Repeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-04-07 DOI:10.1016/j.cmpb.2025.108738

Noemi Pisani , Michela Destito , Carlo Ricciardi , Maria Teresa Pellecchia , Mario Cesarelli , Fabrizio Esposito , Maria Francesca Spadea , Francesco Amato

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引用次数: 0

Abstract

Background and Objective:

Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul.

Methods:

Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics.

Results:

Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82–0.98)) and CCC (0.82 (0.69–0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84–0.92)) and (0.80 (0.73–0.84)), respectively.

Conclusion:

Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis.

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图像强度归一化后脑T1-W MRI放射特征的可重复性：对结构性神经变性纵向研究的意义

背景和目的：放射组学从磁共振图像（MRI）中提取定量特征，在人体软组织中存在细微病理变化时特别有用。然而，这种情况可能不包括固有的影响，例如，与衰老相关的正常脑组织的（生理性）神经变性。这项工作的目的是研究从纵向获取的t1加权MR脑图像中提取的放射学特征的可重复性，使用放射组学中通常使用的三种不同的强度归一化方法：Z-score， WhiteStripe和Nyul。方法：在六个月内的两个不同时间点反复获得59张听力受损但认知完整的患者图像。根据以往的分析和报道，我们在脑桥区域内获得了91个放射学特征，认为这是一个健康的脑组织。以重复性研究中的类内相关系数（Intraclass Correlation Coefficient， ICC）和一致性相关系数（Concordance Correlation Coefficient， CCC）作为指标。结果：用Z-score归一化的MRI提取的特征，在ICC（0.90(0.82 - 0.98)）和CCC（0.82(0.69-0.95)）分布值的中位数和四分位数方面，与用WhiteStripe归一化的图像提取的特征（0.89(0.84-0.92)）和（0.80(0.73-0.84)）具有可比性。结论：我们的研究结果强调了纵向放射学分析之前脑MRI强度归一化的重要性，并为其提供了有用的指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.