Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal malignancies worldwide, accounting for a major share of cancer-related mortality (1–3). Although advances in surgical techniques and perioperative management have improved procedural safety, long-term outcomes following curative liver resection remain unsatisfactory. Up to 70% of patients experience tumor recurrence within five years after surgery (4), underscoring the need for more accurate and individualized risk stratification strategies—particularly for predicting early postoperative relapse, where robust prognostic tools are still lacking.

Among histopathological features, microvascular invasion (MVI) has emerged as one of the strongest and most consistent predictors of early recurrence and reduced survival. MVI is histologically defined as the presence of tumor emboli within endothelial-lined vascular spaces in the peritumoral liver parenchyma. Its presence reflects an aggressive tumor phenotype characterized by intrahepatic micrometastasis and vascular dissemination (5). However, MVI cannot be reliably detected preoperatively via current imaging modalities and is typically confirmed only through postoperative pathological examination (6). Consequently, despite its high prognostic value, MVI remains underrepresented in conventional staging systems—highlighting the urgent need to integrate biologically driven markers into recurrence prediction models (7, 8).Even among patients undergoing liver transplantation or early-stage HCC, the presence of MVI is associated with poorer outcomes (9).

Postoperative recurrence in HCC is commonly categorized into two biologically distinct subtypes based on timing and clonal origin: early recurrence (ER), defined as relapse within two years post-surgery, and late recurrence (LR), occurring thereafter. ER is believed to result from intrahepatic micrometastases disseminated before or during surgery, representing a monoclonal expansion of the primary tumor (10, 11). In contrast, LR typically arises via polyclonal de novo tumorigenesis against a background of cirrhosis or chronic inflammation (10). Clinically, ER is associated with more aggressive tumor behavior, limited salvage options, and substantially poorer survival (12). Its temporal proximity to surgery renders ER a clinically actionable window, wherein timely and accurate risk identification may enable early intervention and tailored postoperative surveillance. ER rates in MVI-positive HCC patients have been reported to exceed 76.7%, with a median recurrence-free survival less than 12 months (13). Despite this, no universally accepted risk classification system currently exists for ER in this population (13).

Although widely used in clinical practice, staging systems such as the Barcelona Clinic Liver Cancer (BCLC), tumor-node-metastasis (TNM), China Liver Cancer (CNLC) and other classifications were originally designed to guide treatment allocation and predict overall survival (14–20). However, their ability to stratify ER risk—particularly in patients with MVI—remains limited. These systems are typically built on linear assumptions and fixed variable hierarchies, which inadequately capture the nonlinear and multifactorial biological processes that drive early relapse. Their suboptimal performance in this context has been corroborated by multiple retrospective validations. In response to these limitations, machine learning–based tools have emerged as promising alternatives for individualized risk prediction in oncology. Among these, classification and regression tree (CART) models offer interpretable, rule-based decision frameworks capable of capturing complex interactions and hierarchical variable importance through recursive partitioning (21, 22). Unlike black-box algorithms, CART is transparent and highly adaptable to clinical settings, making it particularly suitable for bedside risk stratification in heterogeneous HCC populations.

In this study, we developed and validated a CART-based prognostic model to stratify early recurrence risk in HCC patients with histologically confirmed MVI following curative hepatectomy. We further compared its predictive performance against conventional staging systems and evaluated its association with recurrence-free survival (RFS) and overall survival (OS). By identifying patients at elevated risk of early relapse, the model aims to inform risk-adapted surveillance protocols, guide adjuvant therapy planning, and ultimately improve outcomes in this biologically aggressive subset of HCC.

This study developed and validated a CART model for predicting ER in patients with HCC and MVI who underwent curative resection. Based on clinicopathological and molecular data from 440 patients, the model integrated eight independent predictors—including HBV-DNA load, tumor size, ES grade, tumor capsule integrity, MVI classification, presence of satellite nodules, Ki-67 index, and CK19 expression—and demonstrated excellent discriminative ability (C-index = 0.773 in the training cohort and 0.764 in the validation cohort) and calibration performance. Compared with conventional staging systems such as BCLC, TNM, and CNLC, the CART model achieved higher AUC values and greater clinical net benefit in predicting ER, RFS, and OS, highlighting its utility for risk stratification in postoperative HCC management.

From a mechanistic perspective, our findings further emphasize the central role of MVI in HCC recurrence. MVI is not only a morphological manifestation of tumor cell invasion into vascular structures but also reflects underlying molecular alterations and reprogramming of the tumor microenvironment (TME) (26). As the primary splitting node in the model, MVI classification (M1 vs. M2) directly illustrates the biological continuum between the extent of invasion and early recurrence risk. High-risk MVI (M2) is often associated with activation of epithelial-mesenchymal transition (EMT), involving downregulation of E-cadherin and upregulation of N-cadherin and vimentin, which enhances tumor cell migration and invasiveness (27, 28). Furthermore, MVI-positive tumors frequently exhibit vasculogenic mimicry and lymphatic infiltration tendencies, providing a structural basis for the survival and regeneration of minimal residual disease after surgery (29, 30). Notably, patients with M2-type MVI often presented with larger tumor burden, higher Ki-67 proliferation index, and poorer histological differentiation, suggesting a more aggressive biological phenotype. These results are consistent with the clinical utility of the MVI grading system proposed by Cong et al. (6) and underscore the importance of standardizing MVI classification in pathological reporting.

