Deng QY, Leng HQ, Wang TC, Hu C. Establishment and validation of an intratumoral fibrosis-based nomogram for predicting aggressive recurrence after liver resection in hepatocellular carcinoma. World J Gastrointest Surg 2026; 18(5): 116135 [DOI: 10.4240/wjgs.v18.i5.116135]
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Chao Hu, Department of Radiology, The Second Xiangya Hospital of Central South University, No. 139 Renmin Middle Road, Changsha 410011, Hunan Province, China. huchao0101@csu.edu.cn
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Gastroenterology & Hepatology
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Deng QY, Leng HQ, Wang TC, Hu C. Establishment and validation of an intratumoral fibrosis-based nomogram for predicting aggressive recurrence after liver resection in hepatocellular carcinoma. World J Gastrointest Surg 2026; 18(5): 116135 [DOI: 10.4240/wjgs.v18.i5.116135]
World J Gastrointest Surg. May 27, 2026; 18(5): 116135 Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.116135
Establishment and validation of an intratumoral fibrosis-based nomogram for predicting aggressive recurrence after liver resection in hepatocellular carcinoma
Qi-Yuan Deng, Hao-Qun Leng, Tian-Cheng Wang, Chao Hu, Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China
Author contributions: Deng QY and Leng HQ wrote the manuscript, reviewed and analyzed the patients’ clinical data and contributed equally to this article as co-first authors of this manuscript; Deng QY and Wang TC reviewed the histological slides; Deng QY, Leng HQ, and Wang TC collected and provided the patients’ clinical information; Hu C edited and revised the manuscript; Hu C was the main contributor and is the guarantor of this work; All authors read and approved the final manuscript.
Supported by the Hunan Provincial Natural Science Youth Fund Project, No. 2023JJ40824.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the Second Xiangya Hospital of Central South University, approval No. Z0633-01.
Informed consent statement: The informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The patient-derived datasets generated during this study are not publicly available due to patient privacy restrictions but are accessible from the corresponding author upon reasonable request. The underlying code for this study is available in a public GitHub repository (https://github.com).
Corresponding author: Chao Hu, Department of Radiology, The Second Xiangya Hospital of Central South University, No. 139 Renmin Middle Road, Changsha 410011, Hunan Province, China. huchao0101@csu.edu.cn
Received: November 4, 2025 Revised: January 10, 2026 Accepted: March 4, 2026 Published online: May 27, 2026 Processing time: 205 Days and 6.3 Hours
Abstract
BACKGROUND
Aggressive recurrence after liver resection (LRE) in hepatocellular carcinoma (HCC) is associated with unfavorable clinical outcomes. Intratumoral fibrosis (ITF) is mechanistically associated with aggressive recurrence, promoting immunosuppression, angiogenesis, and matrix stiffening, thereby supporting its potential as a predictive biomarker.
AIM
To develop and validate a competing risk nomogram incorporating ITF to predict aggressive HCC recurrence after LRE.
METHODS
This study included untreated patients with solitary HCC tumors from two retrospective datasets spanning August 2013 to November 2023. Patients were stratified into high-ITF (score ≥ 2) and low-ITF (score < 2) groups based on a semiquantitative Sirius red staining scale of 0-3. The cumulative incidence of aggressive recurrence, recurrence-free survival, and overall survival were compared between the groups. Variables associated with aggressive recurrence were identified using Fine-Gray regression analysis and incorporated into a predictive nomogram. The model performance was assessed in terms of calibration, discrimination, and clinical utility of the model.
RESULTS
A significant correlation was identified between ITF and aggressive recurrence. Multivariate Fine-Gray analysis revealed that preoperative neutrophil count, tumor size, ITF, and microvascular invasion were independent predictors of aggressive recurrence. A competing risk nomogram was developed to estimate the risk of aggressive recurrence after LRE in patients with HCC. The predictive nomogram demonstrated strong discrimination and calibration with concordance indices of 0.799 [95% confidence interval (CI): 0.770-0.830] and 0.779 (95%CI: 0.703-0.848), and areas under the receiver operating characteristic curves at 2 years of 0.850 (95%CI: 0.816-0.884) and 0.842 (95%CI: 0.765-0.919) in the training and validation cohorts, respectively.
