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Introduction

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal malignancies worldwide, with hepatectomy remaining a primary curative treatment option for early-stage disease.^1,2 However, postoperative recurrence poses a significant challenge, with 5-year recurrence rate as high as 60%.³ Recurrence can be categorized as early recurrence (<2 years), caused by micro metastases after resection, or late recurrence (>2 years), caused by new tumors arising in a microenvironment prone to carcinogenesis.⁴ Research has shown that HCC patients who experience early recurrence post-resection have a worse prognosis compared to those with late recurrence.⁵ Therefore, accurate prediction of early recurrence is critical for personalized postoperative surveillance and adjuvant therapy strategies.

Certain clinicopathological features, including the Barcelona Clinic Liver Cancer (BCLC) staging system, alpha-fetoprotein (AFP), microvascular invasion (MVI), and satellite nodules (SNs), have been linked to postoperative prognosis.^1,6 However, conventional predictive assessments based on these biomarkers lack sufficient predictive accuracy, and information on MVI and SNs is not available preoperatively. Several recent studies have used artificial intelligence models based on CT/MRI data to effectively predict the risk of postsurgical HCC recurrence risk.^7–10 Compared with CT and MRI, ultrasound possesses the advantages of real-time, radiation-free, and cost-effective, and can also be used for intraoperative evaluation and guide the operation pathway. Additionally, contrast-enhanced ultrasound (CEUS) has emerged as a valuable imaging modality for HCC, offering real-time dynamic assessment of tumor vascularity and perfusion characteristics.¹¹

Previous studies have reported that certain CEUS characteristics such as rapid wash-in, early wash-out phase, rapid portal phase signal regression, and high LI-RADS grading are risk factors associated with early recurrence after radical HCC resection.^12,13 However, a critical gap remains: the interpretation of these characteristics is highly subjective and difficult to quantify, which limits their consistent application in prognostication. To address this gap in objectivity, deep learning (DL) emerges as a powerful tool. DL algorithms can automatically extract massive, quantitative features from medical images to model disease diagnosis and prognosis.¹⁴ The application of DL to liver CEUS is already established for tasks such as focal liver lesion classification and predicting HCC biological behavior,^15,16 demonstrating its potential in this domain. However, these existing applications primarily focus on classification limited to single-phase analysis or static images, potentially overlooking the critical time-dependent kinetic information. The specific knowledge gap our study aims to fill is the use of DL to predict long-term clinical outcomes directly from pre-operative dynamic CEUS cines. Therefore, we hypothesize that a DL-based framework, by leveraging the rich temporal data in multiphase CEUS, can overcome the limitations of subjective interpretation. This study aims to develop and validate such a framework, integrating CEUS data with clinicopathological variables to provide a noninvasive and efficient preoperative tool for predicting early recurrence in patients with early-stage HCC after surgical resection.

Methods

Study Population and Design

This study was performed in accordance with the principles of the Declaration of Helsinki. This retrospective study was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University (Approval No: IIT-O-2025-251). The requirement for informed consent was waived by the Ethics Committee due to the retrospective and minimal-risk nature of the study. All patient data were kept strictly confidential and de-identified prior to analysis. From January 2010 to January 2023, 155 patients with HCC who underwent CEUS within two weeks before hepatectomy were enrolled. The diagnosis of HCC was based on the diagnostic criteria established by the European Association for the Study of the Liver.² Inclusion criteria were: BCLC stage 0 or A; liver function with Child-Pugh class A or B; performance status Eastern Cooperative Oncology Group score 0 or 1; R0 resection;¹⁷ regular follow-up within two years after hepatectomy. The exclusion criteria were poor CEUS imaging quality, recurrent HCC or primary HCC combined with other primary tumors, and a history of antitumor treatment before surgery, such as radiofrequency ablation, microwave ablation, transcatheter arterial chemoembolization, or chemotherapy. Pretreatment baseline clinical characteristics, including demographic data, laboratory test results, and clinical diagnoses, were collected from the Institutional Picture Archiving and Communication System (PACS^®; Carestream Health, Toronto, Canada). Finally, 115 patients’ preoperative CEUS cines and clinical variables were retrospectively analyzed. We developed four CEUS-based AI models to preoperatively predict early recurrence: CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP. We further integrated clinical variables into the CEUS AI model to construct an individualized nomogram for preoperative prediction of early recurrence. The patient enrollment workflow and study design were shown in Figure 1.

Figure 1 Study workflow flowchart. Abbreviations: CEUS, contrast-enhanced ultrasound; HCC, hepatocellular carcinoma; ROI annotation illustration; AP, arterial phase; PP, portal venous phase; LP, late phase; MP, multiple phases.

CEUS Data Acquisition

CEUS was performed by one of two radiologists (ZH and YC) with over 10 years of experience in liver CEUS. Two types of ultrasound instruments were used in this study, Toshiba Aplio (n = 98) and SuperSonic Imagine (n = 17). The system parameters, including gain, dynamic range, mechanical index, output power, and focal zone, were fine-tuned to ensure effective tissue signal reduction without compromising penetration capability. A volume of 2.4 mL of the second-generation contrast agent (SonoVue^®; Bracco Imaging, Milan, Italy) was injected within one second via the elbow, followed by a 5-mL saline flush. The transducer was maintained in a stable position to continuously observe perfusion of the lesion. Three-phase contrast enhancement was dynamically monitored, consisting of arterial phase (AP, 0–30 s), portal venous phase (PP, 31–120 s), and late phase (LP, 121–360 s). The CEUS digital video recordings were stored as DICOM format.

Hepatectomy Procedure and Follow-Up Protocol

Anatomical partial hepatectomy was performed with a resection margin of ≥ 10 mm. All enrolled tumors were safely and completely removed, with no residual tumors following resection (R0 classification). The patients underwent regular follow-up assessments at 1, 3, 6, 9, and 12 months postoperatively, followed by evaluations every 3–6 months thereafter. During these visits, contrast-enhanced imaging (CEUS, CECT, or CEMRI) and serum biomarker testing (AFP and liver function) were performed. In this study, early recurrence was defined as a new lesion with typical imaging features of HCC that appeared in imaging examinations within 2 years after hepatectomy or as confirmed by pathological results from percutaneous liver biopsy or a second surgery.

Developing Prediction Model Based on CEUS

This study employed CEUS cines from three phases (arterial phase, portal venous phase, and late phase) to construct the corresponding single-phase models (CEUS-AP, CEUS-PP, and CEUS-LP). We then developed a comprehensive CEUS-MP (multi-phase) model that integrated all three phases. In each patient, three separate regions of interest (ROIs) were outlined by a radiologist with 5-year experience in CEUS to enable quantitative assessment of the CEUS videos across the arterial, portal venous, and late phases. The annotation tool was ITK-SNAP, which is an open-source software.¹⁸ Specifically, the doctor first annotated the contour of the tumor in the CEUS frame when the tumor displayed clearly. A rectangle to cover the tumor contour and an approximately 0.5 cm wide liver parenchyma surrounding the tumor were created (Figure 2). All frames with the same ROI location determined in the annotated frame were cropped and manually adjusted if necessary. To assess inter-observer variability in ROI delineation, two doctors (one with more than 10 years of experience in CEUS and the other with more than 5 years of experience) independently contoured the ROIs for a randomly selected cohort of 30 patients, blinded to patient outcomes and clinical characteristics. Radiomic features extracted from both sets of ROIs were compared using the intraclass correlation coefficient (ICC) to determine feature reproducibility.

