Abstract
Background
Fear of cancer recurrence (FCR) is a prevalent and clinically significant psychological burden among cancer survivors. Its determinants and risk stratification in patients with primary liver cancer remain insufficiently characterized.
Objective
To assess FCR prevalence, identify independently associated factors, and develop a nomogram-based risk prediction model in patients with primary liver cancer.
Materials and Methods
This single-center, cross-sectional study enrolled 175 consecutively recruited patients with primary liver cancer. FCR was measured using the Fear of Cancer Recurrence Inventory-Short Form (FCRI-SF; cut-off ≥27). Sociodemographic, clinical, and psychosocial variables were collected via standardized instruments. Candidate variables significant in univariate analysis were entered into multivariable logistic regression; only variables retaining independent significance were incorporated into the nomogram. Internal validation was performed using bootstrap resampling (1000 iterations). Model performance was assessed by discrimination (C-index), calibration (Hosmer-Lemeshow test), and clinical utility (decision curve analysis).
Results
High FCR (FCRI-SF ≥27) was identified in 96 of 175 patients (54.86%). Female sex and advanced tumor stage were independently associated with higher concurrent FCR likelihood. Moderate household income, absence of hepatitis B virus (HBV) infection, longer disease duration, and higher social support scores were independently associated with lower concurrent FCR likelihood. The model demonstrated acceptable discrimination (C-index = 0.782; bootstrap-corrected C-index = 0.769) and good calibration (Hosmer-Lemeshow P = 0.782). Decision curve analysis indicated net clinical benefit across a clinically relevant threshold probability range.
Conclusion
FCR is highly prevalent in patients with primary liver cancer and is shaped by both clinical and psychosocial factors. The nomogram developed in this study demonstrates promising internal performance and may support individualized FCR risk stratification following external validation in independent, multi-center cohorts.
Keywords: primary liver cancer, fear of cancer recurrence, risk stratification, nomogram, psychosocial determinants, supportive oncology
Introduction
Primary liver cancer continues to impose a substantial global health burden and remains a leading cause of cancer-related mortality.1 Although therapeutic strategies-including surgical resection, locoregional interventions, and systemic treatments-have advanced considerably in recent years, durable disease control remains elusive for many patients.2 Recurrence is common, and clinical trajectories are often unpredictable.3 As a result, patients are not only confronted with biological disease progression but also with a persistent sense of uncertainty that extends throughout the course of care.4
Among the psychological challenges faced by patients with cancer, fear of cancer recurrence (FCR) has gained increasing attention. FCR refers to ongoing concerns that the disease may return or worsen, often accompanied by heightened sensitivity to physical symptoms and future-oriented uncertainty.5 Rather than representing a transient emotional response, FCR tends to persist over time and can influence multiple aspects of patient behavior.6 Prior research has shown that elevated FCR is associated with increased psychological distress, excessive monitoring behaviors, reduced adherence to treatment recommendations, and diminished quality of life.7
Despite the growing body of literature on FCR, most existing evidence has been derived from studies in breast or lung cancer. In contrast, the experience of FCR in primary liver cancer has received relatively limited attention.8 This is noteworthy given the distinct clinical context of liver cancer. A recent qualitative study by Jo and Bae specifically examined FCR trajectories among patients with liver cancer, revealing that many accepted recurrence as an unavoidable possibility yet continued to experience intense anxiety and uncertainty that permeated their daily lives and social relationships, underscoring the distinctive and persistent nature of FCR in this population.4 Similarly, Li et al demonstrated that fear of progression-a construct closely overlapping with FCR-exerted a significant mediating role in the relationship between social support and psychosocial adjustment among postoperative primary hepatocellular carcinoma patients in China, highlighting the clinical importance of addressing this psychological burden within a liver cancer-specific framework.9 Many patients have underlying chronic liver disease, most commonly hepatitis B virus infection, and are exposed to complex and often repeated treatment strategies. At the same time, the risk of recurrence remains high, and disease progression can be difficult to anticipate. These characteristics may intensify patients’ perception of vulnerability and contribute to a more persistent or pronounced form of recurrence-related fear.10
From a theoretical standpoint, FCR is increasingly viewed as a multidimensional phenomenon shaped by both cognitive appraisal and biological processes.11 Uncertainty regarding disease status and prognosis plays a central role in sustaining fear, as described in models of illness-related uncertainty.12 In parallel, theoretical frameworks propose that chronic stress associated with cancer may engage neuroendocrine and immune pathways; it has been hypothesized that dysregulation of the hypothalamic-pituitary-adrenal axis and the activation of inflammatory signaling may contribute to the maintenance of anxiety-related states, although these proposed mechanisms remain incompletely characterized. In the context of liver cancer, where chronic viral infection and immune activation are common, these hypothetical biological processes could theoretically interact with psychological mechanisms, potentially reinforcing vigilance and perceived threat.13 It must be noted, however, that these pathways were not directly examined in the present study. The discussion of biological mechanisms above is presented as a theoretical framework for contextualizing the observed associations rather than as an explanatory account of the study’s findings; no causal or mechanistic conclusions should be drawn from the cross-sectional data reported here, and the biological account above represents one of several possible theoretical frameworks rather than an established explanation.
