Clinical and CT imaging characteristics of early recurrence in patients with hepatocellular carcinoma after curative resection

doi:10.21203/rs.3.rs-4862882/v1

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Clinical and CT imaging characteristics of early recurrence in patients with hepatocellular carcinoma after curative resection

https://doi.org/10.21203/rs.3.rs-4862882/v1

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Purpose

To investigate the clinical and CT imaging factors of early recurrence in patients with hepatocellular carcinoma (HCC) after surgical resection and their predictive value.

Methods

We retrospectively analyzed 87 HCC patients who underwent curative resection. The associations of clinical and CT imaging factors with early recurrence were investigated by univariate and multivariate analysis. A binary logistic regression model was constructed based on the results of a multivariate regression analysis. The area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy rate, together with their 95% confidence interval (CI), were calculated.

Results

Postoperative early HCC recurrence was associated with alpha-fetoprotein, the platelet-to-lymphocyte ratio (PLR), aspartate aminotransferase, alanine aminotransferase, tumor diameter, unsmooth margins and satellite nodules (P < 0.05). The binary logistic regression model included 3 predictors from the regression analysis: PLR, tumor margin, and satellite nodule. The AUC, sensitivity, specificity, PPV, NPV, and accuracy rate were 0.854 (95% CI 0.776 ~ 0.933), 85.29%, 92.11%, 78.6%, 80% and 81.8%, respectively.

Conclusion

PLR, tumor margins, and satellite nodules were independent risk factors for postoperative HCC early recurrence. The binary logistic regression model based on these indicators showed good performance in predicting postoperative HCC early recurrence.

Biological sciences/Cancer

Health sciences/Oncology

Health sciences/Risk factors

Hepatocellular Carcinoma

Liver resection

Early recurrence

Computed tomography

CK19

PLR

Hepatocellular carcinoma (HCC) is currently the most common type, accounting for 75–90% of all primary liver cancers. Moreover, HCC is the sixth most common cancer and the third leading cause of cancer-related death worldwide.^1,2 Currently, hepatic resection is the primary treatment option for the early treatment of HCC.³ The 5-year overall survival rate after surgical resection is approximately 70%. However, a major disadvantage of resection is the high incidence of postoperative recurrence, which occurs in up to 80% of patients.⁴

Tumor recurrence is generally classified into early or late recurrence by using 2 years as the cutoff.^5,6 Early recurrence originates from intrahepatic metastasis of primary cancer or residual microscopic tumor lesions after resection, whereas late recurrence is de novo HCC developing in the background of persistent liver diseases and hepatitis-related cirrhosis of the remnant liver.^7,8 Previous studies have shown that the survival rate of patients with early recurrence is significantly lower than that of patients with late recurrence.^9,10 Postoperative recurrence significantly reduces the long-term survival rate of HCC patients and is one of the main causes of death in HCC patients.¹¹ Therefore, it is very important for HCC patients to experience early recurrence after resection.

The purpose of this study was to investigate the clinical and CT imaging factors associated with early recurrence in HCC patients after surgical resection.

This retrospective study was approved by the Ethics Committee of the Second Xiangya Hospital Central South University, Changsha, China. All the subjects provided written informed consent. All methods were performed following the relevant guidelines and regulations.

From January 2019 to October 2021, 1160 patients diagnosed with “malignant tumors/space-occupying lesions of the liver” were included in the study. According to the inclusion and exclusion criteria, 1073 patients were excluded, including 627 patients with nonsurgical treatment/surgical treatment combined with other antitumor therapy, 185 patients with “nonhepatocellular carcinoma/a history of other tumors”, 189 patients with “portal vein tumor thrombus/metastasis”, 22 patients with incomplete imaging data, and 50 patients who were lost to follow-up. Finally, we selected 87 HCC patients for our study. Among them, 42 HCC patients experienced early postoperative recurrence, and 45 HCC patients experienced no early postoperative recurrence. Tumor recurrence within 2 years was classified as early recurrence. Detailed information is shown in Fig. 1.

