Journal Article

Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm

Jiang, Dong, Gu, Yijun, Wang, Zhenmeng, Dong, Hui, Yu, Hua, Jiang, Haozheng, Wang, Ru, Chen, Yan, Chen, Dongyu, Qian, Yi, Li, Yiran

2025

Overview

Background A key challenge in the medical field is managing gallbladder polyps (GBP) > 10 mm, especially when their nature is uncertain. GBP with a diameter exceeding 10 mm are associated with an increased risk of gallbladder cancer, making the key to their management the differentiation between benign and malignant types. The current practice, due to the inability to predict accurately, leads to excessive surgeries and ineffective follow-ups, increasing patient risks and medical burdens. Purpose This study aims to establish an imaging radiomics model using clinical data and contrast-enhanced ultrasound (CEUS) to predict neoplastic GBP exceeding 10 mm in diameter preoperatively. Materials and methods Data from 119 patients with GBP > 10 mm of unknown origin were analyzed. A total of 1197 features were extracted from the GBP area using conventional ultrasound (US) and CEUS. Significant features were identified using the Mann–Whitney U test and further refined with a least absolute shrinkage and selection operator (LASSO) regression model to construct radiomic features. By integrating clinical characteristics, a radiomics nomogram was developed. The diagnostic efficacy of the preoperative logistic regression (LR) model was validated using receiver operating characteristic (ROC) curves, calibration plots, and the Hosmer–Lemeshow test. CEUS is an examination based on conventional ultrasound, and conventional two-dimensional ultrasound still poses significant challenges in differential diagnosis. CEUS has a high accuracy rate in diagnosing the benign or malignant nature of gallbladder space-occupying lesions, which can significantly reduce the preoperative waiting time for related examinations and provide more reliable diagnostic information for clinical practice. Results Feature selection via Lasso led to a final LR model incorporating high-density lipoprotein, smoking status, basal width, and Rad_Signature. This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. The model exhibited excellent calibration, confirmed by calibration graphs and the Hosmer–Lemeshow test (P = 0.551 and 0.544). Conclusion The LR model accurately predicts neoplastic GBP > 10 mm preoperatively. Radiomics with CEUS is a powerful tool for analysis of GBP > 10 mm. The model not only improves diagnostic accuracy and reduces healthcare costs but also optimizes patient management through personalized treatment plans, enhancing clinical outcomes and ensuring resources are more precisely allocated to patients who need surgery. Key results The final radiomic features, post-Lasso selection, were fed into various machine-learning algorithms including SVM, LR, MLP, KNN, and XGBoost, facilitating the formulation of risk models. The subsequent development of a radiomics nomogram, which amalgamates Rad Signature with clinical indicators, exhibited an AUC of 0.95 (95% CI: 0.90–0.99). Furthermore, sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy were established to be 94.4%, 84.6%, 63%, 98.2%, and 63%, respectively. CEUS radiomic signatures had an AUC of 0.89 (95% CI: 0.82–0.97), outperforming US radiomic signatures (AUC: 0.85, 95% CI: 0.75–0.94) in predicting neoplastic GBP > 10 mm, offering valuable clinical insights.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Adult / Aged / Antimitotic agents / Antineoplastic agents / Biomedicine / Contrast Media / Contrast-enhanced ultrasound / Female / Gallbladder - diagnostic imaging / Gallbladder - pathology / Gallbladder cancer / Gallbladder Neoplasms - diagnostic imaging / Gallbladder Neoplasms - pathology / Gallbladder Neoplasms - surgery / Gallbladder polyps / Health aspects / Humans / Infectious Diseases / Internal Medicine / Machine learning / Male / Medicine / Medicine & Public Health / Middle Aged / Nomograms / Oncology / Polyps - diagnostic imaging / Polyps - pathology / Polyps - surgery / Radiomics / ROC Curve / Surgery / Ultrasonography - methods