Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)

IF 2.9 2区工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-02-21 DOI:10.1007/s10278-024-01028-7

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Abstract

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.

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开发正常腹部和盆腔计算机断层扫描放射组学图集数据集 (RADAPT)

摘要为了了解健康和疾病中的生物表型，并为全面的全息比较研究奠定基础，正常基因组学、转录组学、蛋白质组学和代谢组学图谱已经出版。放射组学数据还没有这样的图集。本研究的目的是系统地创建一个正常腹部和盆腔放射组学数据集，用于模型开发和验证。研究人员回顾性地纳入了年龄在 17 岁到 36 岁之间、之前未患任何疾病的年轻成年人。所有患者均因紧急情况接受过 CT 扫描。如果发现异常，则排除相关的解剖结构。利用深度学习，使用 3DSlicer 中的 TotalSegmentator 模型自动分割大部分可见的解剖结构。利用 PyRadiomics 提取了放射组学特征，包括一阶、纹理、小波和高斯拉普拉斯变换特征。我们创建了一个 Github 存储库，以托管生成的数据集。共从 531 名平均年龄为 26.8 ± 5.19 岁的患者中提取了放射组学数据，其中包括 250 名女性患者和 281 名男性患者。最多有 53 个解剖结构被分割并用于随后的放射组学数据提取。共从 526 个非对比度和 400 个对比度增强（门静脉）序列中提取了放射组学特征。该数据集可公开用于模型开发和验证。

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 Digital Imaging 医学-核医学

CiteScore

7.50

自引率

6.80%

发文量

192

审稿时长

6-12 weeks

期刊介绍： The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.