Development of a grading diagnostic model for schistosomiasis-induced liver fibrosis based on radiomics and clinical laboratory indicators

Abstract

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators.

Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method.

Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests ( t = -5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features.

Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

Abstract

［摘要］ 目的 探索基于B型超声影像与临床实验室指标构建血吸虫病肝纤维化分级诊断模型的可行性。 方法 收集 2018—2022年江西省都昌县第二人民医院血吸虫病患者超声影像及临床实验室数据。以血吸虫病肝纤维化I级病例 为第1组, II级和III级病例为第2组; 选取2018—2021年病例数据为训练集、2022年病例数据为验证集, 构建机器学习二 分类模型。采用ITK-SNAP软件标记影像特征, 采用Python 3.7编程语言和PyRadiomics工具包提取影像特征。采用 t 检 验或Mann-Whitney U 检验比较两组样本间影像特征差异, 并采用套索算法 (least absolute shrinkage and selection operator, LASSO) 进行关键影像特征筛选。采用Scikit-learn机器学习库进行机器学习建模, 包括支持向量机 (support vector machine, SVM) 、随机森林 (random forest, RF) 、线性回归 (linear regression, LR) 和极端梯度提升 (extreme gradient boosting, XGBoost) 等4种模型。采用受试者工作特征曲线 (receiver operating characteristic curve, ROC) 进行最优机器学习模型筛 选, 并使用沙普利加和解释 (SHapley Additive exPlanations, SHAP) 评估对机器学习模型中超声影像鉴别特征贡献度最高 的特征。 结果 2019—2022年, 累计将491例血吸虫病患者超声影像和临床实验室检查数据纳入研究。累计提取了851 项影像组学特征和54项临床实验室指标, 经统计学检验 ( t = -5.98 ~ 4.80, U = 6 550 ~ 20 994, P 均< 0.05) 及LASSO回归 特征筛选, 纳入44项特征或指标进入下一步研究。临床实验室指标SVM机器学习模型训练集和验证集ROC曲线下面 积 (area under curve, AUC) 分别为0.763和0.611, 超声影像组学SVM机器学习模型训练集和验证集AUC分别为0.951和 0.892, 多模态SVM机器学习模型训练集和验证集AUC分别为0.960和0.913。机器学习模型中贡献度居前10位的特征 或指标包括2项临床实验室指标和8项影像组学特征。 结论 超声影像组学和临床实验室指标相结合的多模态机器学 习模型可用于血吸虫病肝纤维化智能识别, 并可提升单类数据模型的分类效果。