PDF(Pubmed)
Abstract：
UNASSIGNED: Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs).
UNASSIGNED: Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance.
UNASSIGNED: Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization.
UNASSIGNED: To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
UNASSIGNED: Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance.
UNASSIGNED: Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization.
UNASSIGNED: To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
摘要：
■磁共振成像（MRI）扫描对采集和重建参数高度敏感，这些参数会影响放射学研究中的特征稳定性和模型泛化性。这项工作旨在研究图像预处理和协调方法对脑转移瘤（BMs）患者脑MRI影像组学特征的稳定性和影像组学模型的预测性能的影响。
■本研究使用了两个T1对比增强脑MRI数据集。第一个包含25名BMs患者，在两个不同的时间点进行扫描，并用于特征稳定性分析。灰度离散化（GLD）的影响，强度归一化(Z分数，Nyul,WhiteStripe,并在内部开发的名为N-Peaks的方法中），和ComBat协调对特征稳定性进行了研究，认为组内相关系数>0.8的特征是稳定的。包含64名BMs患者的第二个数据集用于分类任务，以研究稳定特征的信息量以及协调方法对放射学模型性能的影响。
■应用固定箱编号(FBN)GLD，与固定箱大小(FBS)离散化相比，稳定特征的数量更高(高10±5.5%)。特征域的协调使用Z分数和WhiteStripe方法提高了非归一化和归一化图像的稳定性。对于分类任务，保持稳定的特征仅对于具有N峰以及FBS离散化的归一化图像产生良好的性能。
■为了开发基于MRI的鲁棒放射学模型，我们建议使用基于参考组织的强度归一化方法（例如gN-Peaks)，然后使用FBS离散化。
■本研究使用了两个T1对比增强脑MRI数据集。第一个包含25名BMs患者，在两个不同的时间点进行扫描，并用于特征稳定性分析。灰度离散化（GLD）的影响，强度归一化(Z分数，Nyul,WhiteStripe,并在内部开发的名为N-Peaks的方法中），和ComBat协调对特征稳定性进行了研究，认为组内相关系数>0.8的特征是稳定的。包含64名BMs患者的第二个数据集用于分类任务，以研究稳定特征的信息量以及协调方法对放射学模型性能的影响。
■应用固定箱编号(FBN)GLD，与固定箱大小(FBS)离散化相比，稳定特征的数量更高(高10±5.5%)。特征域的协调使用Z分数和WhiteStripe方法提高了非归一化和归一化图像的稳定性。对于分类任务，保持稳定的特征仅对于具有N峰以及FBS离散化的归一化图像产生良好的性能。
■为了开发基于MRI的鲁棒放射学模型，我们建议使用基于参考组织的强度归一化方法（例如gN-Peaks)，然后使用FBS离散化。
