Yang Qifan, Nie Lu, Xu Jian, Li Hua, Zhu Xin, Wei Mingwei, Yao Jun
Department of Gastroenterology, The Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Department of Intervention Vascular, Wujin Hospital Affiliated with Jiangsu University, Changzhou, China.
Front Oncol. 2024 Jul 29;14:1406512. doi: 10.3389/fonc.2024.1406512. eCollection 2024.
Biliary stricture caused by malignant tumors is known as Malignant Biliary Stricture (MBS). MBS is challenging to differentiate clinically, and accurate diagnosis is crucial for patient prognosis and treatment. This study aims to identify the risk factors for malignancy in all patients diagnosed with biliary stricture by Endoscopic Retrograde Cholangiopancreatography (ERCP), and to develop an effective clinical predictive model to enhance diagnostic outcomes.
Through a retrospective study, data from 398 patients diagnosed with biliary stricture using ERCP between January 2019 and January 2023 at two institutions: the First People's Hospital affiliated with Jiangsu University and the Second People's Hospital affiliated with Soochow University. The study began with a preliminary screening of risk factors using univariate regression. Lasso regression was then applied for feature selection. The dataset was divided into a training set and a validation set in an 8:2 ratio. We analyzed the selected features using seven machine learning algorithms. The best model was selected based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUROC) and other evaluation indicators. We further evaluated the model's accuracy using calibration curves and confusion matrices. Additionally, we used the SHAP method for interpretability and visualization of the model's predictions.
RF model is the best model, achieved an AUROC of 0.988. Shap result indicate that age, stricture location, stricture length, carbohydrate antigen 199 (CA199), total bilirubin (TBil), alkaline phosphatase (ALP), (Direct Bilirubin) DBil/TBil, and CA199/C-Reactive Protein (CRP) were risk factors for MBS, and the CRP is a protective factor.
The model's effectiveness and stability were confirmed, accurately identifying high-risk patients to guide clinical decisions and improve patient prognosis.
由恶性肿瘤引起的胆管狭窄被称为恶性胆管狭窄（MBS）。MBS在临床上难以鉴别，准确诊断对患者的预后和治疗至关重要。本研究旨在确定所有经内镜逆行胰胆管造影（ERCP）诊断为胆管狭窄的患者发生恶性病变的危险因素，并建立有效的临床预测模型以提高诊断效果。
通过回顾性研究，收集了2019年1月至2023年1月期间在江苏大学附属第一人民医院和苏州大学附属第二人民医院这两家机构中398例经ERCP诊断为胆管狭窄患者的数据。该研究首先使用单变量回归对危险因素进行初步筛选。然后应用套索回归进行特征选择。数据集按8:2的比例分为训练集和验证集。我们使用七种机器学习算法分析所选特征。根据受试者操作特征曲线（ROC）下面积（AUROC）和其他评估指标选择最佳模型。我们使用校准曲线和混淆矩阵进一步评估模型的准确性。此外，我们使用SHAP方法对模型预测进行可解释性分析和可视化。
随机森林（RF）模型是最佳模型，AUROC为0.988。SHAP结果表明，年龄、狭窄部位、狭窄长度、糖类抗原199（CA199）、总胆红素（TBil）、碱性磷酸酶（ALP）、直接胆红素（DBil）/TBil以及CA199/C反应蛋白（CRP）是MBS的危险因素，而CRP是保护因素。
该模型的有效性和稳定性得到证实，能够准确识别高危患者，以指导临床决策并改善患者预后。
