Non-invasive classification of non-neoplastic and neoplastic gallbladder polyps based on clinical imaging and ultrasound radiomics features: An interpretable machine learning model.

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Abstract

Gallbladder (GB) adenomas, precancerous lesions for gallbladder carcinoma (GBC), lack reliable non-invasive tools for preoperative differentiation of neoplastic polyps from cholesterol polyps. This study aimed to evaluate an interpretable machine learning (ML) combined model for the precise differentiation of the pathological nature of gallbladder polyps (GPs). This study consecutively enrolled 744 patients from Xi'an Jiaotong University First Affiliated Hospital between January 2017 and December 2023 who were pathologically diagnosed postoperatively with cholesterol polyps, adenomas or T1-stage GBC. Radiomics features were extracted and selected, while clinical variables were subjected to univariate and multivariate logistic regression analyses to identify significant predictors of neoplastic polyps. A optimal ML-based radiomics model was developed, and separate clinical, US and combined models were constructed. Finally, SHapley Additive exPlanations (SHAP) was employed to visualize the classification process. The areas under the curves (AUCs) of the CatBoost-based radiomics model were 0.852 (95 % CI: 0.818-0.884) and 0.824 (95 % CI: 0.758-0.881) for the training and test sets, respectively. The combined model demonstrated the best performance with an improved AUC of 0.910 (95 % CI: 0.885-0.934) and 0.869 (95 % CI: 0.812-0.919), outperformed the clinical, radiomics, and US model (all P < 0.05), and reduced the rate of unnecessary cholecystectomies. SHAP analysis revealed that the polyp short diameter is a crucial independent risk factor in predicting the nature of the GPs. The ML-based combined model may be an effective non-invasive tool for improving the precision treatment of GPs, utilizing SHAP to visualize the classification process can enhance its clinical application.

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