Figure 2 ROI annotation illustration. (a–c) An example of the red/green/blue rectangle ROI segmented in one AP/PP/LP CEUS frame when the tumor displayed clearly. Abbreviations: ROI, region of interest; AP, arterial phase; PP, portal venous phase; LP, late phase; CEUS, contrast-enhanced ultrasound.

Enrolled patients were randomly divided into training (n = 75) and validation (n = 40) cohorts. To minimize model-induced bias, convolutional neural networks (CNN) were employed for the four models based on CEUS cines in different phases consisting of a two-dimensional (2D) convolution layer, 2D max-pooling layer, ReLU activation function, vector of locally aggregated descriptors (VLAD), and a fully connecting layer (FCL) (Figure 3). The inputs of the CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP models were different CEUS cines in the arterial, portal venous, late, and the above-mentioned three phases, respectively. In the four CEUS AI models, convolution layers automatically learn the imaging features from each frame of CEUS, and VLAD is responsible for the quantitative analysis of enhancement patterns over time for the CEUS cines. The FCL guide model predicted the probability of early recurrence for each patient. A ten-fold validation method was adopted in the training phase to optimize the four CEUS AI models. To improve the prediction performance and reduce the risk of overfitting, CEUS cines were split over time into multiple independent samples at two frames per second; in this way, the training population was augmented for better learning. Data augmentation techniques, including random rotations, flips, and intensity variations, were employed to augment the diversity of the training dataset. Moreover, depth-wise convolution was selected to reduce the number of parameters in the training phase. Both L1 and L2 regularization were employed to combat overfitting. L1 regularization promoted sparsity and can perform feature selection by driving less important weights to zero, while L2 regularization penalized large weights to ensure the model was less sensitive to small fluctuations in the input data. This combined approach was chosen to create a more robust and generalizable model given the high-dimensional feature space relative to our sample size. We implemented early stopping based on the validation loss to halt training once performance on the validation set ceased to improve. In this study, the convolutional layers employed a depthwise separable convolution structure to replace the conventional 3D convolution operations, separating the processes of feature convolution filtering and the integration of cross-channel features. A global pooling layer was incorporated before the fully connected layers, aggregating features across temporal and spatial dimensions to reduce the number of neural nodes that the fully connected layers need to process. Through a series of specialized network design strategies, the parameter count of R-DLCEUS was reduced to approximately 20,000. During the training phase, the model used cross-entropy as the objective function and optimized the parameters using the stochastic gradient descent method. Each batch consisted of 16 samples, and the model was trained for 70 epochs. The initial learning rate was set to 0.001 and was reduced by a factor of 0.5 at the 15th, 30th, and 55th epochs. A momentum coefficient of 0.9 was applied during training. For the CEUS-MP model, the single-channel cine loops from the AP, PP, and VP were concatenated along the channel dimension to form a multi-channel input, which was then processed by the subsequent 2D convolutional network. The area under the receiver operating characteristic curve (AUC) was adopted to quantitatively measure the prediction performance of the CEUS AI models.

Figure 3 The overall network structure of CEUS AI models. CNN were employed for the four models based on CEUS cines in different phases. Input step: The CEUS cines of AP/ PP/LP/MP were inputted into the CNN model, respectively. Feature extraction step: The features extracted from three phases were then integrated into one feature collection which demonstrated the characteristics of the entire CEUS cine. 2D Conv x included 2D convolution layer, 2D max-pooling layer, and ReLU activation function. Output step: probability was calculated to estimate risk of early recurrence after hepatectomy. Abbreviations: CEUS, contrast-enhanced ultrasound; AI, artificial intelligence; CNN, convolutional neural network; AP, arterial phase; PP, portal phase; LP, late phase; MP, multiple phases; 2D Conv, two-dimensional convolution; VLAD, vector of locally aggregated descriptors; FCL, full connected layer.

Building Combined Individualized Nomogram

To incorporate information from clinical variables with CEUS cines, an individualized model was developed to preoperatively predict early recurrence after hepatectomy. Specifically, four models developed from CEUS video data (CEUS-AP, CEUS-PP, CEUS-LP, and CEUS-MP) were evaluated to identify the optimal predictive model. Subsequently, the predictive probability of the selected model was used as a Radiomics signature and integrated with clinical variables to build a combined nomogram prediction model based on the multi-variable logistic regression analysis. In order to demonstrate the incremental value of DL-CEUS-based nomogram, univariate and multivariate logistic regression analyses were performed to build the Clinical-only model. The analyzed clinical variables included 16 basic clinical characteristics (Table 1). Variable selection followed a two-stage process. First, all parameters showing marginal significance (P < 0.10) in univariate analyses were included; subsequently, these were incorporated into a stepwise multivariable regression model. Only variables demonstrating statistical significance (P < 0.05) in the final multivariable analysis were retained as independent predictors and were used for combined nomogram development. The prediction performance of the nomogram was assessed using AUC, calibration curves, and decision curves.

Table 1 Baseline Characteristics of Patients

Visualization of the CEUS-MP Model

To enhance the interpretability of the DL model’s predictions regarding early recurrence, we employed Selvaraju R.’s method to transform the DL feature maps into pseudo-colored visualization maps.¹⁹ In these maps, warm-colored (red) pixels represent regions with stronger predictive relevance, indicating high-weight features that significantly contribute to the model’s output. Conversely, the cool-colored (blue) pixels denote areas of weaker correlation corresponding to the low-weight factors in the prediction. This visualization approach effectively highlights the critical image regions that influence the model’s decision-making process.

Statistical Analysis

The statistical software and packages used were Python (version 3.11), PyTorch (version 2.0.1), R (version 3.4.4), and computeC. The chi-square test was used to compare categorical variables. Student’s t-test or the Mann–Whitney test, as appropriate, was used to compare continuous variables. All statistical tests were two-sided, and differences were considered significant at P < 0.05.

Results

Clinical Characteristics

Among 115 patients, there were 93 males (93/115, 80.9%) and 22 females (22/115, 19.1%). The mean tumor size was 3.1 ± 0.9 cm (range, 1.0–5.0 cm). Postoperative follow-up results revealed that among the115 patients, 30 (30/115, 26.1%) experienced early recurrence, whereas 85 (85/115, 73.9%) showed no evidence of early recurrence. There were no significant differences in the baseline characteristics between the training (n = 75) and validation (n = 40) cohorts. Detailed baseline characteristics of the two cohorts are shown in Table 1.

Predictive Performance of CEUS AI Models

The features demonstrated high reproducibility, with 91% achieving excellent ICC values, indicating strong inter-observer agreement in ROI segmentation. In the training cohort, AUCs of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP reached 0.922 (95% CI: 0.816–0.971), 0.829 (95% CI: 0.712–0.954), 0.816 (95% CI: 0.697–0.941) and 0.808 (95% CI: 0.691–0.938), respectively. The DL-based AI model using multiple CEUS cines (CEUS-MP) achieved the best prediction performance compared with the other three models based on single-phase CEUS cine (CEUS-AP, CEUS-PP, and CEUS-LP) (Figure 4a). Similar results were observed in the validation cohort. In the validation cohort, the AUCs of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP were 0.840 (95% CI: 0.701–0.989), 0.741 (95% CI: 0.670–0.921), 0.719 (95% CI: 0.637–0.909), and 0.703 (95% CI: 0.661–0.889), respectively (Figure 4b). Table 2 provides an overview of the quantitative assessment of the four models. CEUS-MP offered significantly higher AUCs than CEUS-AP, CEUS-PP, and CEUS-LP in both training (P = 0.026, P = 0.014, P = 0.001) and validation (P = 0.029, P = 0.016, P = 0.001) cohorts. However, no significant differences in AUCs were observed between CEUS-AP, CEUS-PP, and CEUS-LP in either the training or validation cohorts (all P > 0.05).

