Development and Validation of a Preoperative Nomogram for Predicting Benign and Malignant Gallbladder Polypoid Lesions

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.

Advances in the management of gallbladder polyps: establishment of predictive models and the rise of gallbladder-preserving polypectomy procedures

Gallbladder polyps are a common biliary tract disease whose treatment options have yet to be fully established. The indication of "polyps ≥ 10 mm in diameter" for cholecystectomy increases the possibility of gallbladder excision due to benign polyps. Compared to enumeration of risk factors in clinical guidelines, predictive models based on statistical methods and artificial intelligence provide a more intuitive representation of the malignancy degree of gallbladder polyps. Minimally invasive gallbladder-preserving polypectomy procedures, as a combination of checking and therapeutic approaches that allow for eradication of lesions and preservation of a functional gallbladder at the same time, have been shown to maximize the benefits to patients with benign polyps. Despite the reported good outcomes of predictive models and gallbladder-preserving polypectomy procedures, the studies were associated with various limitations, including small sample sizes, insufficient data types, and unknown long-term efficacy, thereby enhancing the need for multicenter and large-scale clinical studies. In conclusion, the emergence of predictive models and minimally invasive gallbladder-preserving polypectomy procedures has signaled an ever increasing attention to the role of the gallbladder and clinical management of gallbladder polyps.

The value of CT findings combined with inflammatory indicators for preoperative differentiation of benign and malignant gallbladder polypoid lesions

BACKGROUND

The study aimed to explore the value of CT findings and inflammatory indicators in differentiating benign and malignant gallbladder polypoid lesions before surgery.

METHODS

The study comprised a total of 113 pathologically confirmed gallbladder polypoid lesions with a maximum diameter ≥ 1 cm (68 benign and 45 malignant), all of which were enhanced CT-scanned within 1 month before surgery. The CT findings and inflammatory indicators of the patients were analyzed by univariate and multivariate logistic regression analysis to identify independent predictors of gallbladder polypoid lesions, and then a nomogram distinguishing benign and malignant gallbladder polypoid lesions was developed by combining these characteristics. The receiver operating characteristic (ROC) curve and decision curve were plotted to assess the performance of the nomogram.

RESULTS

Base status of the lesion (p < 0.001), plain CT value (p < 0.001), neutrophil-lymphocyte ratio (NLR) (p = 0.041), and monocyte-lymphocyte ratio (MLR) (p = 0.022) were independent predictors of malignant polypoid lesions of the gallbladder. The nomogram model established by incorporating the above factors had good performance in differentiating and predicting benign and malignant gallbladder polypoid lesions (AUC = 0.964), with sensitivity and specificity of 82.4% and 97.8%, respectively. The DCA demonstrated the important clinical utility of our nomogram.

CONCLUSION

CT findings combined with inflammatory indicators can effectively differentiate benign and malignant gallbladder polypoid lesions before surgery, which is valuable for clinical decision-making.