest advantage may be that one convenient metric includes four prognostically relevant variables.

As mentioned above, advances in imaging have made the diagnosis of HCC combined with macrovascular invasion more accurate. Hence, this study aimed to investigate whether the ADV score could predict both MVI and E-S grade III-IV to provide a new biomarker and theoretical basis for the early identification of HCC patients at high-risk of recurrence.

Patients and methods

Study design and patients

The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU, from January 2020 to August 2023) and Nanjing Drum Tower Hospital (NJDTH, from January 2020 to December 2023) were retrospectively reviewed. Because of the unidentifiable patient information and the nature of the retrospective study, the institutional review boards of TFAHNJMU and NJDTH waived the requirement for written informed consent. This study followed the 1964 Declaration of Helsinki and its later amendments. Patients were included in this study if they met the following criteria: non-recurrent HCC patients; no preoperative local or systemic therapy; complete clinicopathologic data; and no history of other malignancies.

Data collection and calculation formulas

All patients’ laboratory test data were obtained within one week before the hepatectomy, which included hepatitis markers, tumor markers, blood routine examination, liver function test, and coagulation test. In addition, we included six inflammatory markers and three serum liver fibrosis diagnostic models, and their calculation formulas were described in the previously published articles [23, 24]. The presence of liver cirrhosis was documented based on the most recent preoperative imaging reports. Table 1 presented all included variables.

Table 1 Comparison of clinicopathology characteristics among the training cohort

Variables	Risk Group 1
(N = 85)	Risk Group 2
(N = 124)	Risk Group 3
(N = 141)	P	
Age, years	60.4 ± 12.8	61.5 ± 10.1	58.8 ± 10.2	0.117	
Gender

Male, n (%)

Female, n (%)

	68 (80.0%)

17 (20.0%)

	98 (79.0%)

26 (21.0%)

	115 (81.6%)

26 (18.4%)

	0.873	
Liver cirrhosis

Absent, n (%)

Present, n (%)

	60 (70.6%)

25 (29.4%)

	79 (63.7%)

45 (36.3%)

	82 (58.2%)

59 (41.8%)

	0.170	
MVI

Absent, n (%)

Present, n (%)

	85 (100.0%)

0 (0.0%)

	89 (71.8%)

35 (28.2%)

	0 (0.0%)

141 (100.0%)

	< 0.001	
E-S Grade

I-II, n (%)

III-IV, n (%)

	85 (100.0%)

0 (0.0%)

	35 (28.2%)

89 (71.8%)

	0 (0.0%)

141 (100.0%)

	< 0.001	
Tumor number

Solitary, n (%)

Multiple, n (%)

	79 (92.9%)

6 (7.1%)

	116 (93.5%)

8 (6.5%)

	120 (85.1%)

21 (14.9%)

	0.043	
TV, mL	49.1 ± 110.0	63.9 ± 103.0	154.0 ± 239.0	< 0.001	
HbsAg

Negative, n (%)

Positive, n (%)

	29 (34.1%)

56 (65.9%)

	41 (33.1%)

83 (66.9%)

	36 (25.5%)

105 (74.5%)

	0.279	
HCVAb

Negative, n (%)

Positive, n (%)

	79 (92.9%)

6 (7.1%)

	117 (94.4%)

7 (5.6%)

	132 (93.6%)

9 (6.4%)