Inclusion criteria and exclusion criteria

The inclusion criteria were as follows: (1) patients were older than 18 years of age; (2) patients were initially diagnosed with malignant tumors of the liver, hepatectomy was performed, and the focus of the tumor was removed completely; (3) the diagnosis of HCC was confirmed by postoperative histopathological examination; and (4) patients had Child‒Pugh grades A–B without extrahepatic metastasis or portal vein tumor thrombus. The exclusion criteria were as follows: (1) patients who received nonsurgical treatment/preoperative antitumor treatments; (2) patients with portal vein tumor thrombus/metastasis; (3) patients with a history of other tumors; (4) patients lost to follow-up; and (5) patients with incomplete imaging data or unclear images.

Data collection

Thirty indices were collected in this study, including demographic and clinical features, laboratory features, and computed tomography imaging features. All the continuous variables, such as the serum ALB concentration and platelet count, were changed into categorical variables. The detailed information is shown in the supporting information (Table S1).

Diagnostic criteria and follow-up

The CT images were independently reviewed by two board-certified radiologists who were aware of the HCC diagnosis but were blinded to the clinical, laboratory, and histologic data. The CT imaging features of the largest tumor were recorded. In cases of disagreement regarding the review of imaging features between the two radiologists, a final decision was reached through consensus. The typical imaging features are shown in supporting information (Figure S1-3).

After surgery, the patients were monitered by outpatient reexamination. The reexaminations comprised alpha-fetoprotein (AFP) tests, upper abdominal CT or MRI scans, and chest CT scans. The initial follow-up occurred in the first month post-surgery, followed by quarterly reexaminations during the first year. After one year, the follow-up interval was extended to every six months. The images were interpreted and analyzed for lesion characteristics by two experienced radiologists using a double-blind reading method. Recurrence was diagnosed on the basis of the combined findings of these clinical examinations. The endpoint of this study was 31 October 2023 or time-to-recurrence, which was defined as the period between surgery and the diagnosis of recurrence. Patients who died for reasons not related to HCC were censored at the time of death. For patients who did not undergo follow-up at our hospital, telephone follow-ups were conducted.

Statistical analysis

SPSS 23.0 was used for the statistical analyses. The prediction model was developed via a 2-step approach. First, the Pearson chi-square test or Fisher exact test was used to compare enumeration data. Statistical significance was determined as a P value of less than 0.05. All variables with statistical significance (P < 0.05) according to the Pearson chi-square test or Fisher exact test were considered candidates for further binary logistic regression analyses. The feature selection step was performed on complete data. Second, a binary logistic regression model was built based on the variables with statistical significance. The model’s ability and the optimal cutoff value for diagnnosis were evaluated and determined by receiver operating characteristic (ROC) curve analysis. Model-related ability parameters were assessed,including the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy rate, together with their 95% confidence intervals (CIs).

Baseline characteristics

In this analysis, 87 participants were screened. The median age of the participants was 55 years; 92% were male. Early tumor recurrence was observed in 42 (48.3%) patients. The typical recurrence images are shown in Figs. 2–3. The images of patients without early recurrence are shown in Figure S4.

Univariate and multivariate analysis

We performed a univariate analysis of 29 indicators, including demographic characteristics, clinical characteristics, laboratory findings, imaging characteristics, and endoscopic characteristics, between the early recurrence group and the nonearly recurrence group, which was evaluated by Pearson chi-square or Fisher exact analysis on 87 HCC patients. Seven indicators were significantly different (P < 0.05, Table 1). The abovementioned 7 factors were included in the multivariate analysis. On the basis of multivariable logistic regression, a PLR > 77 (OR 14.658, 95% CI 2.366–91.992, P = 0.004), an unsmooth margin (OR 7.134, 95% CI 1.385–36.755, P = 0.019), and satellite nodules (OR 48.501, 95% CI 3.148–747.289, P = 0.005) remained independent factors for early tumor recurrence. The results are shown in Fig. 4.

