Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics.

Q3 Medicine
Yunus Soleymani, Peyman Sheikhzadeh, Mohammad Mohammadzadeh, Davood Khezerloo
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引用次数: 0

Abstract

Background: Radiomics with single Region of Interest (ROI) and single-sequence Magnetic Resonance Imaging (MRI) may facilitate the segmentation reproducibility and radiomics workflow due to a time-consuming and complicated delineation of that in multi-sequence MRI images.

Objective: This study aimed to evaluate the performance of the radiomics approach in grading glioma based on a single-ROI delineation as Gross Tumor Volume (GTV) in a single - sequence as contrast-enhanced T1-weighted MRI.

Material and methods: This retrospective study was conducted on contrast-enhanced T1 weighted (CE T1W) MRI images of 60 grade II and 60 grade III glioma patients. The GTV regions were manually delineated. Radiomics features were extracted per patient. The segmentation reproducibility of the robust features was evaluated in several repetitions of GTV delineation. Finally, a linear Support Vector Machine (SVM) assessed the classification performance of the robust features.

Results: Four significant robust features were selected for training the model (P-value<0.05). The average Intraclass Correlation Coefficient (ICC) of the four features was 0.96 in several repetitions of GTV delineation. The linear SVM model differentiated grades II and III of glioma with an Area Under the Curve (AUC) of 0.9 in the training group.

Conclusion: High predicting power for glioma grading can be achieved with radiomics analysis by a single-ROI delineated on a single-sequence MRI image (CE T1W). In addition, single-ROI segmentation can increase radiomics reproducibility.

简化预测胶质瘤分级的放射组学工作流程:一种快速、可重复的放射组学方法。
背景:单感兴趣区域(ROI)和单序列磁共振成像(MRI)的放射组学可以促进分割的可重复性和放射组学工作流程,因为在多序列MRI图像中需要花费大量时间和复杂的描述。目的:本研究旨在评估放射组学方法在胶质瘤分级中的性能,该方法基于单序列对比增强t1加权MRI的总肿瘤体积(GTV)的单一roi描绘。材料与方法:本研究对60例II级和60例III级胶质瘤患者的MRI增强T1加权(CE T1W)图像进行回顾性研究。GTV区域是手工划定的。提取每位患者的放射组学特征。在多次重复的GTV描述中评估了鲁棒特征的分割再现性。最后,利用线性支持向量机(SVM)对鲁棒特征的分类性能进行评估。结果:选择了四个显著的鲁棒性特征来训练模型(p值)结论:通过在单序列MRI图像(CE T1W)上描绘单个roi的放射组学分析,可以实现对胶质瘤分级的高预测能力。此外,单roi分割可以提高放射组学的再现性。
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
Journal of Biomedical Physics and Engineering
Journal of Biomedical Physics and Engineering Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
2.90
自引率
0.00%
发文量
64
审稿时长
10 weeks
期刊介绍: The Journal of Biomedical Physics and Engineering (JBPE) is a bimonthly peer-reviewed English-language journal that publishes high-quality basic sciences and clinical research (experimental or theoretical) broadly concerned with the relationship of physics to medicine and engineering.
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