	0.916	
AFP, ng/mL	127.0 ± 308.0	348.0 ± 867.0	636.0 ± 1658.0	0.006	
DCP, mAU/mL	1463.0 ± 4372.0	1761.0 ± 7104.0	6359.0 ± 10642.0	< 0.001	
ADV score	4.1 ± 1.9	5.2 ± 1.9	6.8 ± 2.1	< 0.001	
RDW, %	13.1 ± 0.8	13.3 ± 1.3	13.2 ± 1.1	0.377	
NE, ×109/L	3.0 ± 1.4	3.0 ± 1.3	3.0 ± 1.5	0.981	
LYM, ×109/L	1.5 ± 0.5	1.7 ± 0.6	1.5 ± 0.7	0.025	
M, ×109/L	0.5 ± 0.9	0.5 ± 0.2	0.5 ± 0.2	0.627	
PLT, ×109/L	144.0 ± 63.5	158.0 ± 81.1	152.0 ± 63.3	0.390	
ALT, U/L	31.4 ± 21.5	40.2 ± 46.7	40.2 ± 28.3	0.130	
AST, U/L	32.7 ± 21.7	38.6 ± 36.2	45.2 ± 31.0	0.012	
GGT, U/L	54.7 ± 49.1	68.9 ± 73.5	94.5 ± 95.6	0.001	
TB, µmol/L	15.4 ± 7.9	14.6 ± 7.3	15.5 ± 6.8	0.572	
ALB, g/L	39.5 ± 3.9	38.8 ± 4.2	38.5 ± 4.2	0.177	
PT, seconds	12.6 ± 3.1	12.4 ± 1.0	12.5 ± 0.9	0.602	
INR	1.1 ± 0.1	1.1 ± 0.1	1.1 ± 0.1	0.126	
GLR†	41.2 ± 35.1	52.2 ± 79.8	78.7 ± 91.2	0.001	
ALRI†	26.9 ± 29.0	28.1 ± 26.5	39.4 ± 40.2	0.005	
ANRI†	14.8 ± 24.4	17.4 ± 23.5	18.2 ± 18.4	0.517	
NLR†	2.4 ± 1.7	2.0 ± 1.0	2.4 ± 1.8	0.073	
PLR†	109.0 ± 56.8	105.0 ± 61.5	118.0 ± 57.2	0.193	
MLR†	0.4 ± 0.3	0.3 ± 0.1	0.4 ± 0.2	0.045	
APRI#	0.8 ± 1.1	0.9 ± 1.1	0.9 ± 0.8	0.747	
FIB-4#	3.2 ± 2.8	3.3 ± 2.8	3.3 ± 2.1	0.947	
GPR#	0.5 ± 0.4	0.6 ± 1.1	0.9 ± 0.7	0.135	
Continuous variables are presented as mean ± standard deviation (SD). Categorical variables are presented as numbers of patients with percentages in parentheses. †Inflammatory markers. #Serum liver fibrosis diagnostic models

MVI, microvascular invasion; E-S, Edmondson-Steiner; TV, tumor volume; HBsAg, hepatitis B virus surface antigen; HCVAb, hepatitis C virus antibodies; AFP, alpha fetoprotein; DCP, des-γ-carboxy prothrombin; RDW, red blood cell distribution width; NE, neutrophil; LYM, lymphocyte; M, monocyte; PLT, platelet; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transferase; TB, total bilirubin; ALB, albumin; PT, prothrombin time; INR, international normalized ratio; GLR, γ-glutamyl transferase to lymphocyte ratio; ALRI, aspartate aminotransferase to lymphocyte ratio index; ANRI, aspartate aminotransferase to neutrophil ratio index; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; APRI, aspartate transaminase to platelet ratio index; FIB-4, fibrosis-4; GPR, γ-glutamyl transferase to platelet ratio

ADV score

The ADV score = log10[AFP (ng/mL) × DCP (mAU/mL) × TV (mL)]. Tumor sizes from pathology reports were collected and used to calculate the TV. TV = 4/3 × π × a × b × c (a, b, and c = length of the three meridians of HCC in the pathology report). If multiple tumors are present, the total TV is the TV of the largest tumor multiplied by the number of tumors [22].

Pathological examination

Two experienced pathologists analyzed all hepatectomy specimens independently, and any disagreements were resolved after discussion. The degree of differentiation of HCC was defined by E-S grade [14]. Typical pathological images of the E-S grade were shown in Fig. S1. MVI refers to the presence of HCC cells in the lumen of the endothelium-lined vessels found under the microscope [15]. Typical pathological images of M0, M1, and M2 were presented in Fig. S2. Moreover, the number of tumors was recorded as solitary or multiple.

Statistical analysis and model development

Based on the status of MVI and the E-S grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). The Kruskal-Wallis test and χ2 test were used to compare whether there were differences between the three groups for continuous and categorical variables. Subsequently, density maps were used to show the distribution situation of lgAFP, lgDCP, lgTV, and the ADV score in different risk groups [25].