Table 2 Predictive Performance of CEUS-MP, CEUS-AP, CEUS-PP, and CEUS-LP in Training and Validation Cohorts

Figure 4 ROC curves of four CEUS AI models. (a) ROC curves of CEUS AI models in training cohort. (b) ROC curves of CEUS AI models in validation cohort. CEUS-MP achieved the best prediction performance compared with CEUS-AP, CEUS-PP, and CEUS-LP in both cohorts.

The Individualized Predictive Value of Combined Nomogram

Multivariable regression analysis revealed that serum albumin (< 35 g/L) (HR = 1.146, P = 0.034), AFP (> 1000 ng/mL) (HR = 1.908, P = 0.028), and the CEUS-MP signature (HR = 5.141, P < 0.001) were independent preoperative predictors of early recurrence (Table 3). An individualized nomogram was built based on these three significant values (Figure 5a). In the training and validation cohorts, the AUCs of the nomogram for early recurrence was 0.945 (95% CI: 0.861–0.965) and 0.871 (95% CI: 0.751–0.970) for early recurrence, respectively. (Figure 5b). The nomogram achieved a sensitivity of 88.9% (95% CI: 75.9–96.1%) and specificity of 98.1% (95% CI: 81.6–99.1%) in the training cohort. In the validation cohort, the sensitivity and specificity of the nomogram were 83.0% (95% CI: 70.4–90.8%) and 82.5% (95% CI: 70.8–91.5%), respectively. The nomogram showed good calibration for early recurrence prediction (Figure 5c). The Hosmer-Lemeshow test showed that there were no significant differences in early recurrence prediction between the nomogram and the ideal reference curve (P = 0.415). The decision curve analysis of the nomogram is presented in Figure 5d. AFP and serum albumin were selected to construct a Clinical-only model. The Clinical-only model had an AUC of 0.573 (95% CI: 0.501–0.675) in the training cohort with a 0.554 (95% CI: 0.512–0.685) accuracy and an AUC of 0.528 (95% CI: 0.500–0.618) in the validation cohort with a 0.545 (95% CI: 0.509–0.678) accuracy. The nomogram achieved a significantly higher predictive performance compared to the Clinical-only model in both training and validation cohorts (Both P < 0.001). The decision curves showed that if the threshold probability of a patient was > 30%, integrating CEUS-MP signatures and clinical variables (AFP and serum albumin) to predict the patient’s early recurrence added more net benefit than using only clinical variables. In addition, the net benefit of the CEUS-MP signature was similar to that of the nomograms.

Table 3 Prognostic Factors to Predict Early Recurrence of HCC Patients After Hepatectomy

Figure 5 Prediction performance of individualized nomogram, ROC curves, calibration curves and Decision curve analysis. (a) Nomogram for predicting the probability of a patient occur early recurrence after hepatectomy. A patient who obtained high total scores of the three factors tends to have high probability of early recurrence after hepatectomy. ALB, 0 means < 35 g/L, 1 means ≥ 35 g/L; AFP, 0 means < 200ng/ mL, 1 means ≥ 200ng/mL. (b) ROC curves of nomogram in training and validation cohort. (c) Calibration curves of the nomogram for predicting early recurrence in training and validation cohorts. (d) Decision curve analysis for nomogram.

Understanding the R-DLCEUS Model

From the visualization maps of the CEUS-LP model, it can be observed that the model focuses more on the early period AP, suggesting its potential ability to capture the “fast-in” feature, which may be closely associated with the early recurrence of HCC. Additionally, we observed that for some early cases, the attention of the model exhibited a “patchy” pattern (Figure 6). We hypothesize that these heterogeneous attention regions may correspond to areas of necrotic or fibrotic tissue, features often associated with aggressive tumor biology. However, this requires further pathological correlation.

Figure 6 Visualization of the CEUS-LP model. (a–c) Representative time-point images (9s, 12s, 15s; early period AP) from dynamic CEUS in a post-hepatectomy patient with early recurrence. Each set of images displayed, from left to right, the original monochrome ultrasound image, the pseudo-color heatmap, and the heatmap overlaid on the original ultrasound image.

Discussion

This study developed a DL-based model using CEUS to preoperatively predict early recurrence in patients with early-stage HCC patients following hepatectomy. Our results demonstrated that the multiphase CEUS model (CEUS-MP) achieved superior predictive performance compared with single-phase models, with AUC of 0.922 and 0.840 in the training and validation cohorts, respectively. Furthermore, the integration of CEUS-MP signatures with clinical variables (serum albumin and AFP) into a nomogram further enhanced predictive accuracy, yielding AUCs of 0.945 and 0.871 in the training and validation cohorts, respectively. These findings highlighted the potential of CEUS combined with DL as an auxiliary method for preoperative risk stratification in patients with HCC.

While conventional ultrasound remains the first-line imaging tool for liver screening, its inability to assess the tumor vasculature limits its diagnostic and prognostic utility. Since CEUS cines overcome these limitations by providing dynamic, contrast-enhanced visualization of tumor microvasculature, leading to more accurate HCC diagnosis, recurrence prediction, and treatment monitoring.²⁰ Given these advantages, we utilized CEUS cines, rather than conventional ultrasound images, to develop our prediction models in this study, with the help of advanced quantification techniques for more precise analysis. The superior performance of the CEUS-MP model highlighted the importance of leveraging multiphase CEUS data, which capture dynamic tumor perfusion characteristics across the arterial, portal venous, and late phases. This aligns with previous studies emphasizing the prognostic value of CEUS features, such as rapid wash-in and early wash-out, in HCC recurrence prediction.¹² However, traditional CEUS interpretation is limited by subjectivity and inter-observer variability. Our DL approach addressed this limitation by automatically extracting quantitative features from CEUS cines, thereby standardizing the analysis and improving the reproducibility. Huang et al conducted a quantitative analysis of CEUS images of patients with hepatocellular carcinoma (HCC) following radical resection to predict early recurrence.²¹ However, their model achieved an AUC of only 0.57 in the testing cohort, which was significantly lower than that of our proposed model. A potential limitation of their approach was the reliance on a single-frame analysis, the peak contrast intensity of the lesion on CEUS, which may have decreased predictive accuracy. By contrast, our study leveraged continuous multiphase CEUS cine imaging, enabling a more comprehensive assessment and significantly improving the precision of early recurrence prediction.

The integration of CEUS-MP signatures with clinical variables into a nomogram represented a significant advancement in personalized HCC management. Multivariate analysis identified serum albumin and AFP as the independent clinical prognostic predictors. Notably, low ALB levels served as a reliable indicator of advanced hepatic dysfunction, reflecting both the progression of liver carcinogenesis and deteriorating hepatic synthetic capacity.²² Concurrently, elevated AFP served as a biological marker indicating high invasiveness in HCC.²³ In such patients, some microlesions may escape detection by imaging examinations during the initial HCC diagnosis, contributing to early recurrence after surgical resection. This study demonstrated that higher AFP levels were correlated with an increased risk of early tumor recurrence. Therefore, the AFP level held a significant value in predicting the prognosis of therapy. The high sensitivity and specificity of the nomogram in both cohorts suggested its clinical utility in identifying high-risk patients who may benefit from other treatment selections or intensified surveillance. Notably, the decision curve analysis indicated that the nomogram provided a greater net benefit than the clinical variables alone when the threshold probability exceeded 30%, supporting its practical application in clinical decision-making. The visualization maps further enhanced interpretability, revealing that the model focused on regions with heterogeneous enhancement patterns, which may correlate with aggressive tumor biology. The heterogeneous or peripheral enhancement patterns highlighted by the model were often associated with active tumor regions, while the lack of enhancement in certain areas may correlate with central necrosis or fibrosis commonly found in larger lesions.