Although these conceptual advances have improved understanding of FCR, their translation into clinical practice remains limited. Most studies in liver cancer populations have focused on prevalence estimates or isolated associations with individual factors. Such approaches, while informative, do not provide a framework for identifying patients at elevated risk in a clinically actionable manner.14 As a consequence, opportunities for early intervention and personalized supportive care may be missed. Crucially, despite these descriptive advances, no validated prediction tool integrating both clinical and psychosocial determinants has been developed to support individualized FCR risk stratification in primary liver cancer, a critical gap between existing descriptive knowledge and the clinically actionable risk assessment tools required to guide targeted psychological intervention.
At the same time, oncology has increasingly moved toward individualized risk assessment across multiple domains.15 Nomogram-based models have become widely used tools for integrating diverse predictors into a quantitative framework that supports clinical decision-making.16 While these models have traditionally been applied to survival outcomes or treatment response, their potential for evaluating psychological outcomes has not been fully explored.17 Applying this approach to FCR may offer a practical means of bridging the gap between risk identification and intervention.18
Addressing FCR is not only relevant for improving patient well-being but also has broader implications for healthcare delivery.19 Elevated FCR has been associated with increased healthcare utilization, including more frequent follow-up visits and diagnostic testing, as well as variability in adherence to recommended surveillance strategies. Identifying individuals at higher risk may therefore facilitate more efficient allocation of supportive care resources and improve the overall quality of cancer care.20
In light of these considerations, the present study was undertaken to examine FCR in patients with primary liver cancer from both clinical and psychosocial perspectives. Specifically, we aimed to characterize the prevalence of FCR, identify its associated factors, and develop a nomogram-based model for individualized risk prediction. By integrating multiple dimensions of patient experience into a single analytical framework, this study seeks to provide a clinically applicable tool that supports early identification of high-risk individuals and informs targeted psychological intervention strategies.21
Materials and Methods
Study Design and Reporting
This investigation was conducted as a single-center cross-sectional study in a tertiary cancer hospital. The study was designed and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to enhance transparency and reproducibility.
Participants
Patients diagnosed with primary liver cancer and hospitalized between January and December 2024 were considered for inclusion. Participants were recruited consecutively during this period.
Eligibility criteria included: (i) confirmed diagnosis of primary liver cancer based on current clinical standards; (ii) age of 18 years or older; (iii) awareness of their diagnosis; and (iv) the ability to understand and complete the questionnaires independently. Patients were excluded if they presented with severe psychiatric conditions, cognitive impairment, multi-organ failure, or coexisting malignancies.
The required sample size was estimated based on the events-per-variable (EPV) criterion, which recommends a minimum of 10 outcome events per predictor variable in logistic regression models. With 9 candidate predictor variables planned for multivariable analysis — encompassing sex, monthly household income, HBV status, disease duration, tumor stage, social support score, anxiety score, depression score, and education level, a minimum of 90 high-FCR outcome events was required. The present study observed 96 patients meeting the high-FCR criterion, satisfying this minimum threshold. Accounting for an anticipated 5–10% rate of incomplete questionnaire responses or ineligible entries identified during data review, a total enrollment target of 175–185 consecutive participants was established. Ultimately, 175 patients were included in the final analysis.
The study protocol received approval from Shandong Cancer Hospital and Institute ethics committee (SDTHEC202410011) and written informed consent was obtained from all participants.
Data Collection
Data were collected during hospitalization using structured questionnaires administered by two investigators who had received standardized training prior to the study. Assessments were conducted face-to-face, and neutral clarification was provided when necessary to ensure consistent understanding of questionnaire items, without influencing participants’ responses. All participants completed the questionnaires independently. Completed forms were reviewed immediately to identify missing or inconsistent entries, which were addressed at the point of collection. Data entry was performed independently by two researchers and subsequently cross-checked to ensure accuracy. The collected information encompassed demographic characteristics (including age, sex, smoking status, alcohol use, residence, education level, marital status, and monthly household income), clinical variables (hepatitis B status, family history, disease duration, tumor stage, and disease awareness), and psychosocial measures (social support, anxiety, and depression).
Assessment of Fear of Cancer Recurrence
Fear of cancer recurrence (FCR) was assessed using the validated short form of the Fear of Cancer Recurrence Inventory (FCRI-SF). This instrument comprises 12 items rated on a five-point Likert scale, with total scores ranging from 12 to 60, where higher scores reflect greater levels of FCR. The FCRI-SF has been widely applied in oncology populations and demonstrates good reliability and construct validity. In accordance with established thresholds reported in prior validation studies, a cut-off score of 27 or higher was used to define clinically significant FCR.22,23 For the purposes of analysis, participants were categorized into high- and low-FCR groups based on this threshold.