Table 1

Univariate analysis of the clinical and imaging characteristics of HCC patients in the early recurrence group and nonearly recurrence group.
	No. of Patients		Univariable Analysis
Variable	Nonearly recurrence group	Early recurrence group	P Value	χ²
Age (y)			0.131	2.28
≤ 60	30(66.7%)	34(81.0%)
> 60	15(35.7%)	8(19.0%)
Sex			0.138	2.197
Male	39(86.7%)	41(97.6%)
Female	6(13.3%)	1(2.4%)
Hypertension			0.888	0.02
Absence	38(84.4%)	35(83.3%)
Presence	7(15.6%)	7(16.7%)
Diabetes			0.101	2.688
Absence	41(91.1%)	33(78.6%)
Presence	3(6.7%)	9(21.4%)
Hepatitis			0.738	0.112
Absence	11(24.4%)	9(21.4%)
Presence	34(75.6%)	33(78.6%)
Ascites			0.493	0.47
Absence	36(80.0%)	31(73.8%)
Presence	9(20.0%)	11(26.2%)
BMI (kg/m²)			0.216	1.529
≤ 18.5	0	3(7.1%)
> 18.5	45(100%)	39(92.9%)
AFP (ng/ml)			0.031	4.673
≤ 400	38(84.4%)	27(64.3%)
> 400	7(15.6%)	15(35.7%)
Platelet (×10⁹/L)			0.064	3.443
≤ 151	25(55.6%)	15(35.7%)
> 151	20(44.4%)	27(64.3%)
NLR			0.952	0.004
≤ 2.4	26(57.8%)	24(57.1%)
> 2.4	19(42.2%)	18(42.9%)
PLR			0.003	9.081
≤ 77	15(33.3%)	3(7.1%)
> 77	30(66.7%)	39(92.9%)
ALT (U/L)			0.004	8.412
< 27	29(64.4%)	14(33.3%)
≥ 27	16(35.6%)	28(66.7%)
AST (U/L)			0.014	5.999
≤ 28	30(66.7%)	17(40.5%)
> 28	15(33.3%)	25(59.5%)
ALB (g/L)			0.377	0.781
≤ 35	2(4.4%)	5(11.9%)
> 35	43(95.6%)	37(88.1%)
TBIL (µmol/L)			0.636	0.224
≤ 23	39(92.9%)	37(88.1%)
> 23	3(7.1%)	5(11.9%)
ALBI Grade			0.32	0.989
I	23(51.1%)	17(40.5%)
II	22(48.9%)	25(59.5%)
PT (s)			0.609	0.261
≤ 14	43(95.6%)	38(90.5%)
> 14	2(4.4%)	4(9.5%)
Child‒Pugh Grade			0.230
A	45(100%)	40(95.2%)
B	0	2(4.8%)
CNLC Stage			0.055	7.619
IA	24(53.3%)	13(31.0%)
IB	19(42.2%)	21(50.0%)
IIA	1(2.2%)	6(14.3%)
IIB	1(2.2%)	2(4.8%)
Tumor number			0.170	1.885
Single	41(91.1%)	34(81.0%)
Multiple	4(8.9%)	8(19.0%)
Tumor diameter (cm)			0.012	6.321
< 5	26(57.8%)	13(31.0%)
≥ 5	19(42.2%)	29(69.0%)
Tumor location			0.180	3.432
Left hemiliver	16(35.6%)	14(33.3%)
Right hemiliver	28(62.2%)	23(54.8%)
Left + Right hemiliver	1(2.2%)	5(11.9%)
Enhancing capsule			0.176	1.833
Absence	13(28.9%)	7(16.7%)
Presence	32(71.1%)	35(83.3%)
Margin			0.018	5.635
Unsmooth	30(66.7%)	37(88.1%)
Smooth	15(33.3%)	5(11.9%)
Typical enhancement model			0.160	1.978
Absence	20(44.4%)	25(59.5%)
Presence	25(55.6%)	17(40.5%)
Peritumoral hyperenhancement			0.826	0.049
Absence	39(86.7%)	38(90.5%)
Presence	6(13.3%)	4(9.5%)
Satellite nodule			0.026	4.941
Absence	44(97.8%)	34(81.0%)
Presence	1(2.2%)	8(19.0%)
Intratumoral necrosis			0.860	0.031
Absence	35(77.8%)	32(76.2%)
Presence	10(22.0%)	10(23.8%)
Intratumoral artery			0.172	1.864
Absence	18(40.0%)	11(26.2%)
Presence	27(60.0%)	31(73.8%)
AFP, alpha-fetoprotein; ALB, albumin; ALBI, albumin‒bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CNLC, China Liver Cancer; NLR, neutrophil‒lymphocyte ratio; PLR, platelet‒lymphocyte ratio; TBIL, total bilirubin;

