Preoperative differentiation of benign and malignant gallbladder lesions using multimodal ultrasound radiomics: a retrospective study

Abstract

Objectives This study aims to develop and validate a multimodal ultrasound radiomics model to differentiate benign from malignant gallbladder lesions preoperatively by integrating conventional ultrasound, contrast-enhanced ultrasound (CEUS), and clinical indicators. Methods This retrospective study included 166 patients with histopathologically confirmed gallbladder lesions (36 malignant and 130 benign), divided in a 4:1 ratio into a cross-validation set (n = 133) and an independent test set (n = 33). A total of 37 clinical variables were collected. From each imaging modality, 513 radiomics features were extracted, normalized using parameters from the cross-validation set, and selected using sparse representation and sequential forward selection. Multilayer perceptron classifiers were developed using conventional ultrasound, CEUS, and combined features. Performance metrics and their 95% confidence intervals (CIs) were calculated, and areas under the receiver operating characteristic curves (AUCs) were compared using the paired DeLong test. Finally, a radiomics nomogram integrating the combined radiomics score and clinical indicators was constructed. Results Among the clinical and semantic ultrasound variables, maximum lesion diameter, CEUS-derived base width, triglyceride, and high-density lipoprotein cholesterol differed significantly between benign and malignant lesions. In the cross-validation set, the conventional ultrasound, CEUS, and combined models achieved AUCs of 0.814 (95% CI: 0.689–0.938), 0.848 (95% CI: 0.764–0.932), and 0.936 (95% CI: 0.887–0.986), respectively. In the independent test set, the corresponding AUCs were 0.820 (95% CI: 0.664–0.976), 0.915 (95% CI: 0.818–1.000), and 0.920 (95% CI: 0.823–1.000). The difference between the CEUS and combined models was not significant (P = 0.943). At the prespecified classification threshold, the combined model achieved an accuracy of 0.909 (95% CI: 0.757–0.981), a sensitivity of 0.750 (95% CI: 0.349–0.968), a specificity of 0.960 (95% CI: 0.796–0.999), a positive predictive value of 0.857 (95% CI: 0.421–0.996), and a negative predictive value of 0.923 (95% CI: 0.749–0.991). Conclusions The combined conventional ultrasound and CEUS radiomics model showed favorable discrimination and a balanced sensitivity–specificity profile in differentiating benign from malignant gallbladder lesions. Although its AUC was comparable to that of the CEUS radiomics model and its statistical superiority was not established, multimodal feature integration may provide complementary diagnostic information. Integration of radiomics signatures with clinical and imaging variables may support individualized preoperative risk stratification, but the incremental clinical value of this approach requires prospective multicenter validation.