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

Autor: Panahi M; Department of Computer Engineering, Payame Noor University Erbil Branch, Erbil, Iraq. mitipanahi@gmail.com., Habibi M; Department of Computer Engineering, Payame Noor University Birjand Branch, Birjand, Iran., Hosseini MS; Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
Jazyk: angličtina
Zdroj: Magma (New York, N.Y.) [MAGMA] 2024 Nov 28. Date of Electronic Publication: 2024 Nov 28.
DOI: 10.1007/s10334-024-01215-1
Abstrakt: 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.
Competing Interests: Declarations. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethical standards: All ethical requirements have been met in this study. The data used was obtained from the publicly available Parkinson's Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/access-data-specimens/download-data ), which follows standardized protocols and ethical guidelines for data collection and sharing. All data used in this study was de-identified and collected with appropriate patient consent through PPMI's established ethical frameworks. No additional ethical approval was required for this retrospective analysis of publicly available data.
(© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)
Databáze: MEDLINE
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