Zhou Qiao-Mei, Liu Chuan-Xian, Zhou Jia-Ping, Yu Jie-Ni, Wang You, Wang Xiao-Jie, Xu Jian-Xia, Yu Ri-Sheng
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Radiology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, China.
Front Oncol. 2022 Feb 24;12:792077. doi: 10.3389/fonc.2022.792077. eCollection 2022.
Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making.
We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model.
Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC.
The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.
黄色肉芽肿性胆囊炎（XGC）是一种罕见的胆囊良性慢性炎症性疾病，有时与胆囊癌（GBC）难以区分，从而影响治疗方案的选择。因此，本研究旨在分析XGC和GBC的影像学特征，建立用于鉴别诊断和临床决策的诊断预测模型。
我们通过随机森林和逻辑回归确定影像学特征，以建立计算机断层扫描（CT）、磁共振成像（MRI）、CT/MRI模型及诊断预测模型，并进行受试者操作特征曲线（ROC）分析以验证诊断预测模型的有效性。
基于随机森林方法确定的最佳特征，CT模型和MRI模型的ROC曲线下平均面积（AUC）分别为0.817（平均准确率=0.837）和0.839（平均准确率=0.842），而CT/MRI模型具有相当的预测性能，平均AUC为0.897（平均准确率=0.906）。为方便临床应用而建立的诊断预测模型与CT/MRI模型相似，平均AUC和准确率分别为0.888和0.898，表明在区分XGC和GBC方面具有较好的诊断效率。
该诊断预测模型对术前鉴别XGC和GBC具有良好的诊断准确性，可能有助于临床决策。