Development of a predictive model for the identification of early HCC recurrence

The predictive model was constructed according to the regression coefficients and constant terms in the multivariable logistic regression. The equation of the prediction model was logit (P) = 2.993–2.685 X1-1.965 X2-3.822 X3 (X1, PLR; X2, tumor margin; X3, satellite nodule). With the predictive probability value as the test variable and early HCC recurrence as the state variable, a receiver operating characteristic (ROC) curve was used to evaluate the discrimination ability of the prediction model. The area under the curve (AUC) was 0.854 (95% CI 0.776 ~ 0.933), the sensitivity was 85.29%, the specificity was 92.11%, and the Youden index was 0.449 (Fig. 5). The PPV, NPV, and accuracy rate of the prediction model were 78.6%, 80%, and 81.8%, respectively. These results demonstrated that this prediction model has certain clinical value, could be used as a tool for predicting early HCC recurrence in patients after surgery, and could remind clinicians to comprehensively consider patients’ other auxiliary examination results to determine a treatment plan.

Hepatocellular carcinoma (HCC) is one of the most frequently encountered malignancies in clinical practice. The pathogenesis underlying HCC is complex, with risk factors exhibiting high heterogeneity across various regions.¹² The mortality rate associated with HCC is expected to rise in the next decade¹³, which poses a serious threat to people's health. Early postoperative HCC recurrence is an important factor affecting the prognosis of patients. A previous study reported that the 5-year recurrence-free survival (RFS) rate following surgical resection of HCC patients is 34.46%.¹⁴ It is important to identify the factors influencing early HCC recurrence to construct a predictive model for early diagnosis and treatment to effectively improve the prognosis of patients.

In this study, we found that host-related unfavorable factors (AFP > 400 ng/ml, PLR > 77, AST > 28 U/L, and ALT ≥ 27 U/L) and tumor-related unfavorable factors (tumor diameter ≥ 5 cm, unsmooth margins and satellite nodules) were associated with early recurrence. In particular, a PLR > 77, an unsmooth margin, and satellite nodules are independently associated with early postoperative recurrence in HCC patients.

Host-related risk factors

The platelet-to-lymphocyte ratio (PLR) is a broadly recognized marker of inflammation and immunity that is capable of predicting the prognosis of multiple cancers.^15–17 Zheng et al. performed a meta-analysis of 11 cohort studies and reported that a lower PLR is significantly associated with a lower risk of recurrence and higher survival rates after liver resection.¹⁸ This finding is consistent with the findings of this study, which indicate that a PLR > 77 is an independent risk factor for early postoperative recurrence in HCC patients. Previous studies have shown that high platelet counts can promote angiogenesis, the production of adhesion molecules, and the formation of an early metastatic niche, thereby further leading to cancer progression.^19,20 Lymphocytes are closely related to the body's immune defense. An elevated PLR indicates a high platelet count and low lymphocyte count, which explains the correlation between a high PLR and poor prognosis.

Alpha-fetoprotein (AFP) is a glycoprotein that plays an important role in immune regulation and is crucial for early human development and maintaining immune balance during pregnancy. AFP was first identified in human fetal serum in 1956, and it can serve as a transport protein for multiple ligands, such as bilirubin, fatty acids, and some drugs.²¹ Our research indicates that an AFP concentration > 400 ng/ml is associated with early recurrence after HCC surgery, but it cannot be considered an independent risk factor. Jung et al. reported the same results.²² A study by Tsilimigras et al. revealed that AFP was an independent risk factor for early postoperative recurrence of HCC.²³ This may be attributed to differences in study design and sample size.