In the training cohort (TFAHNJMU), the R package nnet (version 7.3–18) was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups [26]. Then, the Welch’s T-test was used to compare differences in clinical variables in three predicted risk groups. At last, the R package caret (version 6.0–94) was used to develop the confusion matrix, aiming to evaluate the diagnostic performance of the model in the training and external validation (NJDTH) cohort [27]. In the subgroup analyses, the paired T-test was used to compare whether the ADV score performed better in some populations (cirrhotic vs. non-cirrhotic patients and chronic hepatitis B [CHB] vs. chronic hepatitis C [CHC] patients). All statistical analyses were completed using the R software (version 4.2.2).

Results

Patients

350 and 405 patients from TFAHNJMU and NJDTH who met the criteria were included in this study. Fig. S3 presented the detailed flowchart for patient selection. According to the postoperative pathology report, in the training cohort, there were 85, 124, and 141 patients in the low-risk, medium-risk, and high-risk groups, respectively. At the same time, in the external validation cohort, there were 150, 175, and 80 patients in risk groups 1, 2, and 3, respectively. Furthermore, 298 (training: 129 of 350, 36.9%; external validation: 169 of 405, 41.7%; p = 0.197) of all included patients had liver cirrhosis.

Evaluation indicator screening

The comparison of clinicopathology characteristics between different risk groups in the training cohort was presented in Table 1. The results showed that demographic characteristics (age and sex) did not differ significantly between groups, while four HCC biology markers (AFP, DCP, TV, and the ADV score) were significantly different and progressively higher with increasing levels of risk. Besides, the degree of hepatic function impairment (AST and GGT) and the level of inflammation (ALRI, MLR, and GLR) were positively associated with risk groups. Finally, density maps demonstrated that the ADV score could best differentiate between the three risk groups Fig. 1.

Fig. 1 Density maps of lgAFP (A), lgDCP (B), lgTV (C), and the ADV score (D) in different risk groups. AFP, alpha-fetoprotein; DCP, des-γ-carboxy prothrombin; TV, tumor volume

Model development and evaluation

Therefore, we selected the ADV score to establish the model to predict risk groups of HCC patients. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model Fig. 2. Notably, the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log < medium-risk ≤ 5.7 log, and high-risk > 5.7 log (intersections of curves in Fig. 2).

Fig. 2 The probability curve based on the predicted results of the multi-categorical unordered logistic regression model

Subsequently, two cohorts of patients were categorized into three groups based on the optimal cut-off values (ADV group). Similar to actual risk groups, HCC patients in different ADV groups had significantly different liver function and inflammation levels. First, patients in ADV group 3 had lower levels of ALB and higher levels of AST and GGT Fig. 3. Second, patients in ADV group 1 had lower inflammation levels than patients in groups 2 and 3 Fig. 4. These results initially proved the reliability of the ADV score.

Eventually, the confusion matrix was used to evaluate the diagnostic performance of the established model in different risk groups (Table 2). Most importantly, the sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively. It was suggested that HCC patients with an ADV score > 5.7 log might be at the highest risk for recurrence. In the meantime, high specificities of diagnosing low-risk groups (88.7% and 86.3%) suggested that HCC patients with an ADV score > 3.4 log had a high probability of MVI or E-S grade III-IV. The subgroup analyses revealed that the ADV score had similar overall diagnostic performance among different populations (cirrhotic vs. non-cirrhotic patients and CHB vs. CHC patients; Table S1 and S2).

Fig. 3 Significant different liver function variables (ALB, AST, and GGT) in patients with different ADV groups in training (A-C) and external validation cohorts (D-F). ALB, albumin; AST, aspartate aminotransferase; GGT, γ-glutamyl transferase

Fig. 4 Significant different inflammation variables (ALRI, MLR, and GLR) in patients with different ADV groups in training (A-C) and external validation cohorts (D-F). ALRI, aspartate aminotransferase to lymphocyte ratio index; MLR, monocyte to lymphocyte ratio; GLR, γ-glutamyl transferase to lymphocyte ratio

Table 2 Diagnostic performance of the ADV score for predicting different risk groups

