基于多参数 MRI 的机器学习模型预测大鼠肾冷缺血再灌注损伤模型中肾损伤的病理分级。
Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.
机构信息
Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, No. 24 Fu Kang Road, Nan Kai District, Tianjin, 300192, China.
Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
出版信息
BMC Med Imaging. 2024 Jul 26;24(1):188. doi: 10.1186/s12880-024-01320-6.
BACKGROUND
Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier.
OBJECTIVE
To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach.
METHODS
Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading.
RESULTS
All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90).
CONCLUSION
The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.
背景
肾脏冷缺血再灌注损伤（CIRI）是肾移植过程中的一种病理过程，可能导致移植物功能延迟，并对移植物的存活和功能产生负面影响。目前缺乏一种准确且非侵入性的工具来评估 CIRI 的程度。多参数 MRI 已广泛用于检测和评估肾脏损伤。机器学习算法为将来自不同 MRI 指标的生物标志物组合到单个分类器中提供了机会。
目的
使用机器学习方法评估多参数磁共振成像在大鼠肾脏冷缺血再灌注损伤模型中评估肾脏损伤程度的性能。
方法
选择 80 只雄性 SD 大鼠建立肾脏冷缺血-再灌注模型，所有大鼠均行多参数 MRI 扫描（DWI、IVIM、DKI、BOLD、T1mapping 和 ASL），随后进行病理分析。共分析了 25 个肾皮质和髓质参数作为特征。采用 K-均值聚类法将病理评分分为 3 组。应用 Lasso 回归进行特征的初步选择。获得最佳特征和最佳病理分级技术。使用多个分类器构建模型以评估对病理分级的预测价值。
结果
所有大鼠均根据病理评分分为轻度、中度和重度损伤组。与病理分类相关性更好的 8 个特征为髓质和皮质 Dp、皮质 T2*、皮质 Fp、髓质 T2*、ΔT1、皮质 RBF、髓质 T1。逻辑回归、SVM 和 RF 模型的病理分类准确率（分别为 0.83、0.850、0.81）和 AUC（分别为 0.95、0.93、0.90）明显高于其他分类器。对于逻辑回归、RF 和 SVM 模型的不同技术的逻辑模型和组合逻辑，稳定性和性能都很好。基于逻辑回归，IVIM 对病理分级的 AUC（0.93）最高，其次是 BOLD（0.90）。
结论
基于多参数 MRI 的机器学习模型可用于评估肾脏损伤程度。
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