CONCLUSION
ITF is a predictive marker of aggressive recurrence after LRE in patients with HCC. A nomogram integrating ITF with other clinical factors demonstrated robust efficacy in predicting aggressive recurrence.
Core Tip: Accurate risk stratification for aggressive recurrence is essential in patients with hepatocellular carcinoma undergoing liver resection. In this multicenter study we identified intratumoral fibrosis as a novel independent histopathological predictor of aggressive recurrence. We developed a competing risk nomogram by integrating intratumoral fibrosis with key clinical variables to provide individualized risk estimation. The model demonstrated robust performance across independent cohorts, offering a validated tool for improved postoperative prognostic stratification.
Citation: Deng QY, Leng HQ, Wang TC, Hu C. Establishment and validation of an intratumoral fibrosis-based nomogram for predicting aggressive recurrence after liver resection in hepatocellular carcinoma. World J Gastrointest Surg 2026; 18(5): 116135
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer worldwide and is the third-leading cause of cancer-related mortality globally[1-3]. Surgical intervention, particularly liver resection (LRE), offers potentially curative outcomes for patients with early-stage HCC. However, high recurrence rates significantly undermine long-term survival. Clinical evidence indicates that approximately 50%-70% of patients experience disease recurrence within 5 years of LRE, highlighting the ongoing challenge of achieving sustained disease control[4-6]. Observational studies have revealed that over 30% of recurrent cases exhibit aggressive disease phenotypes characterized by tumor biological characteristics that exceed the Milan criteria[7]. These aggressive recurrence patterns often manifest as multifocal, rapidly progressive, or extrahepatic lesions that preclude further curative-intent treatments such as repeat resection or liver transplantation[8,9]. Most current prognostic assessment systems are based on measures of tumor burden, including tumor size and number[10,11]. However, their predictive accuracy is limited, and they do not effectively identify patients at risk of aggressive recurrence. Therefore, there is an urgent clinical need for novel risk stratification markers to improve the identification of individuals at high risk of aggressive recurrence.
During cancer progression intratumoral fibrosis (ITF) enhances the aggressive nature of tumors by affecting mechanisms such as tumor initiation, proliferation, angiogenesis, and migration[12]. ITF has been found to significantly worsen the prognosis of various solid tumors, including renal cell carcinoma, breast cancer, and colorectal cancer[13-15]. This pathological process arises from a chronic wound-healing response triggered by persistent cancer-induced tissue damage within the tumor[13]. This is primarily mediated by cancer-associated fibroblasts (CAFs) in the tumor microenvironment[16]. These fibroblasts are activated through the transforming growth factor-beta (TGF-β)/Sma-related and Mad-related protein signaling pathways, leading to matrix stiffening via increased collagen cross-linking mediated by cross-linking enzymes and the formation of a rigid extracellular matrix (ECM)[17,18]. The resulting dense ECM not only physically obstructs immune cell infiltration but also facilitates the proliferation of vascular endothelial cells, contributing to tumor immunosuppression and angiogenesis[19-22]. This microenvironment ultimately creates conditions conducive to tumor recurrence. Although HCC generally demonstrates a low level of fibrosis, the presence of ITF is correlated with more aggressive malignant characteristics, including early recurrence, multifocal growth, and vascular invasion[23].
Consequently, we hypothesized that ITF could function as a significant prognostic marker of aggressive recurrence after LRE. This study aimed to assess the prognostic utility of ITF and its predictive capacity for aggressive recurrence in patients with HCC who underwent LRE. The ultimate objective is to develop an ITF-based predictive model to effectively identify patients at high risk.
MATERIALS AND METHODS
Study population
The study population comprised a training cohort of 855 patients from the Second Xiangya Hospital (October 2013 to December 2022) and an external validation cohort of 148 patients from the Affiliated Hospital of Guizhou Medical University and Guizhou Cancer Hospital (November 2014 to July 2021), all with a single, untreated HCC lesion who underwent LRE.