Limitations

Our study has several limitations. First, it is a retrospective study conducted at a single center, which introduces potential selection bias. Our patient cohort may not fully represent the more heterogeneous patient populations seen across different healthcare institutions. Second, the uniform high-standard scanning protocols and specialized operators may have inflated the performance estimates of our model. Moreover, the relatively small cohort, particularly the limited number of early recurrence events, is a recognized limitation that warrants caution in interpreting the findings and underscores the need for future validation in larger populations. Furthermore, the use of two different ultrasound systems for CEUS acquisition, despite our efforts at intensity normalization, represents a potential technical confounder. Therefore, the generalizability of our findings needs to be further validated in real-world, multi-center settings, and should also explore incorporating additional imaging modalities (eg, MRI or CT) and molecular biomarkers to refine predictive accuracy.

Conclusion

In conclusion, our study demonstrates that a DL-based CEUS framework, particularly when combined with clinical variables, shows strong potential for the preoperative prediction of early recurrence in patients with HCC undergoing hepatectomy. While these initial results are promising, further validation in large-scale, multi-center, prospective cohorts is essential to confirm its generalizability and establish clinical utility. If validated, this approach could facilitate personalized postoperative management and potentially improve long-term patient outcomes. Future work should also focus on integrating this model with other imaging modalities, such as CT or MRI, to further enhance predictive accuracy.

Ethics Approval and Consent to Participate

This study was approved by the Institutional Review Board of The Second Affiliated Hospital of Nanchang University.

Acknowledgments

The authors thank the data collectors for their efforts and interest in participating in data collection. We would like to thank the patients who willingly provided all the necessary information without any reservation.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 82360348), the Jiangxi Provincial Natural Science Foundation (Grant No. 20232BAB216096), and the Jiangxi Ganpo Outstanding Talent Support Program–Academic and Technical Discipline Leader Development Project (Grant No. 20243BCE51175).

Disclosure

All authors have no conflicts of interest to declare in this work.

References

1. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391(10127):1301–1314. doi:10.1016/S0140-6736(18)30010-2

2. European Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182–236. doi:10.1016/j.jhep.2018.03.019

3. Lim KC, Chow PK, Allen JC, et al. Systematic review of outcomes of liver resection for early hepatocellular carcinoma within the Milan criteria. Br J Surg. 2012;99:1622–1629. doi:10.1002/bjs.8915

4. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. doi:10.1038/s41572-020-00240-3

5. Poon RT, Fan ST, Ng IO, et al. Different risk factors and prognosis for early and late intrahepatic recurrence after resection of hepatocellular carcinoma. Cancer. 2000;89(3):500–507.

6. Villanueva A. Hepatocellular carcinoma. N Engl J Med. 2019;380(15):1450–1462. doi:10.1056/NEJMra1713263

7. Lee IC, Huang JY, Chen TC, et al. Evolutionary learning-derived clinical-radiomic models for predicting early recurrence of hepatocellular carcinoma after resection. Liver Cancer. 2021;10(6):572–582. doi:10.1159/000518728

8. Kucukkaya AS, Zeevi T, Chai NX, et al. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Sci Rep. 2023;13(1):7579. doi:10.1038/s41598-023-34439-7

9. Yuan C, Wang Z, Gu D, et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram. Cancer Imaging. 2019;19(1):21. doi:10.1186/s40644-019-0207-7

10. Hui TCH, Chuah TK, Low HM, et al. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study. Clin Radiol. 2018;73(12):1056.e11–1056.e16. doi:10.1016/j.crad.2018.07.109

11. Alzaraa A, Gravante G, Chung WY, et al. Contrast-enhanced ultrasound in the preoperative, intraoperative and postoperative assessment of liver lesions. Hepatol Res. 2013;43(8):809–819. doi:10.1111/hepr.12044

12. Cao K, Wu L, Wang X, et al. Risk factors for early recurrence after radical resection of hepatocellular carcinoma based on preoperative contrast-enhanced ultrasound and clinical features. Technol Cancer Res Treat. 2024;23:15330338241281327. doi:10.1177/15330338241281327

13. Terzi E, Iavarone M, Pompili M, et al. Contrast ultrasound LI-RADS LR-5 identifies hepatocellular carcinoma in cirrhosis in a multicenter restropective study of 1,006 nodules. J Hepatol. 2018;68(3):485–492. doi:10.1016/j.jhep.2017.11.007

14. Wang K, Lu X, Zhou H, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicenter study. Gut. 2019;68(4):729–741. doi:10.1136/gutjnl-2018-316204

15. Ding W, Meng Y, Ma J, et al. Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions. J Hepatol. 2025;83(2):426–439. doi:10.1016/j.jhep.2025.01.011

16. Xu W, Zhang H, Zhang R, et al. Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma. Eur Radiol. 2025;35(2):989–1000. doi:10.1007/s00330-024-10985-0

17. Hermanek P, Wittekind C. Residual tumor (R) classification and prognosis. Semin Surg Oncol. 1994;10(1):12–20. doi:10.1002/ssu.2980100105

18. Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–1128. doi:10.1016/j.neuroimage.2006.01.015

19. Selvaraju RR, Cogswell M, Das A. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. 2017:618–626.

20. Kudo M, Ueshima K, Osaki Y, et al. B-Mode ultrasonography versus contrast-enhanced ultrasonography for surveillance of hepatocellular carcinoma: a prospective multicenter randomized controlled trial. Liver Cancer. 2019;8(4):271–280. doi:10.1159/000501082

21. Huang Z, Shu Z, Zhu RH, et al. Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma. World J Gastrointest Oncol. 2022;14(12):2380–2392. doi:10.4251/wjgo.v14.i12.2380

22. Huang YH, Wu JC, Chen CH, et al. Comparison of recurrence after hepatic resection in patients with hepatitis B vs. hepatitis C-related small hepatocellular carcinoma in hepatitis B virus endemic area. Liver Int. 2005;25(2):236–241. doi:10.1111/j.1478-3231.2005.01081.x

23. Yang L, Gu D, Wei J, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer. 2019;8(5):373–386. doi:10.1159/000494099

Introduction

Globally, hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with increasing incidence projected over the next two decades. In China, it is the second most lethal cancer, and the long-term prognosis following curative surgery remains poor due to high recurrence rates, with 5-year recurrence rates reported at approximately 50%–70%.^1–6 Recurrence is most frequent within the first postoperative year and often appears as distant intrahepatic or extrahepatic metastases, thought to originate from undetected micrometastases present at surgery. Tumor-related features such as size, number, differentiation, microvascular invasion (MVI), and elevated alpha-fetoprotein (AFP) are major determinants of early recurrence. Conversely, factors including patient age, sex, underlying liver disease etiology, and cirrhosis are more commonly linked to late recurrence.^7–9 While transcatheter arterial chemoembolization (TACE) is commonly used in this setting, the introduction of immune checkpoint inhibitors (ICIs) offers a novel therapeutic option. Building on recent trials investigating ICIs in advanced or unresectable HCC, such as IMbrave150 and CheckMate 459, attention has shifted to their potential in earlier disease stages. However, limited data exist regarding their application following surgery, especially in patients with MVI. Moreover, the duration of ICI therapy necessary to maximize efficacy remains undefined. This study sought to compare ICI-based therapy with TACE in a real-world postoperative setting and to assess whether extended ICI treatment (≥12 months) offers added benefit in reducing recurrence.

