Diffusion Kurtosis Imaging in Diagnosing Parkinson's Disease: A Preliminary Comparison Study Between Kurtosis Metric and Radiomic Features.

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-08-08 DOI:10.1016/j.acra.2024.07.001
Ninggui Zhang, Wei Zhao, Song'an Shang, Hongying Zhang, Xiang Lv, Lanlan Chen, Weiqiang Dou, Jing Ye
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Abstract

Rationale and objectives: Parkinson's disease (PD) shows small structural changes in nigrostriatal pathways, which can be sensitively captured through diffusion kurtosis imaging (DKI). However, the value of DKI and its radiomic features in the classification performance of PD still need confirmation. This study aimed to compare the diagnostic efficiency of DKI-derived kurtosis metric and its radiomic features with different machine learning models for PD classification.

Materials and methods: 75 people with PD and 80 healthy individuals had their brains scanned using DKI. These images were pre-processed and the standard atlas were non-linearly registered to them. With the labels in atlas, different brain regions in nigrostriatal pathways, including the caudate nucleus, putamen, pallidum, thalamus, and substantia nigra, were chosen as the region of interests (ROIs) to warped to the native space to measure the mean kurtosis (MK). Additionally, new radiomic features were developed for comparison. To handle the large amount of data, a statistical method called Z-score normalization and another method called LASSO regression were used to simplify the information. From this, a few most important features were chosen, and a combined score called Radscore was calculated using LASSO regression. For the comprehensive analyses, three different conventional machine learning models were then created: logistic regression (LR), support vector machine (SVM), and random forest (RF). To ensure the models were accurate, a process called 10-fold cross-validation was used, where the data were split into 10 parts for training and testing.

Results: Using MK alone, the models achieved good results in correctly identifying PD in the validation set, with LR at 0.90, RF at 0.93, and SVM at 0.90. When the radiomic features were added, the models performed even better, with LR at 0.92, RF at 0.95, and SVM at 0.91. Additionally, a nomogram combining all the information was created to predict the likelihood of someone having PD, which had an AUC of 0.91.

Conclusion: These findings suggest that the combination of DKI measurements and radiomic features can effectively diagnose PD by providing more detailed information about the brain's condition and the processes involved in the disease.

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诊断帕金森病的弥散峰度成像:峰度指标与放射学特征的初步比较研究。
理由和目标:帕金森病(PD)在黑质通路中表现出微小的结构变化,通过弥散峰度成像(DKI)可以灵敏地捕捉到这些变化。然而,DKI 及其放射学特征在帕金森病分类中的价值仍有待确认。本研究旨在比较 DKI 导出的峰度指标及其放射学特征与不同机器学习模型在 PD 分类中的诊断效率。这些图像经过预处理,并与标准图集进行非线性配准。根据图谱中的标签,选择黑质通路中的不同脑区,包括尾状核、丘脑、苍白球、丘脑和黑质,作为感兴趣区(ROIs),将其扭曲到本机空间,以测量平均峰度(MK)。此外,还开发了新的放射学特征进行比较。为了处理大量数据,我们使用了一种名为 Z 分数归一化的统计方法和另一种名为 LASSO 回归的方法来简化信息。从中挑选出几个最重要的特征,并使用 LASSO 回归法计算出一个名为 Radscore 的综合分数。为了进行综合分析,我们创建了三种不同的传统机器学习模型:逻辑回归(LR)、支持向量机(SVM)和随机森林(RF)。为确保模型的准确性,使用了一种称为 10 倍交叉验证的方法,即将数据分成 10 部分进行训练和测试:结果:仅使用 MK,模型在验证集中正确识别出腹膜透析方面取得了良好的效果,LR 为 0.90,RF 为 0.93,SVM 为 0.90。加入放射学特征后,模型的表现更加出色,LR 为 0.92,RF 为 0.95,SVM 为 0.91。此外,我们还创建了一个结合所有信息的提名图,用于预测罹患帕金森病的可能性,其AUC为0.91:这些研究结果表明,结合 DKI 测量和放射学特征可以提供有关大脑状况和疾病过程的更详细信息,从而有效诊断帕金森病。
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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