The inclusion criteria were as follows: (1) Patients with preserved liver function; (2) An Eastern Cooperative Oncology Group performance status of 0; (3) No macrovascular invasion or extrahepatic metastasis; and (4) A single HCC lesion. The exclusion criteria were as follows: (1) Patients who received postoperative adjuvant therapy; (2) Underwent non-curative resection (R1 or R2 margins); (3) Missing contrast-enhanced CT imaging within 1 month before LRE; (4) Missing laboratory information within 1 week before LRE; (5) Missing follow-up data; and (6) Histologically confirmed combined hepatocellular-cholangiocarcinoma. The flowchart of the study design is presented in Figure 1.
The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the institutional review boards of all participating institutions. The requirement for informed consent was waived for the retrospective cohort study due to its study design.
Clinical data collection
Clinical data, including age, sex, Barcelona Clinic Liver Cancer stage, liver cirrhosis status, and etiology of underlying liver disease, were collected. Similarly, laboratory data, encompassing neutrophil, lymphocyte, monocyte, and platelet counts as well as serum albumin, total bilirubin, alanine aminotransferase, aspartate aminotransferase, international normalized ratio, creatinine, and alpha-fetoprotein levels, were collected. Patients were initially observed for 2-3 months following LRE and were followed up at least every 6 months.
Histological examination
Two liver pathologists with 11 and 10 years of experience in liver pathology independently performed histological analyses and reached a consensus in cases of disagreement. HCC was diagnosed according to the 2019 World Health Organization classification. The ITF grade was evaluated via Sirius red staining using a semiquantitative scoring scale from 0 to 3, corresponding to absent, mild, moderate, or severe ITF, respectively; a score of ≥ 2 indicated high-grade ITF. The representative ITF expression levels are displayed in Figure 2.
Figure 2 Sirius red staining of intratumoral fibrosis in hepatocellular carcinoma.
A: Representative photomicrograph showing low intratumoral fibrosis expression; B: Representative photomicrograph showing high intratumoral fibrosis expression.
Aggressive recurrence evaluation and follow-up
Aggressive recurrence was defined as recurrent tumors exceeding the Milan criteria at first recurrence. Postoperative follow-up included serum alpha-fetoprotein and contrast-enhanced CT or magnetic resonance imaging at 1 month, every 3 months for 2 years, and every 6 months thereafter until May 2025. Recurrence management was based on tumor location, liver function, and patient status. The primary endpoint was the time to aggressive recurrence (TTAR), and the secondary endpoints included recurrence-free survival (RFS, any type of recurrence) and overall survival (OS). TTAR was defined as the time from LRE to aggressive recurrence, and patients without relapse were excluded. RFS was defined as the time from LRE to recurrence or death from any cause, and patients without relapse or who did not die were excluded. OS was defined as the time from LRE until death from any cause, and patients who did not die were excluded.
Prognostic model derivation and validation
Predictor variables were first selected using univariable Fine-Gray proportional subdistribution hazard models. Variables with a significance level of P < 0.05 were included in the multivariable analysis. The final model was constructed using a backward stepwise selection process based on the Akaike information criterion and was internally validated using a five-fold cross-validation. This model was then presented as a nomogram to predict TTAR. The analysis was performed within a competing risk framework, treating non-aggressive recurrence and death as competing events. The discrimination of the model was assessed using the concordance index and time-dependent receiver operating characteristic curves. Calibration curves were plotted to evaluate the agreement between the predicted and observed probabilities. Finally, decision curve analysis (DCA) was conducted to quantify the clinical net benefits of the nomogram across a range of threshold probabilities.
Statistical analysis
Categorical variables are presented as n (%) and were compared using the χ2 or Fisher’s exact test as appropriate. Continuous variables are expressed as medians (interquartile range) and were analyzed using Student’s t-test or Mann-Whitney U test.
RFS and OS were estimated using the Kaplan-Meier method and compared using the log-rank test. TTAR, accounting for non-aggressive recurrence and death as competing events, was analyzed using cumulative incidence functions and compared using Gray’s test. Prognostic factors were identified using Fine and Gray's proportional subdistribution hazard model for competing risks. A prognostic nomogram was constructed and validated using the R packages “cmprsk,” “rms,” and “mstate.”