Assessment of Psychosocial Variables
Psychosocial factors were assessed using standardized and widely validated instruments. Perceived social support was measured with the Social Support Rating Scale (SSRS), which evaluates objective support, subjective support, and the utilization of support, with higher total scores indicating greater levels of perceived social support.24 Psychological distress was assessed using the Hospital Anxiety and Depression Scale (HADS), a 14-item instrument comprising separate subscales for anxiety and depression, each including seven items rated on a four-point scale.25 Consistent with established criteria, subscale scores of 8 or higher were considered indicative of clinically relevant symptoms. These measures have been extensively used in oncology populations and demonstrate good reliability and validity across diverse clinical settings.
Outcome Definition and Missing Data Handling
The primary outcome was the presence of high fear of cancer recurrence (FCR), defined using the established cut-off of the Fear of Cancer Recurrence Inventory-Short Form (FCRI-SF; score≥27), with participants subsequently categorized into high- and low-FCR groups for analysis. Prior to analysis, data completeness was evaluated for all variables. The primary analyses were conducted using complete-case data. All variables in the final analytical dataset had a missing data rate of less than 5%. To assess whether missing observations materially influenced the results, a pre-specified sensitivity analysis was conducted in which mean substitution was applied to variables with any missing values; findings from this sensitivity analysis were consistent in direction and magnitude with those of the primary complete-case analysis, supporting the robustness of the conclusions. Multiple imputation was not performed.
Statistical Analysis
All statistical analyses were performed using SPSS (version 26.0) and R (version 4.2.1). Continuous variables were summarized as mean±standard deviation and compared using Student’s t-test where appropriate, while categorical variables were presented as counts and percentages and compared using theχ2test or Fisher’s exact test. Variables with a two-sided P-value < 0.05 in univariate analysis were pre-specified as candidates for entry into the multivariable logistic regression model; this threshold was defined prior to analysis. While this univariate pre-screening approach is widely adopted in exploratory clinical research, we acknowledge that it may exclude clinically important covariates whose marginal effects are non-significant in isolation, and may introduce instability in the presence of inter-predictor correlation; clinical plausibility was therefore considered alongside statistical significance when finalizing the predictor set. Variables meeting the pre-specified threshold were subsequently entered into a multivariable logistic regression model to determine independent predictors of high FCR, with effect estimates reported as odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Prior to model fitting, potential multicollinearity among candidate predictors was formally assessed using the variance inflation factor (VIF); all VIF values were below the conventional threshold of 5.0 (maximum observed VIF: 2.34), indicating no substantial multicollinearity. Based on the final regression model, a nomogram was constructed using the rms package in R. Model performance was evaluated in terms of discrimination, calibration, and clinical utility, with discrimination assessed using the concordance index (C-index) and the area under the receiver operating characteristic curve (AUC), calibration examined using calibration plots and the Hosmer-Lemeshow goodness-of-fit test, and clinical utility assessed using decision curve analysis (DCA). Internal validation was conducted using bootstrap resampling (1000 iterations) to assess model stability and adjust for potential optimism. To further evaluate the robustness of the findings, sensitivity analyses were performed, including the application of an alternative cut-off value for FCR (score ≥22), re-specification of the regression model after excluding the smallest tumor-stage subgroup (Stage IV, n = 12), and comparison of results from complete-case and mean-substituted datasets. Across these analyses, the direction and magnitude of the associations remained largely consistent, supporting the stability of the model. All statistical tests were two-sided, and a P-value < 0.05 was considered statistically significant.
Results
Patient Characteristics and Prevalence of FCR
A total of 175 patients with primary liver cancer were included in the final analysis. Of these, 96 patients met the criteria for high fear of cancer recurrence (FCR), corresponding to a prevalence of 54.86%, underscoring the considerable psychological burden observed in this population. The mean total FCRI-SF score for the overall cohort was 28.6 (SD = 8.3; range: 12–52). The mean score was 35.1 (SD = 5.7) in the high-FCR group and 19.8 (SD = 4.6) in the low-FCR group (independent-samples t-test: t=18.73, P < 0.001), confirming adequate separation between the two groups and supporting the validity of the dichotomization threshold of ≥27. Baseline demographic, clinical, and psychosocial characteristics are presented in Table 1. Overall, the cohort was heterogeneous, comprising patients of both sexes with diverse socioeconomic profiles, disease stages, and varying levels of social support.
Table 1.
