脑转移瘤的磁共振成像放射学特征稳定性:图像预处理、图像和特征级协调的影响

IF 3.4 Q2 ONCOLOGY
Zahra Khodabakhshi, Hubert Gabrys, Philipp Wallimann, Matthias Guckenberger, Nicolaus Andratschke, Stephanie Tanadini-Lang
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引用次数: 0

摘要

背景和目的磁共振成像(MRI)扫描对采集和重建参数高度敏感,这影响了放射学研究中特征的稳定性和模型的普适性。这项工作旨在研究图像预处理和协调方法对脑转移(BMs)患者脑磁共振成像放射学特征稳定性和放射学模型预测性能的影响。第一个数据集包含 25 名脑转移瘤患者在两个不同时间点的扫描数据,用于特征稳定性分析。研究了灰度离散化(GLD)、强度归一化(Z-score、Nyul、WhiteStripe 和内部开发的名为 N-Peaks 的方法)和 ComBat 协调对特征稳定性的影响,并将类内相关系数为 0.8 的特征视为稳定特征。第二个数据集包含 64 名 BMs 患者,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原子模型性能的影响。结果与固定分区大小(FBS)离散化相比,应用固定分区大小(FBN)GLD 得到的稳定特征数量更高(10 ± 5.5 %)。特征域中的协调提高了采用 Z-score 和 WhiteStripe 方法的非归一化和归一化图像的稳定性。结论为了开发基于 MRI 的稳健放射模型,我们建议使用基于参考组织(如 N-Peaks)的强度归一化方法,然后使用 FBS 离散化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization

Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization

Background and purpose

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).

Materials and methods

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.

Results

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.

Conclusions

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.

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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
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