A nomogram‑based diagnostic prediction model for differentiating mucinous cystic neoplasms from simple hepatic cysts in patients with hepatic cystic lesions

Introduction

Background

Hepatic mucinous cystic neoplasm (H-MCN), which was officially designated by the World Health Organization in 2010, is a rare type of epithelial cystic tumor of the liver (1,2). Only approximately 5% of hepatic cystic lesions are ultimately diagnosed as H-MCNs, which have a malignancy rate of up to 30% (3-5). Therefore, distinguishing between H-MCNs from simple hepatic cysts (SHCs) is critical since they share similar clinical presentations but entail different prognoses.

Rationale and knowledge gap

Patients with H-MCNs or SHCs may have no clinical symptoms or nonspecific symptoms, such as abdominal pain, bloating, or the presence of an abdominal mass, and a portion may develop jaundice (6). This makes preoperative differentiation between SHCs and H-MCNs particularly challenging (7,8). Accurate preoperative diagnosis is essential for determining treatment recommendations and improving patient outcomes. Studies have shown that H-MCNs often present as single, large cystic lesions with clear boundaries and septations in enhanced computed tomography (CT) (9). Features such as septal enhancement, septal thickening, cyst wall calcification, cyst wall nodules, and elevated levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) in the cyst fluid may suggest malignancy (10-13). However, a reliable method for distinguishing between SHCs and H-MCNs has not yet been established (14,15).

Objective

Therefore, this study was designed to systematically analyze the clinical and imaging data of SHCs and H-MCNs and to develop a nomogram-based diagnostic prediction model that could aid clinicians in the preoperative differentiation of H-MCNs from SHCs. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0433/rc).

Methods

Study population

This retrospective study included patients who were pathologically diagnosed with SHCs or H-MCNs at West China Hospital of Sichuan University, a major tertiary hospital in southwestern China, from January 2010 to January 2024. The pathological diagnosis was performed by experienced pathologists; however, due to the retrospective nature of the design, they were not specifically required to review the clinical or imaging information in a blinded manner.

We did not formally calculate the sample size. All available data on the database were used to maximize the power and generalizability of the results. A total of 1,193 patients with pathologically confirmed SHC or H-MCN were initially identified from our database. After 106 patients were excluded due to incomplete clinical or laboratory data (n=89), age <18 years (n=5), or complication with other malignancies (n=12), 1,087 patients were included in the final analysis. Patients were randomly divided into a training set and an internal validation set at a ratio of 7:3 for model development and internal validation purposes.

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was reviewed and approved by the Biomedical Ethics Committee of West China Hospital of Sichuan University (approval No. 2024-1029). The requirement for informed consent was waived due to the retrospective nature of the analysis.

Data collection

The following data were extracted for each patient: gender, age, symptoms (defined as the presence of abdominal pain, bloating, or palpable abdominal mass), size of the lesion (cm), location (right lobe vs. left lobe), number of lesions (single vs. multiple), septal enhancement, calcification, mural nodules, alpha-fetoprotein level (ng/mL), CA19-9 level (U/mL), cancer antigen 125 level (CA125; U/mL), CEA level (ng/mL), total bilirubin level (µmol/L), albumin level (g/L), alanine aminotransferase (ALT) level (IU/L), prothrombin time (min), platelet count (×10⁹/L), white blood cell count (×10⁹/L), neutrophil count (×10⁹/L), and lymphocyte count (×10⁹/L). Imaging features derived from contrast-enhanced CT or magnetic resonance imaging (MRI) within 1 month before surgery were analyzed together based on predefined radiological criteria. All laboratory tests were conducted within 30 days before liver surgery. In the case of repeated tests, the results closest to the time of the surgery were used.

Again, owing to the retrospective design, the radiologists and laboratory personnel were not formally blinded to clinical information at the time of assessment, which might have introduced information bias.

