Insights From the Analysis of Clinicopathological and Prognostic Factors in Patients With Gallbladder Cancer

Risk factors and survival prediction model establishment for prognosis in patients with radical resection of gallbladder cancer

BACKGROUND

Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system, and is often undetected until advanced stages, making curative surgery unfeasible for many patients. Curative surgery remains the only option for long-term survival. Accurate postsurgical prognosis is crucial for effective treatment planning. tumor-node-metastasis staging, which focuses on tumor infiltration, lymph node metastasis, and distant metastasis, limits the accuracy of prognosis. Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors, enhancing the precision of treatment planning for patients with GBC.

AIM

To identify risk factors and develop a predictive model for GBC prognosis.

METHODS

A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020. Kaplan-Meier analysis was used to calculate the 1-, 2- and 3-year survival rates. The log-rank test was used to evaluate factors impacting prognosis, with survival curves plotted for significant variables. Single-factor analysis revealed statistically significant differences, and multivariate Cox regression identified independent prognostic factors. A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.

RESULTS

Among 93 patients who underwent radical surgery for GBC, 30 patients survived, accounting for 32.26% of the sample, with a median survival time of 38 months. The 1-year, 2-year, and 3-year survival rates were 83.87%, 68.82%, and 53.57%, respectively. Univariate analysis revealed that carbohydrate antigen 19-9 expression, T stage, lymph node metastasis, histological differentiation, surgical margins, and invasion of the liver, extrahepatic bile duct, nerves, and vessels (P ≤ 0.001) significantly impacted patient prognosis after curative surgery. Multivariate Cox regression identified lymph node metastasis (P = 0.03), histological differentiation (P < 0.05), nerve invasion (P = 0.036), and extrahepatic bile duct invasion (P = 0.014) as independent risk factors. A nomogram model with a concordance index of 0.838 was developed. Internal validation confirmed the model's consistency in predicting the 1-year, 2-year, and 3-year survival rates.

CONCLUSION

Lymph node metastasis, tumor differentiation, extrahepatic bile duct invasion, and perineural invasion are independent risk factors. A nomogram based on these factors can be used to personalize and improve treatment strategies.

Machine learning-based short-term DFS-associated characteristic factor screening and model construction for patients with gallbladder cancer after radical surgery

Gallbladder cancer (GBC) is a malignancy with a bleak prognosis, and radical surgery remains the primary treatment option. However, the high postoperative recurrence rate and the lack of individualized risk assessment tools limit the effectiveness of current treatment strategies. This study aims to identify risk factors affecting the short-term disease-free survival (DFS) of GBC patients using machine learning methods and to build a prediction model. A retrospective analysis was conducted on the clinical data from 328 GBC patients treated at the First Affiliated Hospital of Huzhou University from 2008 to 2021. Patients were randomly divided into a training set (n=230) and a validation set (n=98). Clinical data, laboratory indexes, and follow-up data were collected. Univariate Cox regression analysis identified age, tumor T-staging, lymph node metastasis, differentiation degree, and CA199 level as prognostic factors affecting DFS (all P<0.05). A prediction model constructed using the LASSO regression achieved AUCs of 0.827 and 0.801 for predicting 1-year and 3-year DFS, respectively. Notably, the XGBoost regression model showed higher prediction accuracy with AUCs of 0.922 and 0.947, respectively. The Delong test confirmed that the XGBoost model had significantly higher AUC values compared to the LASSO model (all P<0.001). In the validation set, the XGBoost model demonstrated AUCs of 0.764 and 0.761 for predicting 1-year and 3-year DFS, respectively. Overall, the XGBoost regression model demonstrates high accuracy and clinical value in predicting short-term DFS in GBC patients after radical surgery, offering a valuable tool for personalized treatment.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics

BACKGROUND

Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes.

PURPOSE

To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging.

MATERIAL AND METHODS

All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves.

RESULTS

In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05).