Patients and Methods

Patients and Study Design

To evaluate the comparative efficacy of adjuvant ICI therapy versus TACE in improving RFS among HCC patients with MVI, we conducted a retrospective, multicenter cohort study enrolling patients who underwent curative-intent hepatic resection between January 1, 2017, and March 31, 2024, at Peking Union Medical College Hospital (Beijing), China-Japan Friendship Hospital (Beijing), and Sun Yat-sen University Cancer Center (Guangzhou). Inclusion criteria were: (1) complete (R0) resection; (2) age between 18 and 75 years; (3) pathologically confirmed HCC with MVI; and (4) Eastern Cooperative Oncology Group performance status (ECOG PS) score ≤ 1. Exclusion criteria encompassed: (1) recurrent HCC following prior curative resection; (2) history of spontaneous tumor rupture with hemorrhage; (3) coexistence of other active malignancies, including those in sustained remission; (4) administration of any neoadjuvant treatment modalities for HCC, including TACE, molecular targeted therapy, immunotherapy, or radiotherapy; and (5) death from non-HCC-related causes prior to follow-up.

Treatment and Data Collection

For patients anticipated to undergo extensive hepatectomy, preoperative evaluation of hepatic functional reserve was performed using the indocyanine green (ICG) retention test at 15 minutes. Surgical procedures were standardized across centers in accordance with established protocols previously described in the literature.¹⁰

Postoperative follow-up was routinely conducted 4 to 8 weeks after hepatic resection. For patients with MVI, adjuvant therapy was recommended contingent upon satisfactory general health and absence of contraindications. Given the lack of universally accepted adjuvant standards for MVI-positive HCC, treatment decisions were made based on multidisciplinary clinical evaluation, institutional practice patterns, and physician consensus. Regimen selection followed consistent clinical principles across centers and reflected contemporary real-world management of high-risk HCC. All adjuvant therapies, when applied, were initiated within 4 to 8 weeks following surgery.

Adjuvant TACE was initiated within 4–6 weeks post-resection, involving femoral artery catheterization and infusion of chemotherapeutic agents, as per institutional protocols.

Frequently employed targeted agents included lenvatinib,¹¹ apatinib,¹² donafenib, regorafenib,¹³ bevacizumab,¹⁴ administered once daily with dosage adjusted according to body weight or manufacturer guidelines. Immunotherapeutic agents commonly utilized in the adjuvant setting included sintilimab,¹⁵ carrelizumab,¹⁶ atezolizumab,¹⁴ tislelizumab.¹⁷ Prior to initiation, patients underwent thorough pre-treatment screening comprising blood panels, thyroid function assessment, electrocardiography, and chest CT imaging to ensure eligibility. ICIs were administered intravenously every three weeks in accordance with recommended dosing protocols. Vital signs were closely monitored during infusion and for at least 1hour post-infusion to promptly detect and manage any infusion-related reactions. Although the intended duration of adjuvant ICI therapy was 24 months, actual treatment durations varied in clinical practice. Some patients discontinued treatment within the first few months due to adverse events, financial burden, or poor adherence, even in the absence of tumor recurrence. To reduce immortal time bias in survival comparisons, patients who received ICI therapy for less than 6 months or experienced disease recurrence within 6 months were excluded from the duration-based survival analysis.

Follow-Up

Postoperative surveillance was rigorously implemented. All patients were scheduled for monthly follow-up visits during the first three months after hepatic resection, followed by assessments every three months for the subsequent two years, and semiannually thereafter. Each follow-up visits included evaluation of serum tumor markers, abdominal ultrasonography, and contrast-enhanced abdominal CT or MRI. Additional investigations, such as chest CT, bone scan, or positron emission tomography–CT (PET-CT), were performed when distant metastasis was clinically suspected. Follow-up was continued until patient death or loss to follow-up. The endpoint of the follow-up was September 20, 2024. Treatment of recurrence was personalized according to tumor profile, organ function, and patient status. The primary endpoint was RFS, as it reflects the direct effect of adjuvant therapy on preventing early relapse, particularly relevant in MVI-positive patients. OS was designated as a secondary endpoint, acknowledging the variability introduced by post-recurrence treatment heterogeneity. Of the 319 patients who received postoperative adjuvant therapy, 30 (9.4%) were lost to follow-up. Among the 80 patients under routine surveillance who did not receive adjuvant treatment, 9 (11.2%) were lost to follow-up. These individuals were not excluded from the analysis; instead, the time of their last documented follow-up was incorporated as censored observations in the survival analysis. This censoring strategy, implemented via the Kaplan–Meier method, ensured the inclusion of all available patient data and upheld the statistical robustness and validity of survival estimations.

Statistical Analysis

Continuous variables were summarized as mean ± standard deviation or median with interquartile range (IQR), and compared using Student’s t test or Mann–Whitney U-test, as appropriate. Categorical variables were compared using Pearson’s chi-square or Fisher’s exact test. To adjust for baseline differences between the TACE and immunotherapy groups, 1:1 PSM was performed using a nearest-neighbor algorithm (caliper = 0.2, no replacement). Covariates included AFP grade, HBV DNA, tumor differentiation, surgical margin, tumor number and size, satellite nodules, tumor embolus, MVI, and liver cirrhosis, selected based on clinical relevance and potential influence on treatment assignment. RFS and OS were estimated by Kaplan–Meier analysis and compared using Log rank tests; Cox regression identified independent predictors. Variables with P < 0.05 in univariate analysis, as well as clinically relevant covariates, were entered into the multivariate Cox regression model. To reduce immortal time bias, a 6-month landmark analysis excluded patients with recurrence or death before this point. Analyses were conducted using R (v4.3.1), with p < 0.05 considered significant.

Results

Patient Characteristics

From January 2017 to March 2024, a total of 1526 HCC patients from the three aforementioned centers were initially enrolled. All patients underwent curative liver resection, and postoperative pathology confirmed the diagnosis of HCC. Among them, 1,048 patients without MVI, 46 patients who received neoadjuvant therapy, 23 patients diagnosed with concurrent malignancies, and 10 patients who died from non-HCC-related causes were excluded. Ultimately, 399 patients were included in the final analysis (Figure 1). Among these patients, 132 received TACE alone, 58 received TACE combined with targeted therapy, 68 received TACE combined with targeted immunotherapy, 21 received targeted therapy combined with immunotherapy, 40 received only immunotherapy, and the remaining 80 patients did not receive any form of postoperative adjuvant therapy. Among the 319 patients who received postoperative adjuvant therapy, 30 were lost to follow-up, and among the 80 patients under active monitoring, 9 were lost to follow-up.

Figure 1 Patients flow chart. Abbreviations: MVI, microvascular invasion; HCC, hepatocellular carcinoma; TACE, transcatheter arterial chemoembolization; ICI, immune checkpoint inhibitor.

