Wang Yubing, Qu Chao, Zeng Jiange, Jiang Yumin, Sun Ruitao, Li Changlei, Li Jian, Xing Chengzhi, Tan Bin, Liu Kui, Liu Qing, Zhao Dianpeng, Cao Jingyu, Hu Weiyu
Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Department of Hepatobiliary Surgery, Shandong Second Medical University, No.7166, Baotong West Street, Weicheng District, Weifang, Shandong Province, 261053, China.
World J Surg Oncol. 2025 Jan 28;23(1):27. doi: 10.1186/s12957-025-03671-y.
With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data. Using 12 machine learning algorithms, 110 combination predictive models were constructed. The models were evaluated using internal and external cohort validation, receiver operating characteristic curves, area under the curve (AUC) values, calibration curves, and clinical decision curves to determine the best predictive model.
Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. The model-selected features included gallbladder wall thickness, polyp size, polyp echo, and pedicle. Evaluation through external cohort validation, calibration curves, and clinical decision curves further confirmed its excellent predictive ability for distinguishing gallbladder adenomas from cholesterol polyps. Additionally, this study identified age, adenosine deaminase level, and metabolic syndrome as potential predictive factors for gallbladder adenomas.
This study employed the machine learning combination algorithms and preoperative ultrasound imaging data to construct an SVM + RF predictive model, enabling effective preoperative differentiation of gallbladder adenomas and cholesterol polyps. These findings will assist clinicians in accurately assessing the risk of GPLs and providing personalised treatment strategies.
随着胆囊息肉样病变（GPLs）诊断率的上升，术前区分良性胆固醇息肉与恶性风险较高的胆囊腺瘤至关重要。本研究旨在建立一种能够使用机器学习算法准确区分胆囊腺瘤和胆固醇息肉的术前预测模型。
我们回顾性分析了患者的临床基线数据、血清学指标和超声成像数据。使用12种机器学习算法，构建了110个组合预测模型。通过内部和外部队列验证、受试者操作特征曲线、曲线下面积（AUC）值、校准曲线和临床决策曲线对模型进行评估，以确定最佳预测模型。
在110个组合预测模型中，支持向量机+随机森林（SVM+RF）模型在训练集和内部验证集中的AUC值分别最高，为0.972和0.922，表明具有最佳预测性能。模型选择的特征包括胆囊壁厚度、息肉大小、息肉回声和蒂部。通过外部队列验证、校准曲线和临床决策曲线进行评估，进一步证实了其区分胆囊腺瘤和胆固醇息肉的优异预测能力。此外，本研究确定年龄、腺苷脱氨酶水平和代谢综合征为胆囊腺瘤的潜在预测因素。
本研究采用机器学习组合算法和术前超声成像数据构建了SVM+RF预测模型，能够在术前有效区分胆囊腺瘤和胆固醇息肉。这些发现将有助于临床医生准确评估GPLs的风险并提供个性化治疗策略。