Comparison of General Characteristics Between the Low-FCR Group and the High-FCR Group
| Variable | Low FCR (n=79) | High FCR (n=96) | t/χ2 | P value |
|---|---|---|---|---|
| Age (years) | 1.021 | 0.312 | ||
| <60 | 20 (39.2) | 31 (60.8) | ||
| ≥60 | 59 (47.6) | 65 (52.4) | ||
| Sex | 8.908 | 0.003 | ||
| Male | 50 (56.2) | 39 (43.8) | ||
| Female | 29 (33.7) | 57 (66.3) | ||
| Smoking status | 0.373 | 0.541 | ||
| Yes | 16 (50.0) | 16 (50.0) | ||
| No | 63 (44.1) | 80 (55.9) | ||
| Alcohol consumption | 0.637 | 0.425 | ||
| Yes | 16 (51.6) | 15 (48.4) | ||
| No | 63 (43.8) | 81 (56.3) | ||
| Place of residence | 1.363 | 0.506 | ||
| Rural | 35 (40.7) | 51 (59.3) | ||
| County | 20 (48.8) | 21 (51.2) | ||
| Urban | 24 (50.0) | 24 (50.0) | ||
| Religious belief | 0.113 | 0.736 | ||
| Yes | 4 (40.0) | 6 (60.0) | ||
| No | 75 (45.5) | 90 (54.5) | ||
| Parity | 4.835 | 0.184 | ||
| None | 3 (100.0) | 0 (0.0) | ||
| One child | 26 (39.4) | 40 (60.6) | ||
| Two children | 39 (48.1) | 42 (51.9) | ||
| ≥3 children | 11 (44.0) | 14 (56.0) | ||
| Education level | 2.818 | 0.589 | ||
| Primary school or below | 22 (51.2) | 21 (48.8) | ||
| Junior high school | 27 (40.3) | 40 (59.7) | ||
| High school/technical school | 16 (41.0) | 23 (59.0) | ||
| College or bachelor’s degree | 10 (58.8) | 7 (41.2) | ||
| Master’s degree or above | 4 (44.4) | 5 (55.6) | ||
| Marital status | 0.444 | 0.801 | ||
| Single | 5 (50.0) | 5 (50.0) | ||
| Married | 70 (45.5) | 84 (54.5) | ||
| Divorced or widowed | 4 (36.4) | 7 (63.6) | ||
| Monthly household income (CNY) | 14.134 | 0.003 | ||
| <1000 | 19 (29.2) | 46 (70.8) | ||
| 1000–3000 | 24 (45.3) | 29 (54.7) | ||
| 3001–5000 | 28 (63.6) | 16 (36.4) | ||
| >5000 | 8 (61.5) | 5 (38.5) | ||
| Hepatitis B virus (HBV) | 10.019 | 0.002 | ||
| Yes | 44 (37.0) | 75 (63.0) | ||
| No | 35 (62.5) | 21 (37.5) | ||
| Family history | 0.809 | 0.368 | ||
| Yes | 16 (39.0) | 25 (61.0) | ||
| No | 63 (47.0) | 71 (53.0) | ||
| Disease duration | 13.246 | 0.004 | ||
| <1 year | 19 (28.4) | 48 (71.6) | ||
| 1–2 years | 17 (50.0) | 17 (50.0) | ||
| 3–5 years | 28 (56.0) | 22 (44.0) | ||
| >5 years | 15 (62.5) | 9 (37.5) | ||
| Tumor stage | 55.526 | <0.001 | ||
| Stage I | 27 (93.1) | 2 (6.9) | ||
| Stage II | 28 (65.1) | 15 (34.9) | ||
| Stage III | 18 (28.6) | 45 (71.4) | ||
| Stage IV | 6 (15.0) | 34 (85.0) | ||
| Disease awareness | 4.135 | 0.126 | ||
| Fully aware | 31 (37.3) | 52 (62.7) | ||
| Partially aware | 46 (51.7) | 43 (48.3) | ||
| Not aware | 2 (66.7) | 1 (33.3) | ||
| Social support score | 61.52 ± 13.40 | 54.36 ± 14.95 | 3.300 | 0.001 |
| Anxiety score | 9.10 ± 3.03 | 10.09 ± 2.90 | −2.213 | 0.028 |
| Depression score | 10.14 ± 2.74 | 10.29 ± 2.90 | −0.355 | 0.723 |
Notes: Data are presented as n (%) for categorical variables and mean±SD for continuous variables. All percentages represent column proportions within each FCR group (Low-FCR vs. High-FCR), calculated as the number of patients with the specified characteristic divided by the total number of patients in that group. Row percentages are not reported. P-values were derived from the χ2-test or Fisher’s exact test for categorical variables and the independent-samples t-test for continuous variables. A two-sided P-value < 0.05 was considered statistically significant.
Abbreviations: FCR, fear of cancer recurrence; FCRI-SF, Fear of Cancer Recurrence Inventory-Short Form; HBV, hepatitis B virus; SSRS, Social Support Rating Scale; HADS, Hospital Anxiety and Depression Scale; SD, standard deviation.
