Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Wei Jia, Hailong Li, Redha Ali, Krishna P. Shanbhogue, William R. Masch, Anum Aslam, David T. Harris, Scott B. Reeder, Jonathan R. Dillman, Lili He
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Abstract

ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features extracted from large, multi-center abdominal MRI data. A retrospective study was conducted on T2-weighted (T2W) abdominal MRI data retrieved from individual patients with suspected or known chronic liver disease at three study sites. MRI data were acquired using systems from three manufacturers and two field strengths. Radiomic features and deep features were extracted using the PyRadiomics pipeline and a Swin Transformer. ComBat was used to harmonize radiomic and deep features across different manufacturers and field strengths. Student’s t-test, ANOVA test, and Cohen’s F score were applied to assess the difference in individual features before and after ComBat harmonization. Between two field strengths, 76.7%, 52.9%, and 26.7% of radiomic features, and 89.0%, 56.5%, and 0.1% of deep features from three manufacturers were significantly different. Among the three manufacturers, 90.1% and 75.0% of radiomic features and 89.3% and 84.1% of deep features from two field strengths were significantly different. After ComBat harmonization, there were no significant differences in radiomic and deep features among manufacturers or field strengths based on t-tests or ANOVA tests. Reduced Cohen’s F scores were consistently observed after ComBat harmonization. ComBat harmonization effectively harmonizes radiomic and deep features by removing the non-biological variations due to system manufacturers and/or field strengths in large multi-center clinical abdominal MRI datasets.

Abstract Image

从多中心腹部磁共振成像数据中研究 ComBat 对放射组学和深度特征的协调性
ComBat 协调技术的开发是为了在应用人工智能(AI)的多中心研究中消除数据的非生物变异。我们研究了 ComBat 协调对从大型多中心腹部 MRI 数据中提取的放射学和深度特征的有效性。我们在三个研究机构对疑似或已知慢性肝病患者的 T2 加权(T2W)腹部 MRI 数据进行了回顾性研究。磁共振成像数据是使用三家制造商的系统和两种场强采集的。使用 PyRadiomics 管道和 Swin Transformer 提取了放射组学特征和深度特征。ComBat 用于协调不同制造商和不同场强的放射组学特征和深度特征。采用学生 t 检验、方差分析检验和 Cohen's F 评分来评估 ComBat 协调前后单个特征的差异。在两种场强之间,三家制造商分别有 76.7%、52.9% 和 26.7% 的放射学特征和 89.0%、56.5% 和 0.1% 的深部特征存在显著差异。在三家制造商中,两种场强的放射性原子特征分别为 90.1%和 75.0%,深层特征分别为 89.3%和 84.1%,差异显著。在 ComBat 协调后,根据 t 检验或方差分析检验,不同制造商或不同场强的放射性体征和深部特征没有明显差异。ComBat 协调后,Cohen's F 分数持续降低。在大型多中心临床腹部 MRI 数据集中,ComBat 协调通过消除系统制造商和/或磁场强度造成的非生物学差异,有效地协调了放射学和深部特征。
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来源期刊
Journal of Digital Imaging
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.
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