利用常规MRI进行帕金森病的早期筛查:一项使用t2加权FLAIR成像的多中心机器学习研究

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Junyan Fu, Hongyi Chen, Chengling Xu, Zhongzheng Jia, Qingqing Lu, Haiyan Zhang, Yue Hu, Kun Lv, Jun Zhang, Daoying Geng
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引用次数: 0

摘要

目的:探讨T2W FLAIR图像放射组学特征在区分特发性帕金森病(PD)患者和健康对照(hc)中的潜力。方法:回顾性分析5个队列中1727名受试者的T2W FLAIR图像,分为训练集(395 PD/574 HC)、内部测试集(99 PD/144 HC)和外部测试集(295 PD/220 HC)。感兴趣的区域(roi),包括双侧苍白球(GP)、壳核(PU)、黑质(SN)和红核(RN),被手工划定。从roi中提取放射组学特征。在训练集上训练6个独立的机器学习(ML)分类器,并在内部和外部测试集上进行验证。结果:选择5个、2个、3个和10个高度相关的诊断特征分别从SN、RN、GP和PU区确定。基于20个放射组学特征实现了6个ML分类器。在内测队列中,6个模型的AUC为0.96 ~ 0.98,准确率为0.80 ~ 0.90。在外部测试队列中,多层感知器模型显示出最高的AUC为0.85 (95% CI: 0.80-0.89),准确率为0.78。结论:基于常规T2W FLAIR图像的ML模型对PD具有较好的诊断效果,可为进一步研究ML方法对PD的诊断奠定基础。关键相关性声明:我们的研究证实,在大型多中心队列中,借助机器学习算法,基于常规T2W FLAIR图像进行帕金森病早期筛查是可行的,并且这些模型具有一定的通用性。重点:常规头部MRI是帕金森病(PD)的常规检查,但诊断特异性不足。基于传统T2W FLAIR图像的机器学习(ML)模型对PD诊断具有良好的准确性。ML算法可以在常规T2W FLAIR序列上早期筛查PD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing routine MRI for the early screening of Parkinson's disease: a multicenter machine learning study using T2-weighted FLAIR imaging.

Objective: To explore the potential of radiomics features derived from T2-weighted fluid-attenuated inversion recovery (T2W FLAIR) images to distinguish idiopathic Parkinson's disease (PD) patients from healthy controls (HCs).

Methods: T2W FLAIR images from 1727 subjects were retrospectively obtained from five cohorts and divided into a training set (395 PD/574 HC), an internal test set (99 PD/144 HC) and an external test set (295 PD/220 HC). Regions of interest (ROIs), including bilateral globus pallidus (GP), putamen (PU), substantia nigra (SN), and red nucleus (RN), were manually delineated. The radiomics features were extracted from ROIs. Six independent machine learning (ML) classifiers were trained on the training set, and validated on the internal and external test sets.

Results: A selection of five, two, three, and ten highly correlated diagnostic features were identified from SN, RN, GP, and PU regions, respectively. Six ML classifiers were implemented based on the combined 20 radiomics features. In the internal test cohort, the six models achieved AUC of 0.96-0.98 with the accuracy ranging from 0.80 to 0.90. In the external test cohort, the multilayer perceptron model demonstrated the highest AUC of 0.85 (95% CI: 0.80-0.89) with an accuracy of 0.78.

Conclusion: ML models based on the conventional T2W FLAIR images demonstrated promising diagnostic performance for PD and those models may serve as a basis for future investigations on PD diagnosis with the aid of ML methods.

Critical relevance statement: Our study confirmed that early screening of Parkinson's Disease based on the conventional T2W FLAIR images was feasible with the aid of machine learning algorithms in a large multicenter cohort and those models had certain generalization.

Key points: Conventional head MRI is routinely performed in Parkinson's disease (PD) but exhibits inadequate specificity for diagnosis. Machine learning (ML) models based on conventional T2W FLAIR images showed favorable accuracy for PD diagnosis. ML algorithm enables early screening of PD on routine T2W FLAIR sequence.

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来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
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