Inverse probability of treatment weighting
To minimize potential confounding biases and improve the comparability between patients with high and low ITF, we performed an adjusted analysis using inverse probability of treatment weighting (IPTW). The propensity score (PS), defined as the conditional probability of having a high ITF given the observed baseline covariables, was estimated for each patient using a multivariable logistic regression model. The inverse probability weight was calculated as W = 1/PS for the high-ITF group and W = 1/(1-PS) for the low-ITF group. To enhance the robustness of the estimates, the weights were stabilized and truncated at the 1st and 99th percentiles. The balance of covariables between groups after weighting was assessed using the standardized mean difference with a standardized mean difference < 0.2 indicating an adequate balance. Subsequent comparisons of RFS and OS between ITF groups were performed using weighted Kaplan-Meier curves with the weighted log-rank test and weighted Cox proportional hazards models.
All statistical analyses were performed using R software (version 4.0.2; R Foundation for Statistical Computing) and the Statistical Package for Social Sciences (version 26.0; IBM). A two-sided P value < 0.05 was considered statistically significant.
RESULTS
Baseline patient characteristics
A total of 1197 patients were included in this study of whom 194 were excluded (Table 1). A detailed overview is displayed in Figure 1. The final study population comprised 1003 patients (842 males and 165 females with a mean age of 53.7 ± 11.9 years). In the training cohort 603 and 252 patients were histologically confirmed to have low-grade and high-grade ITF, respectively. The corresponding numbers in the validation cohort were 106 and 42, respectively. The training and validation cohorts had a median follow-up duration of 44.9 months (interquartile range: 26.0-67.2) with median RFS and OS of 30.2 months [95% confidence interval (CI): 25.2-35.2 months] and 70.7 months (95%CI: 59.2-82.3 months), respectively. Aggressive recurrence occurred in 22.7% (228/1003) of patients with approximately 80.7% (184/228) occurring within 2 years of LRE.
Table 1 Baseline clinical and pathologic characteristics of patients in training and validation cohorts.
In the training cohort the cumulative aggressive recurrence rate in the high-ITF group was significantly higher than that in the low-ITF group (high-ITF, 47.6% vs low-ITF, 11.8%; P < 0.001) (Figure 3A). Consistently, in the validation cohort, the high-ITF group also exhibited a significantly higher rate compared with the low-ITF group (47.6% vs 16.0%; P < 0.001) (Figure 3B).
Figure 3 Cumulative incidence of aggressive recurrence stratified by intratumoral fibrosis expression level.
A: Training cohort; B: Validation cohort. Comparisons between groups were performed using a two-sided Gray’s test. SHR: Subdistribution hazard ratio; ITF: Intratumoral fibrosis.
Survival analysis indicated that patients with high ITF experienced worse RFS [16.0 months (95%CI: 11.7-25.1 months) vs 41.5 months (95%CI: 31.1-59.8; P < 0.001)] and OS [47.1 months (95%CI: 35.9-60.5 months) vs 97.9 months 95%CI: 72.2-not reached (NR); P < 0.001] (Figure 4A-C) compared with those with low ITF. To mitigate potential selection bias due to confounding variables, this study employed IPTW. IPTW analysis revealed no significant differences in the baseline characteristics between the two groups (Table 2). RFS [20.8 months, (95%CI: 14.7-33.4) vs 35.8 months, (95%CI: 28.3-50.7); P < 0.001] and OS [52.6 months, (95%CI: 41.1-72.0) vs 92.4 months, (95%CI: 70.2-NR); P < 0.001] were also significantly worse in the high-ITF group than in the low-ITF group (Figure 4B-D).
Figure 4 Survival analysis of patients with low vs high intratumoral fibrosis before and after inverse probability of treatment weighting.
Comparisons between groups were performed using the log-rank test. A: Recurrence-free survival (RFS) before inverse probability of treatment weighting (IPTW) in training cohort; B: RFS after IPTW in training cohort; C: Overall survival (OS) before IPTW in training cohort; D: OS after IPTW in training cohort; E: RFS before IPTW in validation cohort; F: RFS after IPTW in validation cohort; G: OS before IPTW in validation cohort; H: OS after IPTW in validation cohort. HR: Hazard ratio; IPTW: Inverse probability of treatment weighting: ITF: Intratumoral fibrosis.
