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Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause “batch effects” that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
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Details
1 University of Pennsylvania, Department of Bioengineering, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
2 Mayo Clinic, Rochester, USA (GRID:grid.66875.3a) (ISNI:0000 0004 0459 167X)
3 University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)
4 University of California, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
5 University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Columbia University, Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), New York, USA (GRID:grid.21729.3f) (ISNI:0000 0004 1936 8729)
6 University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972)