Mehdi Panahi, Maliheh Habibi, Mahboube Sadat Hosseini
{"title":"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。","authors":"Mehdi Panahi, Maliheh Habibi, Mahboube Sadat Hosseini","doi":"10.1007/s10334-024-01215-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.\",\"authors\":\"Mehdi Panahi, Maliheh Habibi, Mahboube Sadat Hosseini\",\"doi\":\"10.1007/s10334-024-01215-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":18067,\"journal\":{\"name\":\"Magnetic Resonance Materials in Physics, Biology and Medicine\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic Resonance Materials in Physics, Biology and Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10334-024-01215-1\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance Materials in Physics, Biology and Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10334-024-01215-1","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.
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.
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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