Table 2 Baseline characteristics of the low and high intratumoral fibrosis group before and after matching in training cohort.
Similar results were observed between the groups in the validation cohort. Survival analysis indicated that patients with high ITF experienced worse RFS [20.5 months, (95%CI: 11.4-27.8 months) vs 43.2 months, (95%CI: 27.5-72.3); P < 0.001] and OS [39.0 months, (95%CI: 25.2-80.7 months) vs 82.1 months, (95%CI: 58.8-NR); P = 0.008] (Figure 4E-G) compared with those with low ITF. To mitigate potential selection bias due to confounding variables, this study employed IPTW. IPTW analysis revealed no significant differences in baseline characteristics between the two groups (Table 3). RFS [21.0 months, (95%CI: 10.0-34.8) vs 45.5 months, (95%CI: 28.5-NR); P < 0.001] and OS [43.3 months, (95%CI: 25.2-NR) vs 82.1 months, (95%CI: 58.8-NR); P = 0.025] were also significantly worse in the high-ITF group than in the low-ITF group (Figure 4F-H).
Table 3 Baseline characteristics of the low and high intratumoral fibrosis group before and after matching in validation cohort.
Establishment and validation of the competing risk model
Initially, 21 variables, including various clinical indicators and pathological characteristics, were included in the analysis. Ten variables associated with aggressive recurrence were identified using univariable Fine-Gray analysis. Four variables were selected using a backward stepwise regression approach based on the Akaike information criterion and were incorporated into the multivariable Fine-Gray model. These included neutrophil count [subdistribution hazard ratio (SHR) = 1.090; 95%CI: 1.000-1.180; P = 0.046], microvascular invasion (MVI) (SHR = 3.200; 95%CI: 2.360-4.350; P < 0.001), ITF (SHR = 4.750; 95%CI: 3.530-6.370; P < 0.001), and tumor diameter (SHR = 1.010; 95%CI: 1.010-1.020; P < 0.001) (Table 4). Validation cohort Fine-Gray analysis results are summarized in Supplementary Table 1. Finally, a nomogram was constructed based on these four variables (Figure 5).
The competing risk model demonstrated excellent predictive performance across all datasets with C-indices of 0.799 (95%CI: 0.770-0.828) and 0.779 (95%CI: 0.703-0.848) in the training and validation cohorts, respectively (Supplementary Table 2). Given that most aggressive recurrences occurred within 2 years in our study, we evaluated the time-dependent area under the curve (AUC) values at both the 1-year and 2-year time points along with the calibration curves and DCA. The competing risk model demonstrated excellent discrimination with 1-year time-dependent AUC values of 0.850 (95%CI: 0.812-0.887) and 0.836 (95%CI: 0.742-0.931) in the training and validation cohorts, respectively. Similarly, the 2-year time-dependent AUC values were 0.850 (95%CI: 0.816-0.884) and 0.842 (95%CI: 0.765-0.919) in the respective cohorts (Figure 6A and B). Calibration was robust across both time points as evidenced by calibration curves and Brier scores of 9.3% (95%CI: 8.0%-10.6%) and 9.3% (95%CI: 6.0%-12.6%) at 1 year, and 10.7% (95%CI: 9.3%-12.2%) and 11.9% (95%CI: 8.2%-15.6%) at 2 years in the training and validation cohorts, respectively (Figure 6C and D). DCA further confirmed the clinical utility of the competing risk model, depicting net benefits across a range of threshold probabilities at both 1-year and 2-year time points (Figure 6E and F).
Figure 6 Performance evaluation of the predictive nomogram.
A: Time-dependent receiver operating characteristic curve for 1-year and 2-year aggressive recurrence in the training cohort; B: Time-dependent receiver operating characteristic curve for 1-year and 2-year aggressive recurrence in the validation cohort; C: Calibration plot comparing predicted vs observed probabilities at 1 year and 2 years in the training cohort; D: Calibration plot at 1 year and 2 years in the validation cohort; E: The 1-year and 2-year decision curve analysis curves of the training cohort; F: The 1-year and 2-year decision curve analysis curves of the validation cohort. AUC: Area under the curve.
