背景:肾冷缺血再灌注损伤(CIRI),肾移植的病理过程,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。缺乏评估CIRI程度的准确且非侵入性的工具。多参数MRI已广泛用于检测和评估肾损伤。机器学习算法引入了将来自不同MRI度量的生物标志物组合成单个分类器的机会。
目的:使用机器学习方法评估多参数磁共振成像在肾脏冷缺血再灌注损伤大鼠模型中的肾脏损伤分级性能。
方法:选用80只雄性SD大鼠建立肾脏冷缺血再灌注模型,并全部进行了多参数MRI扫描(DWI,IVIM,DKI,大胆,T1映射和ASL),然后进行病理分析。共分析肾皮质和髓质的25个参数作为特征。采用K-means聚类方法将病理评分分为3组。Lasso回归应用于特征的初始选择。获得了病理分级的最佳特征和最佳技术。使用多个分类器构建模型以评估病理分级的预测值。
结果:所有大鼠均分为轻度,中度,根据病理评分和严重损伤组。与病理分类相关较好的8个特征是髓质和皮质Dp,皮层T2*,皮质FP,髓质T2*,ΔT1,皮质RBF,髓质T1。Logistic回归的病理分类的准确性(分别为0.83、0.850、0.81)和AUC(分别为0.95、0.93、0.90),SVM,和RF显著高于其他分类器。对于逻辑模型和组合逻辑,不同病理分级技术的RF和SVM模型,稳定和表现都很好。基于逻辑回归,IVIM的病理分级AUC最高(0.93),其次是BOLD(0.90)。
结论:基于多参数MRI的机器学习模型对于肾损伤程度的无创性评估是有价值的。
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).
CONCLUSIONS: The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.