Panahi Mehdi, Habibi Maliheh, Hosseini Mahboube Sadat
Department of Computer Engineering, Payame Noor University Erbil Branch, Erbil, Iraq.
Department of Computer Engineering, Payame Noor University Birjand Branch, Birjand, Iran.
MAGMA. 2025 Feb;38(1):23-35. doi: 10.1007/s10334-024-01215-1. Epub 2024 Nov 28.
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.
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.
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.
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.
本研究旨在评估磁共振成像（MRI）衍生的放射组学特征在多个灰度离散化水平上对帕金森病（PD）亚型进行分类的可重复性，并评估ComBat归一化对特征稳定性和机器学习性能的影响。
从帕金森病进展标志物计划（PPMI）数据库中获取了140例PD患者（70例震颤为主型，70例姿势不稳步态障碍型）和70名健康对照者的T1加权MRI扫描图像。使用6个离散化水平（8、16、32、64、128和256个区间）从16个脑区提取放射组学特征。使用结合了扫描仪型号和离散化水平的组合批次变量应用ComBat归一化。组内相关系数（ICC）和Kruskal-Wallis检验评估归一化前后的特征可重复性。使用支持向量机分类器进行PD亚型分类。
ComBat归一化显著提高了所有特征组的特征可重复性。归一化后显示出优异稳健性（ICC≥0.90）的特征百分比大幅增加。归一化后，受离散化水平显著影响的特征比例降低。分类准确率显著提高，在大多数离散化水平上，从归一化前的0.42-0.49提高到归一化后的0.86-0.96。AUC值同样从归一化前的0.60-0.67增加到0.93-0.99。
ComBat归一化显著提高了放射组学特征在离散化水平上的可重复性，并改善了PD亚型分类性能。本研究强调了归一化在PD放射组学研究中的重要性，并提出了在个性化治疗计划中的潜在临床应用。