DISCUSSION
Patients with HCC undergoing LRE are at a heightened risk of experiencing aggressive recurrence, posing a significant clinical challenge and adversely affecting their prognosis[9]. Research indicates that over 30% of these patients develop aggressive recurrence following surgery[7]. This type of recurrence is highly invasive and frequently exceeds the Milan criteria, manifesting as multifocal, rapidly progressing, or distant metastatic patterns. Once aggressive recurrence occurs, patients often lose opportunities for further curative interventions, such as repeat LRE or liver transplantation, resulting in markedly limited therapeutic options[8,9]. Consequently, the early identification of high-risk patients and the implementation of proactive interventions are clinically essential for enhancing long-term postoperative outcomes in patients with HCC.
In this study the occurrence of competing risks, notably non-aggressive recurrence and death, represents a clinically significant reality that may preclude the observation of the primary event of interest (aggressive recurrence). In such settings conventional survival analysis methods, such as the standard Cox proportional hazards model, may produce biased estimates by treating competing events as independent censored observations, potentially overestimating the cumulative incidence of the primary event[24,25]. To address this methodological challenge and obtain unbiased estimates of the direct effect of predictors on the cause-specific hazard, we employed the Fine-Gray proportional subdistribution hazard model[26]. This model is specifically designed for competing risk data as it allows for the direct modeling of the cumulative incidence function, which is more clinically interpretable for risk prediction in the presence of competing events[27]. The application of the Fine-Gray model is well established in oncology research, including studies on HCC recurrence[28], ensuring that our nomogram provides a more accurate and clinically relevant assessment of the risk of aggressive recurrence.
ITF, characterized by the abnormal accumulation of collagen matrix synthesized by CAFs[12], plays a crucial role in facilitating aggressive tumor behavior through multiple interconnected mechanisms. The dense collagen network acts as a physical barrier, impeding the infiltration of cytotoxic T lymphocytes and natural killer cells, concurrently promoting the recruitment of immunosuppressive cell populations, such as regulatory T cells and myeloid-derived suppressor cells, via cytokine signaling pathways mediated by C-X-C motif chemokine ligand 12, interleukin-6, and prostaglandin E2. This process contributes to the formation of an immunosuppressive tumor microenvironment, which facilitates immune evasion and confers resistance to immune checkpoint inhibitors[19,29,30]. Additionally, CAFs and ECM components provide pro-survival signals to cancer cells and secrete TGF-β, which induces epithelial-mesenchymal transition (EMT), thereby enhancing the invasive capacity and metastatic potential of tumors[31]. To date few studies have explored the association between ITF and HCC clinical outcomes. A recent study demonstrated that elevated ITF levels in HCC correlated with significant activation of Wnt/β-catenin and TGF-β signaling pathways. This activation facilitates intratumoral ECM remodeling, angiogenesis, and immunosuppression, thereby promoting tumor progression and leading to unfavorable clinical outcomes[23].
In this study we classified patients into high-ITF and low-ITF groups based on collagen density assessed via Sirius Red staining. To address potential confounding arising from the non-randomized study design, we applied IPTW to construct a balanced pseudo-population, thereby enabling more reliable effect estimation. Consistently, both before and after IPTW adjustment, the high-ITF group was associated with significantly poorer RFS and OS. Moreover, competing risk analysis using the Fine-Gray model confirmed a significantly higher cumulative incidence of aggressive recurrence in the high-ITF group, supporting ITF as an independent predictive factor for aggressive recurrence.