A total of 129 patients received adjuvant immunotherapy following curative hepatic resection. Among them, 40 received tislelizumab (anti–PD-1), 24 received camrelizumab (anti–PD-1), 21 received sintilimab (anti–PD-1), 11 received atezolizumab (anti–PD-L1), 10 received cadonilimab (a bispecific PD-1/CTLA-4 antibody), 10 received toripalimab (anti–PD-1), 7 received envafolimab (anti–PD-L1), and 6 received pembrolizumab (anti–PD-1). To minimize immortal time bias in the treatment duration analysis, 45 patients were excluded due to tumor recurrence occurring between 2 and 6 months postoperatively or receipt of immunotherapy for fewer than 6 months. The remaining 84 patients were included in the final analysis: 46 received adjuvant immunotherapy for less than 12 months, and 38 received it for 12 months or longer (Figure 1).

Before matching, the TACE and immunotherapy groups were largely comparable in baseline demographics and disease characteristics, except for differences in age, HBV DNA, and AFP levels. After 1:1 propensity score matching, 108 patients were included in each group (Figure 1), with no significant differences observed in baseline characteristics. A summary of baseline characteristics before and after matching is presented in Table 1.

Table 1 Baseline Characteristics of HCC Patients in the TACE and Immunotherapy Groups Before and After PSM

Treatment and Efficacy

During a median follow-up of 18 months (IQR 10–29 months), recurrence or metastasis occurred in 44 patients (34.1%) in the immunotherapy cohort and 86 patients (65.2%) in the TACE cohort. The predominant recurrence sites were the liver, lungs, and bones.

The median RFS was significantly longer in the immunotherapy cohort at 35 months (95% CI, 19–NA), compared with 16 months (95% CI, 10.8–27) in the TACE cohort (HR = 0.50, 95% CI, 0.34–0.72; p = 0.00015; Figure 2a). RFS rates at 12, 24, and 36 months were 75.4%, 53.1%, and 49.6% in the immunotherapy group, versus 54.5%, 42.8%, and 25.9% in the TACE group. Mortality was lower in the immunotherapy cohort (5.4%, 7 patients) compared to the TACE cohort (17.4%, 23 patients). Median overall survival (OS) was not reached in either group, but OS was significantly improved with immunotherapy (HR = 0.34, 95% CI, 0.14–0.80; p = 0.0096; Figure 2b). OS rates at 12, 24, and 36 months were 100.0%, 93.6%, and 86.9% in the immunotherapy cohort, versus 88.6%, 82.8%, and 81.0% in the TACE cohort. After PSM, the RFS benefit associated with immunotherapy remained statistically significant (HR = 0.54, 95% CI, 0.36–0.82; p = 0.0042; Figure 2c), while the OS difference was no longer statistically significant (HR = 0.43, 95% CI, 0.18–1.40; p = 0.060; Figure 2d).

Figure 2 Kaplan-Meier survival curves comparing the adjuvant ICI cohort and the TACE cohort: (a) RFS and (b) OS before PSM; (c) RFS and (d) OS after PSM. Abbreviations: HR, hazard ratio; CI, confidence interval.

Univariate and Multivariate Analyses of RFS and OS

Univariate and multivariate Cox regression analyses identified several independent predictors of poor prognosis (Table 2). For RFS, an advanced CNLC stage was significantly associated with shorter survival. For OS, a resection margin of less than 0.5 cm was identified as independent adverse prognostic factors.

Table 2 Univariate and Multivariate Analysis for RFS and OS of HCC Patients

Adverse Events (AE) in the Immunotherapy Cohort

Among the 129 patients who received immunotherapy, 55 patients (42.6%) experienced at least one treatment-related AE. Grade 1–2 AEs occurred in 51 patients (39.5%), while 13 patients (10.1%) experienced grade 3–4 events. No grade 5 AEs were reported (Supplementary Table 1). The most frequent AEs (any grade) were rash (9.3%), elevated AST (8.5%), ALT (7.8%), thrombocytopenia (7.0%), hypoalbuminemia (6.2%), and hypothyroidism (6.2%). Most events were grade 1–2 in severity. The most common grade 3–4 AEs included rash (5.4%), hypothyroidism (4.7%), and hypertension (3.9%). Other observed toxicities such as increased bilirubin (3.9%), elevated creatinine (3.9%), leukopenia (3.1%), mouth ulcers (3.1%), and fatigue (1.6%) were generally mild and manageable. No treatment-related deaths were observed.

Efficacy of Immunotherapy Duration on Survival Outcomes and AEs

To minimize immortal time bias, we excluded patients with recurrence-free survival less than 6 months and those who received ICI therapy for fewer than 6 months. Among the remaining cohort, patients who received adjuvant ICI therapy for 12 months or longer demonstrated significantly better RFS compared to those treated for less than 12 months (HR: 0.46, 95% CI: 0.21–0.99, p = 0.041; Figure 3a). A similar trend toward improved overall survival was observed, although the difference did not reach statistical significance (HR: 0.19, 95% CI: 0.02–1.59, p = 0.086; Figure 3b).

Figure 3 Survival outcomes of patients receiving adjuvant ICI therapy for ≥12 months versus <12 months: (a) RFS and (b) OS. Abbreviations: HR, hazard ratio; CI, confidence interval.

Among the 129 patients who received ICI-based adjuvant therapy, 68 received TACE combined with targeted immunotherapy, 21 received targeted immunotherapy, and 40 received immunotherapy alone. To account for the potential influence of different treatment regimens on survival outcomes, we conducted a subgroup analysis focusing on the largest group—patients who received postoperative TACE combined with targeted immunotherapy. After excluding those with a DFS less than 6 months and those who received immunotherapy for less than 6 months, we reevaluated RFS and OS. The analysis showed that patients who received ICIs for 12 months or longer had significantly improved RFS compared to those treated for less than 12 months (HR: 0.29, 95% CI: 0.11–0.79, p = 0.011; Figure 4a). While a longer OS was also observed in patients treated for 12 months or more, this difference was not statistically significant (HR: 0.19, 95% CI: 0.02–1.61, p = 0.089; Figure 4b).

Figure 4 Survival outcomes in the subgroup receiving TACE combined with targeted immunotherapy, stratified by ICI treatment duration (≥12 months vs <12 months): (a) RFS and (b) OS. Abbreviations: HR, hazard ratio; CI, confidence interval.

Among patients who received adjuvant immunotherapy, no significant differences were observed in the total number of adverse events between those treated for ≥12 months and those treated for <12 months (84.8% vs 89.5%, p >0.999; Supplementary Table 2). Similarly, the occurrence of grade 3–4 adverse events did not differ significantly between the two groups (26.1% vs 15.9%, p = 0.149; Supplementary Table 2). When comparing specific adverse events, no individual AE type showed a statistically significant difference between the two groups. However, numerically higher rates of hypertension and thrombocytopenia were noted in the ≥12-month treatment group, suggesting a trend toward increased incidence of some events with prolonged immunotherapy. Overall, extended treatment duration was not associated with a significantly increased risk of severe toxicity.

