神经母细胞瘤 T2 加权核磁共振成像处理和分割交替后放射学特征的再现性分析

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Diana Veiga-Canuto, Matías Fernández-Patón, Leonor Cerdà Alberich, Ana Jiménez Pastor, Armando Gomis Maya, Jose Miguel Carot Sierra, Cinta Sangüesa Nebot, Blanca Martínez de Las Heras, Ulrike Pötschger, Sabine Taschner-Mandl, Emanuele Neri, Adela Cañete, Ruth Ladenstein, Barbara Hero, Ángel Alberich-Bayarri, Luis Martí-Bonmatí
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Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (<i>P</i> < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. 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引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现影响内容的错误。目的 评估神经母细胞瘤患者从 T2 加权磁共振成像中提取的放射组学特征的可重复性。材料与方法 一项回顾性研究纳入了 419 例神经母细胞瘤患者(平均(标清)年龄 29(34)岁;男性 220 例,女性 199 例),这些患者在 2002-2023 年间被诊断出患有神经母细胞瘤,属于 PRIMAGE 项目的研究范围,涉及诊断时和/或初始化疗后的 746 个 MRI T2/T2* 加权序列。图像经过了处理步骤(去噪、不均匀偏倚场校正、归一化和重采样)。自动分割肿瘤并提取 107 个形状、一阶和二阶放射学特征,作为参考标准。随后,修改了之前的图像处理设置,并应用了容积掩膜。提取新的放射组学特征并与参考标准进行比较。使用一致性相关系数(CCC)评估再现性,使用变异系数(CoV)测量受试者内的可重复性。结果 省略归一化后,只有 5%的放射组学特征显示出较高的可重复性。统计分析表明,归一化和重新取样过程发生了重大变化(P < .001)。去除不均匀性对放射组学的影响最小(83%的参数保持稳定)。掩膜修改后,形状特征保持稳定,CCC > 0.90。掩膜修改是获得高 CCC 值最有利的修改,70% 的放射组学特征保持稳定。只有 7% 的二阶放射组学特征显示出小于 0.10 的出色 CoV。结论 神经母细胞瘤患者T2加权磁共振成像制备过程的改变会导致放射组学特征的变化,而正常化被认为是对可重复性影响最大的因素。去除不均匀性对放射组学特征的影响最小。©RSNA,2024。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors.

Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 patients (mean age, 29 months ± 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features. Keywords: Pediatrics, MR Imaging, Oncology, Radiomics, Reproducibility, Repeatability, Neuroblastic Tumors Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Safdar and Galaria in this issue.

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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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