Li Jiaxin, Du Yao, Huang Gaoming, Zhang Chiyu, Ye Zhenfeng, Zhong Jinghui, Xi Xiaoqing, Huang Yawei
Department of Urology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China.
Department of Cardiovascular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China.
iScience. 2024 Oct 23;27(12):110843. doi: 10.1016/j.isci.2024.110843. eCollection 2024 Dec 20.
Preoperative diagnosis of infection stones presents a significant clinical challenge. We developed a machine learning model to predict urinary infection stones using computed tomography (CT) values, enabling preoperative identification. In this study, we included 1209 patients who underwent urinary lithotripsy at our hospital. Seven machine learning algorithms along with eleven preoperative variables were used to construct the prediction model. Subsequently, model performance was evaluated by calculating AUC and AUPR for subjects in the validation set. On the validation set, all seven machine learning models demonstrated strong discrimination (AUC: 0.687-0.947). Additionally, the XGBoost model was identified as the optimal model significantly outperforming the traditional LR model. Taken together, the XGBoost model is the first machine learning model for preoperative prediction of infection stones based on CT values. It can rapidly and accurately identify infection stones , providing valuable guidance for urologists in managing these stones.
感染性结石的术前诊断是一项重大的临床挑战。我们开发了一种机器学习模型，利用计算机断层扫描（CT）值来预测尿路中的感染性结石，从而实现术前识别。在本研究中，我们纳入了在我院接受尿路碎石术的1209例患者。使用七种机器学习算法以及十一个术前变量构建预测模型。随后，通过计算验证集中受试者的AUC和AUPR来评估模型性能。在验证集上，所有七个机器学习模型均表现出较强的区分能力（AUC：0.687 - 0.947）。此外，XGBoost模型被确定为最优模型，显著优于传统的LR模型。综上所述，XGBoost模型是首个基于CT值进行感染性结石术前预测的机器学习模型。它能够快速、准确地识别感染性结石，为泌尿外科医生处理这些结石提供有价值的指导。