Statistical analysis

Only patients with complete data on all candidate predictors and outcomes were included in the analysis. Continuous variables with a normal distribution were reported as the mean ± standard deviation, while those with non-normal distributions were reported as the median and interquartile range. Categorical variables were presented as frequencies (percentages). The Mann-Whitney U test was used to compare continuous variables, while the Chi-squared test was used to compare categorical variables and determine significant differences between the SHC and H-MCN groups.

Variables showing statistically significant differences were further analyzed with logistic least absolute shrinkage and selection operator (LASSO) regression to identify significant variables and prevent model overfitting. The penalty term k was used to select variables for model fitting. Logistic regression analysis was then conducted, and a nomogram was developed. A receiver operating characteristic (ROC) curve was generated from the model, and the area under the curve (AUC) with a 95% confidence interval (CI) was calculated for the nomogram. Calibration curves were plotted for both the training and internal validation sets via 1,000 bootstrap resamples to validate prediction accuracy. Decision curve analysis (DCA) was performed to assess the net clinical benefit. All statistical analyses were conducted with R version 4.3.3 (The R Foundation for Statistical Computing, Vienna, Austria), with P<0.05 indicating a significant difference.

Results

Patient characteristics

The training set included 761 patients (701 with SHC and 60 with H-MCN), whereas the internal validation set included 326 patients (303 with SHC and 23 with H-MCN). Clinical features of both training and validation sets are summarized in Table 1. In both the SHC and H-MCN groups, the majority of patients were female. Patients with H-MCN were more likely to exhibit symptoms and typically had solitary lesions.

Nomogram construction

LASSO regression was used to identify relevant variables and prevent overfitting. A penalty term (k) was applied, resulting in the selection of 13 variables: sex, presence of symptoms, number of lesions, lesion location, septal characteristics, septal enhancement, mural nodules, hepatitis status, age, CA19-9 level, CA125 level, CEA level, and ALT [λ=0.008023; mean cross-validation error =0.2673; standard error (SE) =0.0423] (Figure 1).

Figure 1 Variable selection using LASSO regression analysis. LASSO, least absolute shrinkage and selection operator.

Multivariate logistic regression of these 13 variables identified 8 independent predictors of H-MCNs: sex [odds ratio (OR) 5.061, 95% CI: 1.461–23.445; P=0.02], presence of symptoms (OR 12.022, 95% CI: 4.947–32.169; P<0.001), number of lesions (OR 0.175, 95% CI: 0.055–0.498; P=0.002), lesion location (OR 5.388, 95% CI: 2.215–14.193; P<0.001), septal enhancement (OR 3.793, 95% CI: 1.111–13.464; P=0.04), mural nodules (OR 11.593, 95% CI: 1.646–121.764; P=0.02), age (OR 0.955, 95% CI: 0.920–0.990; P=0.01), CA19-9 (OR 1.003, 95% CI: 1.001–1.006; P=0.02) (Table 2). The final logistic regression model was formulated as follows: logit(P) = −3.757 + 1.622X1 + 2.487X2 − 1.746X3 + 1.684X4 + 1.333X5 + 2.45X6 − 0.046X7 + 0.003X8, where P represents the predicted probability of H-MCN [X1 = sex (female =1, male =0); X2 = symptom (yes =1, no =0); X3 = number of lesions (multiple =1, single =0); X4 = lesion location (left lobe =1, right lobe =0); X5 = septal enhancement (yes =1, no =0); X6 = mural nodules (yes =1, no =0); X7 = age (years); X8 = serum CA19-9 level (U/mL)].

A nomogram was constructed based on the regression coefficients of these eight variables (Figure 2). To apply the nomogram, the value of each predictor is located on its corresponding axis, and a vertical line is drawn upward to determine the assigned score. The individual scores are then summed to generate the total points, which correspond to the estimated probability of H-MCN shown on the bottom scale. Higher total scores indicate a greater likelihood of H-MCN.

Figure 2 The nomogram for preoperative distinguishing SHCs from H-MCNs. CA19-9, carbohydrate antigen 19-9; H-MCN, hepatic mucinous cystic neoplasm; SHC, simple hepatic cyst.