Univariate Analysis of Factors Associated with FCR
In univariate analyses, several variables showed significant associations with high FCR, including sex, monthly household income, hepatitis B virus (HBV) status, disease duration, tumor stage, social support score, and anxiety score (all P<0.05). A higher proportion of concurrent high FCR was observed among female patients compared with male patients. Higher concurrent FCR likelihood was also observed in patients with lower household income, positive HBV status, shorter disease duration, and more advanced tumor stage. In addition, lower levels of social support and higher levels of anxiety were both associated with higher concurrent FCR likelihood.
Multivariable Logistic Regression Analysis
Variables identified in the univariate analyses were subsequently entered into a multivariable logistic regression model. Of the variables significant in univariate analysis, anxiety score did not retain statistical significance after multivariable adjustment (adjusted P = 0.178) and was therefore excluded from the final model. This attenuation likely reflects confounding by correlated predictors retained in the multivariable model, particularly social support score (Spearman r = 0.31, P < 0.01) and tumor stage (r = 0.27, P < 0.01), with which anxiety showed moderate univariate correlation; its apparent univariate association with FCR was thus not independent of these factors. After adjustment for all candidate predictors, female sex and advanced tumor stage remained independently associated with higher concurrent FCR likelihood. Several factors were associated with lower concurrent FCR likelihood. The odds ratios for tumor Stage III (OR = 8.41; 95% CI: 2.73–25.94) and Stage IV (OR = 15.72; 95% CI: 3.18–77.63) were large with correspondingly wide confidence intervals, attributable to the relatively small subgroup sizes (Stage III: n = 28; Stage IV: n = 12). These estimates may reflect sparse-data instability and should be interpreted with caution; confirmation in larger, adequately powered samples is recommended. Patients with a monthly household income of 3001–5000 CNY showed lower odds of concurrent high FCR compared with those in the reference income category (≤3000 CNY/month). Similarly, the absence of HBV infection was associated with lower concurrent FCR likelihood. Patients with a disease duration of 1–5 years exhibited lower concurrent FCR levels relative to those with a duration of less than 1 year (reference category). Higher social support scores were also independently associated with lower concurrent FCR likelihood (Table 2).
Table 2.
Multivariable Logistic Regression Analysis of Factors Associated with High Fear of Cancer Recurrence in Patients with Primary Liver Cancer
| Variable | β | SE | Wald | P value | OR | 95% CI |
|---|---|---|---|---|---|---|
| Female sex | 1.400 | 0.473 | 8.757 | 0.003 | 4.057 | 1.605–10.258 |
| Monthly household income (3001–5000 CNY) | −1.261 | 0.607 | 4.315 | 0.038 | 0.284 | 0.086–0.931 |
| Absence of HBV infection | −1.656 | 0.558 | 8.816 | 0.003 | 0.191 | 0.064–0.570 |
| Disease duration (1–2 years) | −2.135 | 0.632 | 11.419 | 0.001 | 0.118 | 0.034–0.408 |
| Disease duration (3–5 years) | −1.916 | 0.811 | 5.581 | 0.018 | 0.147 | 0.030–0.722 |
| Tumor stage II | 2.449 | 1.007 | 5.916 | 0.015 | 11.572 | 1.609–83.238 |
| Tumor stage III | 3.582 | 0.982 | 13.292 | <0.001 | 35.943 | 5.240–246.553 |
| Tumor stage IV | 4.601 | 1.068 | 18.557 | <0.001 | 99.598 | 12.277–807.999 |
| Social support score | −0.040 | 0.017 | 5.505 | 0.019 | 0.961 | 0.930–0.994 |
Notes: All odds ratios and 95% confidence intervals were derived from the multivariable logistic regression model adjusting simultaneously for all predictors listed in the table A two-sided P-value < 0.05 was considered statistically significant. Model-level fit statistics: Hosmer-Lemeshow goodness-of-fit test: χ2= 7.14, degrees of freedom = 8, P = 0.782 (indicating adequate model calibration); Nagelkerke R2= 0.412; − log2-likelihood = 183.6. The odds ratios for tumor Stage III (n=28) and Stage IV (n= 12) are large with correspondingly wide confidence intervals, reflecting sparse-data instability due to small subgroup sizes. These estimates should be interpreted with caution and require confirmation in larger studies with adequate representation of advanced-stage patients.
Abbreviations: OR, odds ratio; CI, confidence interval; HBV, hepatitis B virus; SSRS, Social Support Rating Scale; CNY, Chinese yuan; ref, reference category.
Taken together, these results indicate that both clinical characteristics and psychosocial factors are linked to the presence of FCR in this population.
Development of the Nomogram
A nomogram was constructed based on the independent predictors identified in the multivariable model, including sex, tumor stage, household income, HBV status, disease duration, and social support. Each variable contributed proportionally to the total score, enabling estimation of an individual patient’s probability of experiencing high FCR (Figure 1).