MVI, characterized by the infiltration of tumor cells into the microvessels surrounding the tumor, is a pivotal pathological feature indicative of aggressive tumor biology and is associated with an increased risk of postoperative recurrence and poor prognosis in patients with HCC[32]. This aggressive phenotype is orchestrated by multiple interconnected mechanisms with EMT playing a central role[33,34]. During EMT cancer cells downregulate epithelial markers, such as E-cadherin, and upregulate mesenchymal markers, such as N-cadherin and vimentin, thereby acquiring enhanced migratory and invasive capabilities[33]. Furthermore, MVI-positive tumors often exhibit elevated expression of programmed death-ligand 1, which interacts with programmed death-1 on T cells, leading to the suppression of T cell activation and promotion of apoptosis. This interaction fosters an “immune-excluded” tumor microenvironment that allows circulating tumor cells to evade immune surveillance and facilitates metastatic spread[34]. Concurrently, aberrant angiogenesis supports primary tumor growth and provides further pathways for intravasation, and fibronectin 1-mediated remodeling of the ECM further enhances cellular motility and invasiveness[35,36]. Collectively, these interrelated processes drive aggressive HCC progression associated with MVI.
Elevated neutrophil counts indicate a systemic state characterized by chronic inflammation and immunosuppression. Within this framework neutrophils facilitate the aggressive recurrence of HCC following LRE through a multiple interconnected mechanisms. A pivotal pathway involves the formation of neutrophil extracellular traps (NETs), which directly contribute to tumor progression by initiating signaling cascades such as high-mobility group box 1/toll-like receptor 4/cyclooxygenase-2[37,38], thereby enhancing cancer cell survival and motility. NETs further induce EMT, increasing tumor invasiveness, and trap circulating tumor cells within the liver microvasculature[39]. By interacting with the coiled-coil domain containing 25 receptor on tumor cells, NETs activate the Ras-related C3 botulinum toxin substrate 1 and cell division control protein 42 homolog signaling axis, promoting hepatic colonization and establishing a foundation for metastatic lesion development[40]. Simultaneously, neutrophils exacerbate immunosuppression by impairing the functions of differentiation 8-positive T cells and natural killer cells, promoting the differentiation of regulatory T cells and stimulating CAFs[41-43]. These processes in conjunction with the activation of the coagulation system result in the formation of protective microthrombi, which shield residual tumor cells within a supportive niche[44]. The postoperative hepatic microenvironment further amplifies these effects. Surgical trauma enhances the release of NETs and intensifies systemic inflammatory responses, resulting in a synergistic interaction between neutrophil-driven immunosuppression, tumor promotion, and metastatic initiation[39,43,44]. This interplay ultimately leads to aggressive HCC recurrence after resection.
A larger tumor size is indicative of a more aggressive tumor biology, characterized by intratumoral heterogeneity and a prometastatic microenvironment, which includes hypoxia, MVI, and immunosuppression[45,46]. This environment facilitates the early dissemination of highly malignant circulating tumor cells that colonize distant organs, particularly under conditions of postoperative inflammation, ultimately leading to aggressive recurrence[47,48]. Consistent with prior research, MVI, elevated neutrophil count, and tumor size were identified as independent predictors of aggressive recurrence after LRE. In this study we developed a competing risk model nomogram that integrates ITF, MVI, neutrophil count, and tumor size to predict aggressive recurrence in patients with HCC. The model exhibited strong predictive performance with a concordance index of 0.800 (95%CI: 0.772-0.829) in the training cohort and 0.783 (95%CI: 0.704-0.852) in the external validation cohort, demonstrating robust generalizability across patient populations.
Nonetheless, it is imperative to acknowledge the limitations of this study. First, the retrospective design may introduce potential selection bias, highlighting the need for prospective multicenter validation to definitively establish the utility of ITF in risk stratification. Furthermore, although the ITF-based nomogram exhibited strong performance, its applicability in real-time clinical settings requires further prospective validation. Second, although our study provides preliminary evidence of an association between ITF and aggressive recurrence post-hepatectomy for HCC, the underlying biological mechanisms remain to be demonstrated. Finally, this study was conducted in a region with endemic hepatitis B, whereas hepatitis B is not the predominant cause of HCC in Europe or North America.
CONCLUSION
This study demonstrated that ITF is a predictive marker of aggressive recurrence following LRE in patients with HCC. Additionally, the nomogram that integrates ITF with clinical and pathological variables exhibited robust performance in predicting aggressive recurrence.
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