Validation of the nomogram

First, the discriminatory power of the nomogram was assessed through the plotting of ROC curves and calculation of AUC values. The nomogram achieved an AUC of 0.950 (95% CI: 0.911–0.988) in the training set (Figure 3A) and an AUC of 0.944 (95% CI: 0.903–0.985) in the internal validation set (Figure 3B). These results demonstrate favorable discriminatory ability and calibration performance in distinguishing between the SHCs and H-MCNs. Calibration curves generated with 1,000 bootstrap resamples demonstrated good agreement between predicted and observed probabilities (Figure 4), suggesting acceptable calibration performance of the model within the current dataset. DCA was used to assess the net clinical benefit of the nomogram, which showed a net benefit above baseline (Figure 5). These results confirm the nomogram’s effectiveness as a valuable tool for the preoperative differentiation of SHCs and H-MCNs.

Figure 3 ROC curve of the nomogram in the training set (A) and the internal validation set (B). AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

Figure 4 Calibration plot for the nomogram in the training set and the validation set.

Figure 5 The DCA for the nomogram. DCA, decision curve analysis.

Discussion

H-MCNs, a rare type of hepatic cystic lesion accounting for approximately 5% of all hepatic cystic lesions (3,16), is often diagnosed based on the pathological feature of ovarian-like stroma (17). SHCs, being benign, are generally treated with fenestration only when severe compression symptoms occur and rarely necessitate radical resection (18). However, H-MCNs have a malignancy rate of up to 30%, making radical resection the preferred treatment upon diagnosis to reduce recurrence and malignancy rates (19-21). Fenestration surgery for H-MCNs can lead to a recurrence rate of 48.6%, potentially increasing malignancy risk (19). Therefore, accurate preoperative differentiation is critical. However, due to the lack of specific clinical symptoms, serological markers, and imaging features, the preoperative differentiation between H-MCNs and SHCs remains challenging.

Studies have been conducted aimed at accurately distinguishing H-MCNs from SHCs preoperatively; however, due to small sample sizes, a reliable preoperative method is yet to be identified (14,22-25). To obtain an adequate sample size, we conducted a retrospective analysis of patients pathologically diagnosed with H-MCN or SHC at West China Hospital of Sichuan University from January 2010 to January 2024. A comparative analysis of 1,004 patients with SHC and 83 with H-MCN revealed that both lesions more frequently occur in females, with H-MCNs especially prevalent among younger women, which is consistent with previous studies (22,24). Both patients with H-MCN and those with SHC lacked specific symptoms. Although SHCs may cause abdominal discomfort due to tumor size or location, H-MCNs are more likely to present nonspecific symptoms such as abdominal pain and bloating, which are possibly linked to their malignant potential. Advances in imaging technology have made imaging modalities, particularly contrast-enhanced CT and MRI, a common method for differentiating H-MCNs from SHCs (26). H-MCNs mainly presented as solitary cystic lesions in the left liver lobe, which is consistent with previous findings (9,27-29). The reason that H-MCNs are more frequently located in the left lobe remains unclear, and thus further exploration into their origins and development mechanisms is warranted. Statistically significant differences in septal enhancement, septal calcification, and mural nodules were observed between the H-MCN and SHC groups, which may also be related to the malignant nature of H-MCNs (10-12). Careful differentiation is needed if these features are present. Previous studies have also investigated biological markers for accurately distinguishing H-MCNs from SHCs (22-25). Koffron et al. reported that elevated CA19-9 and CEA levels in cyst fluid can distinguish H-MCNs (25), whereas Choi et al. found cyst fluid analysis ineffective for diagnosing H-MCNs (22). These conflicting results may be due to the small sample sizes of the related studies. Furthermore, cyst fluid sampling may increase the risk of malignancy spread, potentially impacting patient outcomes. Therefore, we chose to use blood test data in our study. Analysis revealed that an elevated CA19-9 level was an independent risk factor for H-MCNs, a finding not previously reported. An elevated serum CA19-9 level is known to have value in predicting outcomes in patients with pancreatic mucinous cystic neoplasms, and mucinous cystic neoplasms in the liver, pancreas, ovary, and retroperitoneum are thought to share a common origin (30,31). Thus, the association of elevated CA19-9 level as an independent risk factor for H-MCN is likely significant, supporting accurate preoperative differentiation and presenting a novel research direction.