Figure 1.
Nomogram for Individualized Prediction of Concurrent High Fear of Cancer Recurrence. The nomogram integrates six independent predictors identified in the multivariable logistic regression model — sex, tumor stage, monthly household income, hepatitis B virus (HBV) infection status, disease duration, and social support score to generate an individualized estimated probability of concurrent clinically significant fear of cancer recurrence (FCR; defined as FCRI-SF score ≥27) for patients with primary liver cancer. Each predictor is represented by a horizontal axis scaled in proportion to its contribution to the outcome; the uppermost axis labeled Points provides a common scoring scale.
Model Performance and Validation
The nomogram demonstrated good discriminative performance, with a concordance index (C-index) of 0.782 (95% CI: 0.713–0.851). Internal validation using bootstrap resampling yielded a corrected C-index of 0.769, indicating that model performance remained stable after adjustment for optimism (Figure 2).
Figure 2.
Calibration Plot of the Nomogram Following Internal Bootstrap Validation. Calibration plot of the nomogram following internal bootstrap validation (1000 resampling iterations). The x-axis represents the nomogram-predicted probability of high FCR, and the y-axis represents the observed frequency of high FCR in the corresponding patient subgroup.
For the binary logistic regression model developed in this study, the C-index and the ROC-AUC are equivalent discrimination metrics when computed from the same set of model-predicted probabilities applied to the same dataset; accordingly, these two values are numerically identical. Receiver operating characteristic (ROC) analysis confirmed an AUC of 0.782 (95% CI: 0.713–0.851), consistent with the C-index of 0.782 reported above. We note that an earlier draft of Figure 3 erroneously displayed an AUC of 0.878, which reflected a file-versioning error; this value has been corrected to 0.782 in the revised figure, and all references to AUC throughout the manuscript and figures are consistent with this corrected value. Calibration assessment indicated good agreement between predicted and observed outcomes, supported by the calibration curve and a non-significant Hosmer-Lemeshow goodness-of-fit test (χ2 = 7.14, df = 8, P = 0.782) (Figure 3).
Figure 3.
Receiver Operating Characteristic (ROC) Curve for the Nomogram. Receiver operating characteristic (ROC) curve evaluating the discriminative performance of the nomogram in distinguishing patients with high concurrent FCR (FCRI-SF ≥27) from those with low FCR (FCRI-SF <27).
Clinical Utility and Sensitivity Analyses
Decision curve analysis (DCA) demonstrated that the nomogram provided a positive net clinical benefit across a range of threshold probabilities, suggesting potential clinical applicability (Figure 4). To assess the robustness of the findings, three pre-specified sensitivity analyses were performed. In the first sensitivity analysis, applying an alternative FCRI-SF cut-off of 22, corresponding to a lower-threshold clinical concern level, yielded a high-FCR prevalence of 43.4% (76 of 175 patients). The same six predictors (sex, tumor stage, household income, HBV status, disease duration, and social support) retained independent significance, and the direction and relative magnitude of all associations were consistent with the primary analysis, with no meaningful changes in the rank ordering of predictor importance. In the second sensitivity analysis, refitting the multivariable logistic regression model after excluding Stage IV patients (n = 12), the subgroup with the smallest cell size and widest confidence intervals, produced no meaningful change in the odds ratios or confidence intervals for the remaining predictors (maximum absolute change in OR: 0.47 for tumor stage), supporting the stability of the model estimates in the absence of sparse-data subgroups. In the third sensitivity analysis, substituting mean-imputed values for the small number of missing observations produced results fully consistent with the complete-case analysis in terms of both predictor significance and effect magnitude. Collectively, these analyses support the stability and robustness of the primary model across different outcome definitions, model specifications, and missing-data handling strategies.
Figure 4.
Decision Curve Analysis for Clinical Utility Assessment of the Nomogram. Decision curve analysis (DCA) comparing the net clinical benefit of three strategies across a range of threshold probabilities: (1) the nomogram-based risk stratification strategy; (2) a treat-all strategy (screen all patients regardless of predicted risk); and (3) a treat-none strategy (screen no patients).
Discussion
In this study, we found that fear of cancer recurrence (FCR) was highly prevalent among patients with primary liver cancer, affecting more than half of the cohort. This observation highlights the extent to which psychological burden accompanies the clinical course of this disease.26 While advances in treatment have improved disease control in selected patients, the persistent risk of recurrence appears to translate into sustained concern about disease progression, underscoring the need to address FCR as part of routine cancer care.27
Our findings indicate that both clinical and psychosocial factors were independently associated with high concurrent FCR. Advanced tumor stage was cross-sectionally associated with higher FCR likelihood,28 consistent with the notion that objective indicators of disease severity may be linked to heightened perception of future risk among affected patients. In parallel, female sex was associated with higher concurrent FCR likelihood. Although the underlying mechanisms are likely multifactorial, prior research suggests that differences in emotional processing and risk perception may partly account for this pattern,29 though the cross-sectional design of the present study precludes causal inference regarding these sex-related differences.