	Risk Group 1	Risk Group 2	Risk Group 3	
Training cohort	
Sensitivity (%)	44.7 (38/85)	46.8 (58/124)	70.2 (99/141)	
Specificity (%)	88.7 (235/265)	73.0 (165/226)	69.4 (145/209)	
Accuracy (%)	78.0 (273/350)	63.7 (223/350)	69.7 (244/350)	
External validation cohort	
Sensitivity (%)	36.0 (54/150)	34.9 (61/175)	78.8 (63/80)	
Specificity (%)	86.3 (220/255)	63.0 (145/230)	67.1 (218/325)	
Accuracy (%)	67.7 (273/405)	50.9 (223/405)	69.4 (281/405)	

Discussion

The ADV score is considered a parameter that can quantify HCC aggressiveness [28]. Table 3 provides an overview of published articles reporting the application of the ADV score in HCC [20–22, 28–36]. Results demonstrated that the ADV score could prognostically stratify HCC patients undergoing hepatectomy or LT. However, it is worth noting that almost all studies were from Korea (except for one study cooperating with Japan) [21]. This suggests that studies from different centers and regions are needed to validate the generalizability of the ADV score. This study categorized HCC patients into three groups based on two risk factors, with group 3 being the population to focus on in clinical practice. Early identification of the group 3 was the most essential objective of this study. Predicting MVI or E-S grade alone can be done using previously published models [17–19], as they were not distinguished in group 2.

Table 3 An overview of the application of the ADV score in HCC

Ref.	Procedure	Cases	Cut-off value	Predicted event	Efficiency	The ADV score of 1log interval†	
DFS	OS	
Hwang S et al., 2016 [29]	hepatectomy	1727	5log	recurrence

survival

	HR = 1.57, p < 0.001

HR = 2.17, p < 0.001

	< 0.001	/	
Hwang S et al., 2018 [30]	hepatectomy	526	7log	recurrence

survival

	HR = 1.29, p < 0.001

HR = 1.33, p = 0.001

	0.001	< 0.001	
Ha SM et al., 2018 [31]	hepatectomy	35	4log	recurrence

survival

	HR = 2.20, p = 0.095

HR = 2.13, p = 0.062

	/	/	
Jung DH et al., 2019 [28]	hepatectomy	1572	4log	recurrence	HR = 1.31, p = 0.004	0.117	0.106	
Park GC et al., 2020 [32]	hepatectomy	1390	10log	survival	HR = 5.00, p < 0.001	/	< 0.001	
Hwang S et al., 2021 [33]	hepatectomy	147	9log	recurrence

survival

	HR = 1.85, p = 0.002

HR = 2.13, p = 0.001

	< 0.001	0.042	
Hwang S et al., 2021 [34]	LDLT	625	4log

6log

	survival	c-index = 0.7

c-index = 0.66

	< 0.001	< 0.001	
Hwang S et al., 2021 [22]	LDLT	843	5log	recurrence

survival

	c-index = 0.63

c-index = 0.63

	< 0.001	< 0.001	
Hwang S et al., 2021 [35]	hepatectomy	100	8log	recurrence

survival

	HR = 1.40, p = 0.098

HR = 1.45, p = 0.120

	0.873	0.017	
Hwang S et al., 2022 [36]	LDLT	100	5.4log	recurrence

survival

	HR = 3.56, p < 0.001

HR = 5.58, p < 0.001

	/	/	
Kang WH et al., 2023 [21]	hepatectomy	9200	5log	recurrence	AUROC = 0.577	< 0.001	< 0.001	
Park GC et al., 2023 [20]	LT	1599	5log	recurrence

survival

	AUROC = 0.705

AUROC = 0.728

	0.021	< 0.001	
†HCC patients were stratified according to the interval of 1log ADV score, then DFS and OS were compared

HCC, hepatocellular carcinoma; DFS, disease-free survival; OS, overall survival; HR, hazard ratio; LDLT, living donor liver transplantation; c-index, index of concordance; AUROC, area under the receiver operating characteristic curve; LT, liver transplantation