A nomogram is a visual tool that presents logistic regression models intuitively, aiding clinical decision-making. In recent years, nomograms have been widely used in tumor diagnosis to predict metastasis risk and patient prognosis (27,32). In 2021, Gao et al. analyzed 75 patients with SHC and 25 with H-MCN, constructing a nomogram based on contrast enhancement, intrahepatic location, bile duct dilatation, and CA19-9 level. However, the small sample size introduced selection bias, and the inclusion of CA19-9 level, a statistically nonsignificant variable, limited its applicability (14). In this study, we identified eight independent risk factors for H-MCNs using multivariate logistic regression: sex, presence of clinical symptoms, number of lesions, lesion location, septal enhancement, mural nodules, age, and CA19-9 level. To accurately distinguish H-MCNs from SHCs preoperatively, we constructed a nomogram based on these eight factors. ROC curve analysis yielded AUC values of 0.950 in the training set and 0.944 in the internal validation set, indicating strong discriminatory ability. Calibration curve confirmed a high agreement between predicted and actual outcomes, validating the model’s reliability. DCA demonstrated that the nomogram’s net clinical benefit exceeded the baseline, indicating practical value for clinical decision-making. These results indicate that the nomogram may help improve preoperative diagnostic accuracy and assist in optimizing treatment strategies.

This study involved several limitations that should be addressed. First, as we employed a single-center, retrospective design with a limited sample size; the model may not have wide generalizability. In addition, the study population represented surgically treated or diagnostically challenging hepatic cysts, which may introduce selection bias and limit the generalizability of the model to all patients with SHCs. Moreover, although LASSO regression and bootstrap calibration were used to reduce the risk of overfitting, the relatively limited number of H-MCN cases, particularly for some infrequent imaging features such as mural nodules, may still have resulted in statistical instability and optimistic model performance estimates. Therefore, larger multicenter cohorts and external validation are required to further confirm the robustness and reproducibility of the model. Second, patients with incomplete clinical or laboratory data were excluded from the analysis, and complete-case analysis may have introduced selection bias if the missingness was associated with clinical characteristics or disease severity. Although the proportion of excluded patients was relatively small, the potential impact of missing data on model performance and generalizability cannot be completely excluded. Future studies using prospective data collection or multiple imputation methods may help reduce this potential bias. Third, the imaging features were retrospectively assessed by experienced radiologists without formal interobserver agreement analysis. Therefore, variability in imaging interpretation, particularly for subjective features such as septal enhancement and mural nodules, may have influenced predictor assessment and model performance. Future prospective studies with standardized imaging evaluation protocols and reproducibility analyses are warranted.

Conclusions

This study developed and internally validated a nomogram based on sex, presence of symptoms, number of lesions, lesion location, septal enhancement, mural nodules, age, and CA19-9 level for preoperatively distinguishing between SHCs and H-MCNs. The nomogram showed favorable discriminatory performance and may serve as a promising tool for optimizing treatment strategies and improving patient outcomes.

Acknowledgments

The authors thank the nurses, patients and their families for their contributions.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0433/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0433/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0433/prf

Funding: This work was supported by the 1·3·5 Project for Disciplines of Excellence-Clinical Research Fund, West China Hospital of Sichuan University (No. 2025HXFH015).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0433/coif). All authors report funding support from the 1·3·5 Project for Disciplines of Excellence-Clinical Research Fund, West China Hospital of Sichuan University (No. 2025HXFH015). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was reviewed and approved by the Biomedical Ethics Committee of West China Hospital of Sichuan University (approval No. 2024-1029). The requirement for informed consent was waived due to the retrospective nature of the analysis.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)