The liver maintains a uniquely tolerant immune environment, constantly bathed in diverse foreign antigens due to its role as a tolerogenic organ. However, under specific pathological conditions, this state of tolerance can be broken, leading to pathological inflammation and dysregulated immune responses (31, 32). This dual immune role is closely associated with the development of HCC, MVI, and ER. Chronic HBV infection remains a dominant etiological factor for HCC, particularly in Asia (33). In our cohort, 87.0% of patients were HBV-positive. Elevated HBV-DNA load was independently associated with increased ER risk, consistent with prior studies (11, 34). Persistent viral replication may contribute to microenvironmental changes favoring tumor recurrence. These findings underscore the importance of stringent viral suppression to reduce recurrence and improve survival in HBV-related HCC patients. Tumor size is another critical determinant of prognosis (5, 35). Larger tumors often exhibit features such as microvascular infiltration, an incomplete capsule, and satellite nodules, all of which are associated with early dissemination. Our data confirm that larger tumor is significantly associated with a higher risk of ER, supporting the need for more aggressive postoperative surveillance and consideration of adjuvant therapies in this subgroup. Other pathological factors, including a higher ES grade, incomplete tumor capsule, presence of satellite nodules, and MVI subclassification, further stratify recurrence risk (6, 11, 36). We adopted the MVI grading system proposed by Cong et al. (6) due to its simplicity and clinical applicability. High-risk MVI was associated with more aggressive biological features and poorer outcomes, reinforcing its prognostic relevance.

Regarding molecular markers, both Ki-67 and CK19 also emerged as significant prognosticators. Ki-67, a nuclear protein involved in cell proliferation (37), has been consistently associated with poor outcomes in HCC (38, 39). Using X-tile analysis, we identified an optimal threshold for Ki-67 positivity, and higher expression correlated with unfavorable clinicopathological features and worse survival. CK19, a marker of hepatic progenitor cells (40, 41), was similarly linked to aggressive tumor biology and adverse prognosis, particularly in HBV-associated HCC (42). These results suggest that incorporating molecular biomarkers into predictive models can significantly improve the accuracy of risk stratification and reflect deeper immunobiological mechanisms underlying tumor recurrence, including abnormal immune responses caused by dysregulated antigen presentation mechanisms—observed not only in neoplastic diseases but also in non-neoplastic liver conditions such as autoimmune hepatitis (43, 44). Therefore, the predictive model constructed in this study integrates not only clinical and pathological features but also key immunological dimensions of recurrence biology, providing a more comprehensive basis for individualized prognostic assessment and treatment decision-making.

From a methodological standpoint, the advantage of the CART model lies in its ability to capture complex interactions and nonlinear relationships among predictive variables, making it more aligned with real-world clinical decision-making scenarios. For example, among patients with M1-type MVI, the model further stratifies risk based on tumor size and Ki-67 index, identifying subgroups still at high risk of recurrence—suggesting that even with less severe MVI, combined high proliferative activity and large tumor burden should warrant vigilance for early recurrence. For M2-type MVI patients, CK19 expression and satellite nodule status further refine risk classification, demonstrating the importance of multi-parameter integration in prognostic assessment.

This study develops a predictive model for ER following curative HCC resection, addressing a distinct clinical challenge that differs from initial diagnosis or general surveillance paradigms. While established serum biomarkers (AFP and DCP) remain indispensable for HCC surveillance and diagnosis per international guidelines (2, 3, 45), their post-operative predictive utility is constrained by variable sensitivity/specificity, particularly when values normalize post-resection (46, 47). Our perioperative CART model addresses this gap by complementing—rather than replacing—existing biomarker monitoring and imaging protocols. Designed for immediate surgical or post-operative risk stratification, the algorithm identifies high-risk patients before conventional surveillance detects recurrence, enabling tailored follow-up intensity. High-risk patients could receive intensified monitoring despite unremarkable initial AFP/DCP levels, while low-risk cohorts might adopt reduced surveillance frequency, optimizing resource allocation and minimizing patient distress (48). By augmenting current strategies with this prognostication layer, the model aims to expedite recurrence detection and therapeutic intervention, potentially improving long-term outcomes through personalized surveillance adaptation.

Despite the encouraging findings, this study also has several limitations. First, the retrospective single-center design may introduce selection bias. Although internal validation was conducted, external validation across multi-center cohorts involving diverse etiologies, such as non-HBV-related HCC, is needed to verify the model’s generalizability. Second, while the CART model incorporated several key biological predictors, it did not include emerging dynamic biomarkers like ctDNA or quantitative radiomic features, which may further enhance the accuracy of risk stratification. Subsequent studies could integrate multi-omics data to refine the model and strengthen its predictive capability. Moreover, the study only enrolled patients who underwent curative resection, which may introduce selection bias and restrict the applicability of the conclusions to surgical candidates with early-stage disease. Therefore, the results should not be generalized to patients with advanced HCC or those ineligible for surgical intervention. In addition, due to the retrospective nature of the study, post-recurrence treatment strategies were highly individualized and inconsistently documented, hindering systematic collection and analysis of postoperative management data. As a result, treatment-related variables were excluded from covariate adjustment and subgroup analyses. Future studies should account for heterogeneity in adjuvant therapies as a crucial factor to allow a more comprehensive evaluation of recurrence risk. Finally, although CART offers clinical interpretability, future work may explore hybrid models combining interpretable tree-based frameworks with more complex machine learning approaches to optimize both transparency and predictive accuracy.

The development and validation of an MVI-based CART strategy has provided a valuable tool for predicting ER risk, RFS, and OS in patients with HCC. This strategy may assist clinicians in tailoring postoperative surveillance programs and therapeutic interventions for HCC patients with MVI following curative hepatectomy.