AFP is a traditional biomarker for HCC, and its combination with ultrasound is still recommended as the primary strategy for screening HCC [12]. Nevertheless, due to the complex nature and inter-tumor heterogeneity of HCC, AFP alone is insufficient to diagnose it and predict its clinical course [37]. Recently, many studies have shown that DCP is vital in detecting HCC (including HCC ≤ 3 cm), monitoring treatment outcomes and recurrence, and assessing prognosis [38, 39]. High levels of AFP and DCP are poor prognostic indicators for HCC patients [4]. Although differences in race, cohort size, and other aspects of different research might contribute to the differences in their cut-off value. For instance, Zhang Y et al. [40] reported that preoperative AFP > 400 ng/mL increased the risk of HCC recurrence after hepatectomy by approximately 2-fold. Meanwhile, another retrospective study revealed that the hazard ratio (HR) for the effect of DCP > 40 mAu/mL on HCC recurrence was 1.479 based on the multivariate analysis [41]. Similar to previous studies, this study used the tumor size from the pathology report to calculate the TV. Since the pathology index can only be obtained postoperatively, this is one of the limitations of this study. Our intention was to preoperatively identify independent risk factors for recurrence early, helping surgeons decide on treatment and management strategies. However, with the development of imaging technology, it is now clinically possible to perform preoperative 3-dimension (3D) reconstruction of the liver based on contrast-enhanced CT and calculate the TV (the user interface of the software of our department is shown in Fig. S4). We believe a close relevance exists between CT-based TV and pathology-based TV [35]. In future prospective studies, the CT-based TV will be used to further verify the dependability of the ADV score.

There is a strong association between two high-risk factors for recurrence, MVI and HCC poor differentiation. Qu C et al. [42] certified that the risk of MVI in patients with E-S grade III-IV was approximately 2.97 times higher than that of patients with good differentiation (p < 0.001). Another retrospective study found the presence of MVI in 35% of patients with poor differentiation, compared with only 14.6% of patients with E-S grade I-II (p < 0.001) [43]. Preoperative prediction of MVI risk can help guide therapeutic decisions in HCC patients with good hepatic reserve function who are scheduled to undergo surgery. First, for patients with HCC ≤ 3 cm or within the Milan criteria at high-risk of MVI, hepatectomy provided better 5-year recurrence and OS rates compared with radiofrequency ablation [44]. Second, for HCC patients within the Milan or up-to-7 criteria, predicted high-risk patients undergoing LT had a better long-term prognosis than those undergoing hepatectomy [45, 46]. Third, wide margins (margin distance ≥ 1 cm) and anatomic hepatectomy significantly prolonged DFS and OS in predicted MVI-positive HCC patients [47]. Fourth, intraoperative radiotherapy can be performed where available [48].

Our results demonstrated significant differences in hepatic function and inflammation levels among the three groups of patients differentiated by the ADV score (Figs. 3 and 4). Notably, previous studies indicated high preoperative levels of AST (> 40 U/L) and GGT (≥ 60 U/L) as independent risk factors for the presence of MVI [49, 50]. At the same time, the decision tree and permutation test screened AST from numerous clinical parameters, which was significantly associated with E-S grade III-IV [19]. ALB was a relatively important variable influencing HCC recurrence and overall mortality [51]. The pro-inflammatory microenvironment of the cirrhotic liver is important in the development of most HCC [52]. ALRI, MLR, and GLR are prognostic indicators of inflammation capable of reflecting systemic inflammatory status, and they are significantly associated with early recurrence and long-term survival of HCC after hepatectomy [23, 53, 54]. In addition, Zhang H et al. [55] certified that GLR > 56 could be used for risk prediction of MVI. Meanwhile, HCC patients with E-S grade III-IV were reported to have higher ALRI levels than patients with E-S grade I-II (p = 0.029) [43]. The above results supported the reliability of the ADV score in predicting the risk of MVI and E-S grade.