Like AFP, the other two factors, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), cannot be regarded as independent risk factors. The results of Zhou et al.’s study indicate that high levels of AST and ALT are risk factors for early recurrence of HCC after surgery but cannot be considered independent predictors, which is consistent with the findings of this study.²⁴ Moreover, a retrospective cohort study by Wei et al. demonstrated that the AST is independently associated with postoperative recurrence of HCC.²⁵ A previous study reported that elevated transaminase levels are significantly associated with Ki-67 immunoexpression, which may be one of the reasons for the early postoperative recurrence of HCC in patients with high AST and ALT levels.²⁶

Tumor-related risk factors

On CT images, the lesion margins are frequently related to the benign or malignant nature of the disease. Currently, few studies have directly linked tumor margins with HCC recurrence. However, the relationship between tumor margins and microvascular invasion (MVI) has been extensively studied, and irregular HCC margins are significantly associated with MVI.²⁷ Since MVI is closely related to early postoperative recurrence²², an unsmooth margin may be associated with early postoperative recurrence of HCC. This study revealed that an unsmooth margin is independently associated with early postoperative recurrence of HCC, which aligns with the findings of Zhang et al.²⁸ Unsmooth tumor margins on CT images may indicate rapid tumor proliferation, which helps explain why HCC with unsmooth margins tends to be more aggressive and more likely to recur.²⁹

The results of this study demonstrated that satellite nodules are another independent risk factor for early postoperative recurrence of HCC. Satellite nodules are often considered tumor nodules formed by the spread of tumor cells from a single primary tumor to the surrounding area, representing intrahepatic metastasis within the venous drainage area around the main tumor.³⁰ Early recurrence is usually the result of metastasis from the resected primary cancer or residual in situ microcarcinomas rather than new cancer development.^7,8 Therefore, satellite nodules can predict early recurrence. Previous studies have also proven that satellite nodules are a predictive indicator of early postoperative recurrence of HCC.

Tumor size is the most visually striking morphological feature on CT images and has been widely regarded as one of the most important predictors of tumor recurrence and patient survival.³¹ The results of this study show that a tumor diameter ≥ 5 cm is associated with early postoperative recurrence of HCC, but its independent predictive role has not been revealed. Lee et al. reported that a maximal tumor size ≥ 3 cm is independently associated with early HCC recurrence.³² The impact of tumor size on recurrence may be related to intrahepatic metastasis and portal vein invasion.³³

A logistic regression prediction model

In this study, we constructed a logistic prediction model including the PLR, tumor margins, and satellite nodules through univariate and multivariate logistic regression analyses. The ROC curve for this model was 0.854 (95% CI 0.776 ~ 0.933), and the sensitivity, specificity, PPV, NPV, and accuracy were 85.29%, 92.11%, 78.6%, 80% and 81.8%, respectively. The results of the model are satisfactory. When Logit (P) ≥ 0.449, the likelihood of early postoperative recurrence in HCC patients is high, suggesting that clinicians should comprehensively consider other auxiliary examination results to make individualized treatment decisions and develop reasonable strategies for monitoring for recurrence after resection.

Our study has several limitations. First, this study was an observational and retrospective analysis, so some potential confounders were neglected. Second, this was a single-center study with a small sample size, and prospective studies with larger sample sizes are needed. Finally, the clinical effectiveness of this model in actual applications might be less accurate owing to the limited number of samples.

PLR > 77, unsmooth margins, and satellite nodules were independent risk factors for early recurrence in patients with HCC after hepatectomy. The predictive model constructed in this study has a good predictive effect and can provide information for clinicians to formulate reasonable strategies for postoperative monitoring and treatment of HCC.

Contributions:

J.C. and Z.C. designed research, J.C., T.L., L.J.G, K.X.C and K.L. analyzed the results, J.C. wrote the manuscript. All authors reviewed the manuscript.

Data availability

The datasets generated and/or analysed during the current study are not available since we are still collecting more data for further study, but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

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Contributions.
designed research, Z. C. J.C. and J.C., T.L., L.J.G, K.X.C and K.L. analyzed the results, J.C. wrote the manuscript. All authors reviewed the manuscript.

No competing interests reported.

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Clinical and CT imaging characteristics of early recurrence in patients with hepatocellular carcinoma after curative resection

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Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Inclusion criteria and exclusion criteria

Diagnostic criteria and follow-up

Statistical analysis

Results

Discussion

Host-related risk factors

Tumor-related risk factors

A logistic regression prediction model

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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