Wang Shijing, Chang Jie, Yang Caiyu, Rao Rao, Wu Tong, Wang Jiawei, Sun Dandan, Xia Kun, Wang Xun, Han Yongzhu, Han Yongsheng
Institute of Neurology, Anhui University of Traditional Chinese Medicine, Hefei, 230038, China; Affiliated Hospital of Institute of Neurology, Anhui Univesity of Traditional Chinese Medicine, No. 357, Changjiang Middle Road, Hefei, 230038, China.
Wannan Medical College, Wuhu, 241002, China.
Parkinsonism Relat Disord. 2025 Aug;137:107908. doi: 10.1016/j.parkreldis.2025.107908. Epub 2025 Jun 4.
This study aims to build a machine learning (ML) model to predict the deterioration of neurological symptoms in Wilson's disease (WD) patients during short-term anti-copper therapy. The model combines brain T1WI MRI radiomics with clinical features and employs SHapley Additive exPlanations (SHAP) to interpret the contributions of the features.
Automated segmentation techniques were used to delineate regions of interest (ROI) in routine brain T1WI MRI scans from 107 WD cases. Radiomics features were extracted and screened using LASSO regression. Six ML models were trained to develop a predictive model for symptom deterioration during short-term anti-copper therapy. The SHAP method was applied to identify and explain the importance of the features in the ML models.
Significant correlations were observed between UWDRS-N scores, Course of disease, age, and radiomics features of the brainstem, caudate, and corpus callosum (p < 0.05). The ML models incorporated 18 radiomics and 9 clinical features. Six ML models were used in the training and test set, the best performing model was XGBoost, with AUC values of 0.96 and 0.94, respectively. SHAP analysis revealed that the five most important features were the UWDRS-N score, age, right putamen GrayLevelNonUniformityNormalized, right caudate ZoneEntropy, and central corpus callosum SmallDependenceEmphasis. The SHAP force plot illustrated how the XGBoost model predicted neurological symptom deterioration at the patient level.
An explainable XGBoost model was successfully developed using brain T1WI MRI radiomics and clinical features. This model identifies WD patients at risk of neurological symptom deterioration during anti-copper therapy and provides insight into the contributions of individual features.
本研究旨在构建一种机器学习（ML）模型，以预测肝豆状核变性（WD）患者在短期抗铜治疗期间神经症状的恶化情况。该模型将脑T1WI MRI影像组学与临床特征相结合，并采用SHapley加性解释（SHAP）来解释特征的贡献。
使用自动分割技术在107例WD患者的常规脑T1WI MRI扫描中划定感兴趣区域（ROI）。采用LASSO回归提取并筛选影像组学特征。训练六个ML模型，以建立短期抗铜治疗期间症状恶化的预测模型。应用SHAP方法识别并解释ML模型中特征的重要性。
观察到统一威尔逊病评定量表-神经（UWDRS-N）评分、病程、年龄与脑干、尾状核和胼胝体的影像组学特征之间存在显著相关性（p < 0.05）。ML模型纳入了18个影像组学特征和9个临床特征。在训练集和测试集中使用了六个ML模型，表现最佳的模型是XGBoost，其AUC值分别为0.96和0.94。SHAP分析显示，五个最重要的特征是UWDRS-N评分、年龄、右侧壳核灰度非均匀性标准化、右侧尾状核区域熵和胼胝体中部小依赖性强调。SHAP力场图展示了XGBoost模型在患者层面预测神经症状恶化的方式。
利用脑T1WI MRI影像组学和临床特征成功开发了一个可解释的XGBoost模型。该模型可识别抗铜治疗期间有神经症状恶化风险的WD患者，并深入了解各个特征的贡献。
