Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-11-28 DOI:10.1007/s10334-024-01215-1
Mehdi Panahi, Maliheh Habibi, Mahboube Sadat Hosseini
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Abstract

Objective: This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance.

Methods: T1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype classification.

Results: ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. Classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after harmonization.

Conclusions: ComBat harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

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提高帕金森病核磁共振成像放射组学特征的可重复性和分类性能:灰度离散化变异的协调方法。
研究目的本研究旨在评估用于帕金森病(PD)亚型分类的多灰度离散化水平的 MRI 衍生放射学特征的可重复性,并评估 ComBat 协调对特征稳定性和机器学习性能的影响:从帕金森病进展标记物倡议(PPMI)数据库中获取了 140 名帕金森病患者(70 名震颤为主,70 名姿势不稳步态困难)和 70 名健康对照者的 T1 加权 MRI 扫描图像。使用 6 个离散化级别(8、16、32、64、128 和 256 个仓)从 16 个脑区提取放射线特征。使用包含扫描仪模型和离散化水平的组合批次变量进行 ComBat 协调。类内相关系数 (ICC) 和 Kruskal-Wallis 检验评估了协调前后的特征再现性。支持向量机分类器用于PD亚型分类:结果:ComBat协调大大提高了所有特征组的特征再现性。协调后,表现出卓越稳健性(ICC ≥ 0.90)的特征比例大幅增加。协调后,受离散化水平显著影响的特征比例有所降低。在大多数离散化水平上,分类准确率从协调前的 0.42-0.49 大幅提高到协调后的 0.86-0.96。AUC值也同样从协调后的0.60-0.67提高到0.93-0.99:结论:ComBat统一化大大提高了不同离散化水平放射学特征的可重复性,并改善了PD亚型分类性能。这项研究强调了协调PD放射组学研究的重要性,并提出了个性化治疗计划的潜在临床应用。
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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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