PDF(Pubmed)
Abstract：
BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models.
METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification.
RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92.
CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model\'s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat\'s impact on radiomic features in medical imaging.
METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification.
RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92.
CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model\'s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat\'s impact on radiomic features in medical imaging.
摘要：
背景：这项研究调查了战斗补偿方法是否可以消除从不同扫描仪提取的放射学特征的变异性，同时还检查了其对机器学习模型后续预测性能的影响。
方法：从西门子制造的三台扫描仪中收集并筛选了135张Credence盒式放射体模的CT图像，飞利浦,和GE。根据Lasso回归方法提取100个影像组学特征，筛选出20个影像组学特征。从墨盒中的橡胶和树脂填充区域提取的放射学特征被标记为不同的类别，以评估机器学习模型的性能。根据不同的扫描仪制造商，影像组学功能分为三组。将放射学特征随机分为训练集和测试集，比例为8：2。五种机器学习模型(套索，逻辑回归，随机森林,支持向量机，神经网络）用于评估战斗对放射学特征的影响。使用方差分析（ANOVA）和主成分分析（PCA）评估影像组学特征之间的变异性。准确性,精度,召回，和受试者曲线下面积(AUC)作为模型分类的评价指标.
结果：主成分和方差分析结果表明，消除了不同扫描仪制造商在影像组学特征上的变异性（P﹤0.05）。与战斗算法协调后，影像组学特征的分布在位置和尺度上是一致的.改进了机器学习模型的分类性能，随机森林模型显示出最显著的增强。AUC值从0.88增加到0.92。
结论：战斗算法减少了来自不同扫描仪的放射学特征的变异性。在幻像CT数据集中，看来，机器学习模型的分类性能可能在战斗协调后有所改善。然而,需要进一步的调查和验证，以充分了解战斗对医学成像中放射学特征的影响。
方法：从西门子制造的三台扫描仪中收集并筛选了135张Credence盒式放射体模的CT图像，飞利浦,和GE。根据Lasso回归方法提取100个影像组学特征，筛选出20个影像组学特征。从墨盒中的橡胶和树脂填充区域提取的放射学特征被标记为不同的类别，以评估机器学习模型的性能。根据不同的扫描仪制造商，影像组学功能分为三组。将放射学特征随机分为训练集和测试集，比例为8：2。五种机器学习模型(套索，逻辑回归，随机森林,支持向量机，神经网络）用于评估战斗对放射学特征的影响。使用方差分析（ANOVA）和主成分分析（PCA）评估影像组学特征之间的变异性。准确性,精度,召回，和受试者曲线下面积(AUC)作为模型分类的评价指标.
结果：主成分和方差分析结果表明，消除了不同扫描仪制造商在影像组学特征上的变异性（P﹤0.05）。与战斗算法协调后，影像组学特征的分布在位置和尺度上是一致的.改进了机器学习模型的分类性能，随机森林模型显示出最显著的增强。AUC值从0.88增加到0.92。
结论：战斗算法减少了来自不同扫描仪的放射学特征的变异性。在幻像CT数据集中，看来，机器学习模型的分类性能可能在战斗协调后有所改善。然而,需要进一步的调查和验证，以充分了解战斗对医学成像中放射学特征的影响。