Discussion

Adjuvant therapies such as TACE, targeted agents, and immunotherapy have been associated with improved RFS and OS in HCC patients after curative resection.^18–20 However, the optimal duration of adjuvant immunotherapy for HCC patients with high-risk recurrence factors remains undefined, and real-world evidence on this topic is lacking. Although certain guidelines recommend that adjuvant immunotherapy should not exceed one year, they do not specify a minimum or preferred treatment duration.^21,22 In our study, compared to TACE alone, immunotherapy—either as monotherapy or in combination with TACE or targeted agents—was associated with a significant reduction in recurrence and improvement in OS among HCC patients with MVI who underwent R0 resection. After PSM, the benefit in RFS remained statistically significant, while the difference in overall survival was attenuated and no longer reached statistical significance. Importantly, ICI-based adjuvant therapies did not lead to a significant increase in AEs, with most AEs being grade 1–2, indicating good safety and tolerability. Among patients receiving adjuvant ICIs, a treatment duration of 12 months or longer was associated with significantly improved RFS compared to shorter durations. While a numerically favorable trend in OS was noted in the longer-duration group, the difference was not statistically significant. Therefore, no definitive conclusion regarding OS benefit can be drawn based on the current data, and this observation should be interpreted with caution. Furthermore, the total number of AEs and the incidence of grade 3–4 AEs were not significantly increased with longer treatment durations. Nevertheless, given the limited sample size of our study, larger and more comprehensive trials are needed to validate the safety and efficacy of postoperative adjuvant ICI therapy in this setting.

The clinical efficacy of ICIs was initially established in the advanced or unresectable HCC setting, as demonstrated by trials such as CheckMate 459²³ and IMbrave150.⁷ These studies showed that ICIs enhance antitumor immunity and may eradicate disseminated tumor cells. Extending this principle to earlier disease stages, adjuvant therapy aims to eliminate residual micrometastases and reduce distant recurrence, a major cause of treatment failure in MVI-positive patients. Indeed, several recent trials have tested this hypothesis. IMbrave050 reported an early RFS benefit with atezolizumab plus bevacizumab, though its updated analysis raised concerns about durability.²⁴ In contrast, a Phase II randomized controlled trial investigating adjuvant sintilimab showed a significant improvement in RFS among HCC patients with MVI,¹⁹ aligning with our findings and underscoring that high-risk populations may derive the greatest benefit. Retrospective studies have also suggested that adjuvant ICIs may improve prognosis among patients at high risk of recurrence.^25,26

Although several studies have investigated the safety and efficacy of adjuvant immunotherapy for HCC, the optimal duration of treatment has not been thoroughly explored. Given that treatment duration may critically influence patient outcomes, there is an urgent need for dedicated clinical trials addressing this issue. However, research specifically focused on treatment duration remains scarce. A prospective, multicenter cohort study evaluated the impact of adjuvant ICI treatment duration on RFS and OS in HCC patients at high risk of recurrence.²⁷ The results suggested that patients receiving adjuvant ICI therapy for more than six months tended to achieve better RFS and OS compared to those treated for six months or less, although the differences did not reach statistical significance. Despite the absence of a positive finding, the study indicated that six months of adjuvant ICI therapy might be insufficient and that extended treatment duration could potentially yield greater clinical benefits. Importantly, the design of ongoing Phase III randomized trials also reflects this rationale. Major studies such as CheckMate-9DX, KEYNOTE-937, JUPITER-04, SHR-1210-III-325, EMERALD-2, and DaDaLi have all adopted a 12-month adjuvant ICI regimen as the standard duration,²⁷ underscoring the clinical plausibility of our chosen cutoff. Nevertheless, the optimal duration of adjuvant immunotherapy remains an unresolved issue, not only in HCC but also in other malignancies such as non-small cell lung cancer^28,29 and melanoma, where prolonged ICI therapy has shown improved outcomes in certain settings. Our findings suggest that extending ICI therapy beyond 12 months may confer additional benefits for high-risk HCC patients; however, this hypothesis requires validation in prospective randomized studies. Future research should focus on defining the optimal duration of adjuvant immunotherapy, identifying predictive biomarkers for treatment benefit, and developing combination strategies tailored to individual recurrence risk profiles.

As a retrospective study, our analysis is inevitably subject to inherent biases. We acknowledge that comparisons based on treatment duration are vulnerable to immortal time bias, as longer-lived patients may be more likely to receive prolonged therapy. To mitigate this issue, we excluded patients who experienced recurrence or death within 6 months after surgery and those who received ICI therapy for less than 6 months. By restricting the analysis to patients who survived at least 6 months and initiated ICI treatment early, the impact of immortal time bias was reduced, although residual confounding remains possible. We also recognize that analyses involving secondary endpoints and subgroup comparisons may increase the risk of type I error. Another limitation is that the majority of ICIs used in our cohort were PD-1 inhibitors, with only a minority of patients treated with a bispecific PD-1/CTLA-4 antibody. Consequently, the efficacy and safety of other classes of immunotherapeutic agents, such as PD-L1 inhibitors, CTLA-4 inhibitors, and dual-targeting antibodies, were not assessed and warrant further investigation. Moreover, the heterogeneity of treatment regimens within our cohort may have influenced the outcomes. In addition, most patients in our study had HBV-related HCC, reflecting the epidemiological profile of HBV-endemic regions. Therefore, the generalizability of our findings to populations with HCV-related, alcohol-related, or non-viral HCC remains uncertain. Taken together, these limitations indicate that our conclusions should be interpreted with caution. Nonetheless, our findings provide important insights into the potential inadequacy of immunotherapy durations shorter than one year in high-risk HCC patients and highlight the need for prospective, standardized studies across diverse patient populations to confirm these observations.

Conclusions

This retrospective cohort study suggests that adjuvant ICI therapy following curative resection may improve RFS in HCC patients at high risk of recurrence compared to TACE. Notably, our findings indicate that a treatment duration of 12 months or longer is associated with improved RFS in patients with MVI. However, no statistically significant improvement in OS was observed with longer treatment duration. These results highlight the need to reconsider adjuvant immunotherapy strategies in this population, and underscore the importance of prospective, randomized, and large-scale clinical trials to determine the optimal duration of adjuvant ICI therapy for HCC.

Abbreviations

HCC, Hepatocellular carcinoma; MVI, Microvascular invasion; ICI, Immune checkpoint inhibitor; TACE, Transcatheter arterial chemoembolization; RFS, Recurrence-free survival; OS, Overall survival; PSM, Propensity score matching; AFP, Alpha-fetoprotein; HAIC, Hepatic arterial infusion chemotherapy; ECOG PS, Eastern Cooperative Oncology Group performance status; CT, Computed tomography; MRI, Magnetic resonance imaging; ICG, Indocyanine green; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; CNLC, China Liver Cancer staging system; BCLC, Barcelona Clinic Liver Cancer staging; PET-CT, Positron emission tomography-computed tomography; HR, Hazard ratio; CI, Confidence interval; AE, Adverse event; PD-1, Programmed death-1; PD-L1, Programmed death-ligand 1; CTLA-4, Cytotoxic T-lymphocyte-associated protein 4.

Data Sharing Statement

All data supporting the results of the study can be found in the article. Further inquiries can be directed to the corresponding author.

Statement of Ethics

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. I-23PJ964). Informed consent was obtained from all individual participants included in the study.

Acknowledgments

Xiaokun Chen, Jiali Xing, and Baoluhe Zhang are co-first authors for this study. We thank all the patients and the medical staff.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by the National Natural Science Foundation of China (81972698); the CAMS Innovation Fund for Medical Sciences (CIFMS 2021-I2M-01-014); Changzhou Xi Tai Hu development foundation for frontier cell- therapeutic technology (2024-P-019); the 2024 PhD Short-term Academic Visiting Program of Peking Union Medical College; the Start-up Fund from the Department of Liver Surgery, Peking Union Medical College Hospital; the Central high-level hospital clinical research special key cultivation project (2022-PUMCH-C-047); and 2021 Liver Cancer Diagnosis and Treatment Exchange Fund of Hubei Chen Xiaoping Science and Technology Development Foundation (CXPJJH1200009-01).