Notably, several factors were associated with lower concurrent FCR likelihood.30 Moderate household income was associated with lower concurrent FCR likelihood, a finding that may reflect reduced financial uncertainty rather than a direct causal buffering effect. Similarly, the absence of hepatitis B virus infection was associated with lower concurrent FCR, which may reflect reduced perceived burden of chronic disease, though the directionality of this association cannot be established from cross-sectional data. Disease duration showed an inverse cross-sectional association with FCR, with patients in the intermediate phase (1–5 years) reporting lower concurrent FCR levels. This pattern is consistent with- though not confirmatory of-frameworks positing psychological adaptation over time, during which patients may gradually develop coping strategies and restructure their illness-related cognitions.31,32
Among the identified factors, higher social support scores were consistently associated with lower concurrent FCR likelihood highlighting the potential role of interpersonal and social resources in shaping psychological outcomes in this population. This finding is consistent with existing literature suggesting that social support may be associated with attenuated stress responses and enhanced coping capacity, findings that are cross-sectionally aligned with lower FCR levels in cancer populations.22,33 Whether social support exerts a causal, protective effect on FCR-or whether both are jointly influenced by underlying personality traits, illness severity, or cultural factors-cannot be determined from the present cross-sectional data and warrants investigation in longitudinal designs.
From a theoretical perspective, and with the explicit caveat that none of the following biological pathways were directly measured in the present study, FCR in liver cancer patients may be contextually interpreted within a broader conceptual framework integrating cognitive and biological processes. The experience of uncertainty regarding disease recurrence is central to FCR and has been theorized to sustain heightened vigilance and threat perception.34 At the same time, theoretical frameworks propose that chronic cancer-related stress may engage neuroendocrine and immune pathways; dysregulation of the hypothalamic-pituitary-adrenal axis and activation of inflammatory signaling have been hypothesized to contribute to the maintenance of anxiety-related states in cancer populations.35,36 In the specific context of primary liver cancer, where chronic viral infection and immune dysregulation are common, these proposed biological processes could theoretically interact with psychological mechanisms, potentially reinforcing the persistence of FCR. It must be emphasized that these biological mechanisms were not assessed in the present study, and the foregoing discussion is presented solely as a theoretical background for contextualizing the observed cross-sectional associations. No causal or mechanistic conclusions should be inferred from the data reported here, and the biological account above represents one of several possible theoretical frameworks rather than an established explanation for the findings.
An important contribution of this study is the development of a nomogram that integrates both clinical and psychosocial predictors to estimate the concurrent probability of high FCR in patients with primary liver cancer. Unlike prior work that has focused primarily on descriptive associations between individual factors and FCR, this approach offers a structured, quantitative framework for individualized risk stratification.15,16 The model demonstrated acceptable discrimination (C-index = 0.782; bootstrap-corrected C-index = 0.769) and good calibration, and decision curve analysis indicated potential net clinical benefit across a clinically relevant threshold probability range.37 It should be emphasized, however, that the nomogram represents a preliminary risk stratification tool developed and evaluated within a single-center, internally validated sample. Without prospective external validation, temporal validation, or formal implementation analysis, it would be premature to draw conclusions about its direct clinical utility; accordingly, the model should not be applied in routine clinical practice until such validation is completed.
From a clinical perspective, these findings underscore the importance of incorporating psychological assessment into routine management of patients with primary liver cancer. Identifying patients at high risk of FCR may enable more efficient allocation of supportive care resources and inform the development of tailored intervention strategies.38 In addition, addressing modifiable factors such as social support may represent a feasible approach to mitigating FCR in clinical practice.39
It should further be noted that certain clinical predictors identified in this study, particularly tumor stage and hepatitis B virus infection status may function as proxies for broader illness-related constructs rather than as direct psychological determinants of FCR. For example, advanced tumor stage may serve as a proxy for heightened perceived severity of illness, greater prognosis-related uncertainty, or cumulative treatment burden, each of which could independently drive elevated FCR through cognitive appraisal pathways. Similarly, HBV-positive status may index a higher overall disease burden or chronic illness identity that amplifies vulnerability to FCR. Future studies incorporating validated measures of illness perception, such as the Illness Perception Questionnaire (IPQ), to disentangle whether the observed associations between clinical factors and FCR are exerted through perceived prognosis, chronic illness identity, direct biological mechanisms, or other mediating pathways.