Recent evidence suggested a benefit of adjuvant therapy in patients at high-risk of recurrence [12]. A phase III randomized study from China confirmed that postoperative adjuvant hepatic arterial infusion chemotherapy with 5-fluorouracil and oxaliplatin (HAIC-FOLFOX) significantly improved DFS in HCC patients with MVI [56]. Although the American Association for the Study of Liver Diseases (AASLD) still does not recommend neoadjuvant therapy for HCC patients undergoing hepatectomy outside of a clinical trial setting [12], the research teams of Ho WJ et al. [57] and Kaseb AO et al. [58] reported that preoperative neoadjuvant systemic therapy was feasible. Exploring effective neoadjuvant therapeutic options is an important future research direction to improve the prognosis of HCC patients after surgery. In a clinical trial setting, we considered predicted high-risk patients (presence of MVI & E-S grade III-IV) to be the candidates of the intention-to-treat (ITT) population. A randomized, placebo-controlled study published in 2015 showed that adjuvant therapy with sorafenib after hepatectomy in HCC patients did not improve RFS [59]. Notably, MVI was present in only 32% and 33% of patients in sorafenib and placebo groups, respectively. Is the appropriate ITT population the key to successful clinical trials in HCC? If the ITT population of the STORM trial was all patients with MVI or E-S grade III-IV, nobody can guarantee that sorafenib will not improve the prognosis of these patients. Since high-grade evidence needs to be obtained through multicenter randomized controlled trials (RCTs) with long-term follow-up, this requires significant human and financial resources. Thus, selecting the appropriate ITT population is crucial to enhance the RCTs’ success rate. Patients with a high ADV score (> 5.7 log) may be the ideal ITT population for neoadjuvant therapy.

This study used the ADV score to predict MVI and E-S grade status, which differs from previous articles focusing on HCC recurrence or overall survival (Table 3). Combining what is discussed above, the ADV score may be helpful in clinical decision-making in the following aspects, which were not proposed in previous studies. First, surgeons should choose a better surgical strategy for eradicating HCC lesions and MVI. Selecting predicted high-risk patients for neoadjuvant therapy in a clinical trial setting can be considered. Second, careful examination of pathology specimens by experienced pathologists is required to determine the accurate E-S grade because the E-S grades are categorized according to the size and morphology of HCC cells. Meanwhile, the “7-point method” is recommended for diagnosing MVI when sampling HCC specimens [60]. Third, for hepatitis B virus (HBV)-related HCC patients, standardized antiviral therapy should be administered preoperatively and postoperatively to reduce the incidence of MVI and improve prognosis [61, 62]. Finally, all predicted high-risk patients should cooperate with the doctor’s treatment and receive more frequent follow-up visits.

Although an independent external validation cohort verified the diagnostic efficacy of the ADV score, this study still has several limitations. As mentioned above, pathology-based TV was used to calculate the ADV score. Since this is a retrospective cross-sectional study, selection bias is unavoidable, and follow-up data are lacking to investigate the impact of the ADV score on the long-term survival of HCC patients. Few non-HBV-related HCC patients were included due to the realities in China. Lastly, prospective studies are lacking in validating the ADV score’s performance.

Conclusion

In conclusion, high-risk groups had more severe hepatic function impairment and higher levels of inflammation. The ADV score could best differentiate between risk groups, a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

Not applicable.

Author contributions

Study concept and design: KW, XLX, and GWJ. Analysis and interpretation of data: SYC, ZYZ, and CBC. Generation of tables and figures: WWL, JSL, JWX, CLZ, YHY, ZGX, and HYW. Drafting of the manuscript: ZYZ, SYC, and CBC. Critical revision of the manuscript: KW, XLX, and GWJ. All authors read and approved the final version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (82102150 and 82103135), the Natural Science Foundation of Jiangsu Province (BK20210968), Jiangsu Provincial Key Research and Development Program (Social Development) (BE2020708), and Top Talent Support Program for Young and Middle-Aged People of Wuxi Health Committee (HB2023116).

Data availability

The datasets used in this study are available from corresponding authors upon reasonable request.

Declarations

Ethics approval and consent to participate

The institutional review boards of The First Affiliated Hospital of Nanjing Medical University and The Affiliated Drum Tower Hospital of Nanjing University Medical School approved this retrospective study and waived the requirement for written informed consent. All included patients’ personal information is strictly confidential. This study followed the 1964 Declaration of Helsinki and its later amendments.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shuya Cao, Zheyu Zhou and Chaobo Chen contributed equally to this work.
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