CONCLUSION

It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

Fatty Acid Binding Protein 1 is an Independent Prognostic Biomarker for Gallbladder Cancer with Direct Hepatic Invasion

Background: Direct liver invasion (DI) is a predominant pathway of gallbladder cancer (GBC) metastasis, but the molecular alterations associated with DI remain addressed. This study identified specific genes correlated with DI, which may offer a potential biomarker for the diagnosis and prognosis of advanced GBC. Methods: RNA samples from 3 patients with DI of GBC were used for RNA-seq analysis. Differentially expressed genes and metabolic pathways between primary tumor (T) and DI tissue was used to analyze aberrant gene expressions. Immunohistochemistry (IHC) of fatty acid binding protein 1 (FABP1) in 62 patients with DI was engaged to evaluate its association with clinicopathological characteristics and prognosis. IHC of CD3+ and CD8+ T cells was analyzed for their correlation with FABP1 expression, clinicopathological features and prognosis. Univariate and multivariate Cox hazards regression analyses were performed to identify independent prognostic factors for disease-free survival (DFS) and overall survival (OS). Results: FABP1 mRNA levels were significantly upregulated in DI region compared to T tissue. IHC results showed identical results with elevated FABP1 (p < 0.0001). Expression of FABP1 in DI region was significantly associated with lymph node metastasis (P = 0.028), reduced DFS (P = 0.013) and OS (P = 0.022); in contrast, its expression in T region was not associated with clinicopathological characteristics and prognosis (P > 0.05). The density of CD8+ T cells in DI region with higher FABP1 expression was significantly lower than that with lower FABP1 expression (p = 0.0084). Multivariate analysis unveiled those hepatic metastatic nodules (HR = 3.35, 95%CI: 1.37-8.15, P = 0.008) and FABP1 expression in DI region (HR = 2.01, 95%CI: 1.05-3.88, P = 0.036) were high risk factors for OS, and FABP1(HR = 2.05, 95%CI: 1.04-4.06, P = 0.039) was also a high risk factor for DFS. Conclusions: Elevated expression of FABP1 in DI region serves as a potential prognostic biomarker for advanced GBC with DI.

Genetic characteristics and clinical-specific survival prediction in elderly patients with gallbladder cancer: a genetic and population-based study

Background

Biliary system cancers are most commonly gallbladder cancers (GBC). Elderly patients (≥ 65) were reported to suffer from an unfavorable prognosis. In this study, we analyzed the RNA-seq and clinical data of elderly GBC patients to derive the genetic characteristics and the survival-related nomograms.

Methods

RNA-seq data from 14 GBC cases were collected from the Gene Expression Omnibus (GEO) database, grouped by age, and subjected to gene differential and enrichment analysis. In addition, a Weighted Gene Co-expression Network Analysis (WGCNA) was performed to determine the gene sets associated with age grouping further to characterize the gene profile of elderly GBC patients. The database of Surveillance, Epidemiology, and End Results (SEER) was searched for clinicopathological information regarding elderly GBC patients. Nomograms were constructed to predict the overall survival (OS) and cancer-specific survival (CSS) of elderly GBC patients. The predictive accuracy and capability of nomograms were evaluated through the concordance index (C-index), calibration curves, time-dependent operating characteristic curves (ROC), as well as area under the curve (AUC). Decision curve analysis (DCA) was performed to check out the clinical application value of nomograms.

Results

Among the 14 patients with GBC, four were elderly, while the remaining ten were young. Analysis of gene differential and enrichment indicated that elderly GBC patients exhibited higher expression levels of cell cycle-related genes and lower expression levels of energy metabolism-related genes. Furthermore, the WGCNA analysis indicated that elderly GBC patients demonstrated a decrease in the expression of genes related to mitochondrial respiratory enzymes and an increase in the expression of cell cycle-related genes. 2131 elderly GBC patients were randomly allocated into the training cohort (70%) and validation cohort (30%). Our nomograms showed robust discriminative ability with a C-index of 0.717/0.747 for OS/CSS in the training cohort and 0.708/0.740 in the validation cohort. Additionally, calibration curves, AUCs, and DCA results suggested moderate predictive accuracy and superior clinical application value of our nomograms.

Conclusion

Discrepancies in cell cycle signaling and metabolic disorders, especially energy metabolism, were obviously observed between elderly and young GBC patients. In addition to being predictively accurate, the nomograms of elderly GBC patients also contributed to managing and strategizing clinical care.