Abstract #3107

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

Jonathan R. Dillman¹, Wei Jia², Hailong Li¹, Redha Ali², Krishna Shanbhogue³, William R. Masch⁴, Anum Aslam⁴, David Harris⁵, Scott Reeder⁶, and Lili He¹

¹Department of Radiology, Cincinnati children's hospital medical center, Cincinnati, OH, United States, ²Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ³Department of Radiology, New York University Langone Health, New York, NY, United States, ⁴University of Michigan, Ann Arbor, MI, United States, ⁵University of Wisconsin-Madison, Madison, WI, United States, ⁶Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Analysis/Processing, Data Processing, Harmonization

Motivation: Multi-center studies often suffer from non-biological variations due to different MRI scanners. This can adversely affect the comparability of MRI radiomic features and deep features.

Goal(s): This multi-center study aims to investigate the effectiveness of ComBat harmonization on radiomic and deep features from abdominal MRI data.

Approach: We retrieved 3,857 clinical T2-weighted MRI examinations of adult patients from three institutions. The ANOVA test and Cohen’s F score were applied as evaluation metrics.

Results: An average of 78.7% of radiomic features, and 74.9% of deep features had significant distribution differences. After ComBat harmonization, none of radiomic and deep features had significant difference.

Impact: This multi-center study showed that ComBat can effectively remove non-biological variations of radiomic and deep features from abdominal MRI studies acquired from different MRI scanners and institutions. Future multi-center studies should consider ComBat harmonization to improve data comparability.

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