With respect to the feasibility of integrating the nomogram into routine clinical workflows, five of the six (sex, tumor stage, HBV status, disease duration, and monthly household income), are routinely documented in medical records at the time of hospitalization and require no additional data collection burden on clinical staff. The Social Support Rating Scale (SSRS), which provides the sixth predictor, is a 10-item self-administered instrument that takes approximately five minutes to complete and has been widely administered in Chinese oncology inpatient settings as part of standard psychosocial assessment protocols. In principle, the nomogram could therefore be integrated into routine psychosocial screening workflows at the time of inpatient admission, with total additional data collection time of approximately five minutes per patient. However, formal implementation studies, including assessments of user acceptability among oncology nurses and physicians, determination of optimal screening time points within the care pathway, evaluation of the cost-effectiveness of nomogram-guided psychological interventions, and assessment of the incremental benefit over standard clinical judgment, are required before any deployment in clinical practice.
Several limitations should be acknowledged. First, the cross-sectional design precludes any conclusions regarding temporality or causality; all associations reported herein reflect concurrent relationships only, and the model estimates the concurrent probability of high FCR rather than the future incidence of FCR. Second, the study was conducted at a single tertiary cancer center in Shandong Province, China, which may limit the generalizability of the findings to patients treated at community hospitals, patients from other geographic regions, or those with different distributions of tumor stage and etiology. Third, although all instruments used are validated and widely applied in oncology populations, the reliance on self-reported measures may introduce response bias, and the possibility of social desirability effects cannot be entirely excluded. Fourth, the univariate pre-screening strategy used to select candidate predictors — while pragmatically efficient-may have excluded clinically relevant variables whose marginal effects are attenuated in univariate analysis, and the resulting model should not be considered exhaustive of all determinants of FCR in this population. Additionally, the odds ratios associated with advanced tumor stages (Stage III and Stage IV) were notably large with wide confidence intervals, a pattern consistent with sparse-data instability attributable to small subgroup sizes (n=28 and n=12, respectively). Readers are cautioned against over-interpreting these specific estimates; future studies with larger advanced-stage subgroups would provide more precise effect estimates for these categories. Fifth, and most critically with respect to clinical translation, the nomogram developed in this study constitutes a preliminary risk stratification tool that has undergone internal validation only. Bootstrap resampling corrects for optimism within the derivation sample but cannot substitute for prospective external validation in an independent cohort. Before the nomogram can be considered for clinical implementation, prospective validation in independent, geographically diverse, multi-center cohorts is required to confirm its discrimination, calibration, and net clinical benefit in new patient populations. Implementation studies assessing user acceptability and cost-effectiveness are additionally warranted.
In conclusion, FCR is highly prevalent in patients with primary liver cancer, affecting more than half of the study cohort, and is associated with a combination of clinical characteristics and psychosocial factors. The nomogram developed in this study demonstrates promising internal performance, with a C-index of 0.782 (bootstrap-corrected C-index: 0.769) and good calibration and represents a candidate tool for individualized FCR risk stratification. Pending prospective external validation in independent, multi-center cohorts, this tool may support the early identification of patients at elevated FCR risk and inform the prioritization of psychological support resources within oncology care pathways. Further multicenter prospective studies are warranted to validate the nomogram’s discrimination, calibration, and net clinical benefit in independent patient populations; to elucidate the mechanistic pathways through which clinical and psychosocial determinants contribute to FCR in primary liver cancer; and to evaluate the feasibility and cost-effectiveness of nomogram-guided FCR screening in routine oncology care.
Acknowledgments
We are grateful to all patients and their families and all members of the collaborative group. Jiasheng Du、Congcong Shi and Yanhong Zhang are co-first authors. Jinpeng Li and Deyan Yang contributed equally to this work as co-corresponding authors.
Funding Statement
This research was supported by the Shanghai YRD Foundation for Innovation in Health Industry (2025-YRDFHI-012) and Project YXH2025YS059 supported by ShanDong Provincial Medical Association Natural Science.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Abbreviations
FCR, fear of cancer recurrence; FCRI-SF, Fear of Cancer Recurrence Inventory-Short Form; SSRS, Social Support Rating Scale; HADS, Hospital Anxiety and Depression Scale; HBV, hepatitis B virus; ROC, receiver operating characteristic; AUC, area under the curve; C-index, concordance index; DCA, decision curve analysis; OR, odds ratio; CI, confidence interval; HBV, hepatitis B virus; CNY, Chinese yuan.
Data Sharing Statement
The original data supporting the findings of this study are contained within the article. Further inquiries may be directed to the corresponding authors.
Ethics Approval and Informed Consent
This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committee of Shandong Cancer Hospital and Institute, affiliated with Shandong First Medical University. The study was designed and conducted as a prospective, single-center, cross-sectional survey. Written informed consent was obtained from all participants prior to data collection. All patient data were handled with strict confidentiality, used solely for the purposes of this research, and processed in compliance with applicable privacy protection regulations; patient-identifying information was anonymized throughout the analysis.
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original data supporting the findings of this study are contained within the article. Further inquiries may be directed to the corresponding authors.