Disclosure

The authors have no conflicts of interest to declare in this work.

References

1. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47–53. doi:10.1016/j.jncc.2024.01.006

2. Rumgay H, Arnold M, Ferlay J, et al. Global burden of primary liver cancer in 2020 and predictions to 2040. J Hepatol. 2022;77(6):1598–1606. doi:10.1016/j.jhep.2022.08.021

3. Zhou L, Wang S-B, Chen S-G, et al. Risk factors of recurrence and poor survival in curatively resected hepatocellular carcinoma with microvascular invasion. Adv Clin Exp Med. 2020;29(7):887–892. doi:10.17219/acem/76750

4. Lee S, Kang TW, Song KD, et al. Effect of microvascular invasion risk on early recurrence of hepatocellular carcinoma after surgery and radiofrequency ablation. Ann Surg. 2021;273(3):564–571. doi:10.1097/SLA.0000000000003268

5. Straś WA, Wasiak D, Łągiewska B, et al. Recurrence of hepatocellular carcinoma after liver transplantation: risk factors and predictive models. Ann Transplant. 2022;27:e934924. doi:10.12659/AOT.934924

6. Hirokawa F, Hayashi M, Asakuma M, et al. Risk factors and patterns of early recurrence after curative hepatectomy for hepatocellular carcinoma. Surg Oncol. 2016;25(1):24–29. doi:10.1016/j.suronc.2015.12.002

7. Qin S, Chen M, Cheng A-L, et al. Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): a randomised, open-label, multicentre, Phase 3 trial. Lancet. 2023;402(10415):1835–1847. doi:10.1016/S0140-6736(23)01796-8

8. Pan H, Zhou L, Cheng Z, et al. Perioperative Tislelizumab plus intensity modulated radiotherapy in resectable hepatocellular carcinoma with macrovascular invasion: a phase II trial. Nat Commun. 2024;15(1):9350. doi:10.1038/s41467-024-53704-5

9. Peng N, Mao L-F, Su J-Y, et al. The efficacy and safety of tislelizumab with or without tyrosine kinase inhibitor as adjuvant therapy in hepatocellular carcinoma with high-risk of recurrence after curative resection. Front Immunol. 2025;16:1593153. doi:10.3389/fimmu.2025.1593153

10. Shi M, Guo R-P, Lin X-J, et al. Partial hepatectomy with wide versus narrow resection margin for solitary hepatocellular carcinoma: a prospective randomized trial. Ann Surg. 2007;245(1):36–43. doi:10.1097/01.sla.0000231758.07868.71

11. Kudo M, Finn RS, Qin S, et al. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet. 2018;391(10126):1163–1173. doi:10.1016/S0140-6736(18)30207-1

12. Qin S, Li Q, Gu S, et al. Apatinib as second-line or later therapy in patients with advanced hepatocellular carcinoma (AHELP): a multicentre, double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Gastroenterol Hepatol. 2021;6(7):559–568. doi:10.1016/S2468-1253(21)00109-6

13. Bruix J, Qin S, Merle P, et al. Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;389(10064):56–66. doi:10.1016/S0140-6736(16)32453-9

14. Zhu AX, Abbas AR, de Galarreta MR, et al. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat Med. 2022;28(8):1599–1611. doi:10.1038/s41591-022-01868-2

15. Ren Z, Xu J, Bai Y, et al. Sintilimab plus a bevacizumab biosimilar (IBI305) versus sorafenib in unresectable hepatocellular carcinoma (ORIENT-32): a randomised, open-label, Phase 2–3 study. Lancet Oncol. 2021;22(7):977–990. doi:10.1016/S1470-2045(21)00252-7

16. Qin S, Chan SL, Gu S, et al. Camrelizumab plus rivoceranib versus sorafenib as first-line therapy for unresectable hepatocellular carcinoma (CARES-310): a randomised, open-label, international phase 3 study. Lancet. 2023;402(10408):1133–1146. doi:10.1016/S0140-6736(23)00961-3

17. Qin S, Kudo M, Meyer T, et al. Tislelizumab vs sorafenib as first-line treatment for unresectable hepatocellular carcinoma: a phase 3 randomized clinical trial. JAMA Oncol. 2023;9(12):1651–1659. doi:10.1001/jamaoncol.2023.4003

18. Wen Y, Lu L, Mei J, et al. Hepatic arterial infusion chemotherapy vs transcatheter arterial chemoembolization as adjuvant therapy following surgery for MVI-positive hepatocellular carcinoma: a multicenter propensity score matching analysis. J Hepatocell Carcinoma. 2024;11:665–678. doi:10.2147/JHC.S453250

19. Wang K, Xiang Y-J, Yu H-M, et al. Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med. 2024;30(3):708–715. doi:10.1038/s41591-023-02786-7

20. Chen X, Wu X, Peng W, et al. Combined TACE with targeted and immunotherapy versus TACE alone improves DFS in HCC with MVI: a multicenter propensity score matching study. J Hepatocell Carcinoma. 2025;12:561–577. doi:10.2147/JHC.S504016

21. Benson AB, D’Angelica MI, Abrams T, et al. NCCN clinical practice guidelines in Oncology. (NCCN Guidelines^®). Hepatobiliary cancers. 2024 (Version 2.2024).

22. Singal AG, Llovet JM, Yarchoan M, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922–1965. doi:10.1097/HEP.0000000000000466

23. Yau T, Park J-W, Finn RS, et al. Nivolumab versus sorafenib in advanced hepatocellular carcinoma (CheckMate 459): a randomised, multicentre, open-label, phase 3 trial. Lancet Oncol. 2022;23(1):77–90. doi:10.1016/S1470-2045(21)00604-5

24. Yopp A, Kudo M, Chen M, et al. LBA39 Updated efficacy and safety data from IMbrave050: phase III study of adjuvant atezolizumab (atezo) + bevacizumab (bev) vs active surveillance in patients (pts) with resected or ablated high-risk hepatocellular carcinoma (HCC). Ann Oncol. 2024;35.

25. Chen W, Hu S, Liu Z, et al. Adjuvant anti-PD-1 antibody for hepatocellular carcinoma with high recurrence risks after hepatectomy. Hepatol Int. 2023;17(2):406–416. doi:10.1007/s12072-022-10478-6

26. Yang J, Jiang S, Chen Y, et al. Adjuvant ICIs plus targeted therapies reduce HCC recurrence after hepatectomy in patients with high risk of recurrence. Curr Oncol. 2023;30(2):1708–1719. doi:10.3390/curroncol30020132

27. Su J-Y, Liu SP, Xu XL, et al. Treatment duration of adjuvant immune checkpoint inhibitors in hepatocellular carcinoma patients at high risk of recurrence after resection: a prospective, multicentric cohort study. Liver Cancer. 2024;1–18.

28. Waterhouse DM, Garon EB, Chandler J, et al. Continuous versus 1-year fixed-duration nivolumab in previously treated advanced non-small-cell lung cancer: checkMate 153. J Clin Oncol. 2020;38(33):3863–3873. doi:10.1200/JCO.20.00131

29. Zalcman G, Balmaña J, Bober SL, et al. 972O Nivolumab (Nivo) plus ipilimumab (Ipi) 6-months treatment versus continuation in patients with advanced non-small cell lung cancer (aNSCLC): results of the randomized IFCT-1701 phase III trial. Ann Oncol;2022:33. doi:10.1016/j.annonc.2022.10.004