Addressing Inter-reconstruction variability in multi-energy myocardial CT Radiomics: The Benefits of combat harmonization

Sarv Priya; Abigail Reutzel; Otavio Augusto Ferreira Dalla Pria; Sawyer Goetz; Hanh Td Pham; Aiah Alatoum; Pritish Y Aher; Sabarish Narayanasamy; Prashant Nagpal; Knute D Carter

doi:10.1016/j.ejrad.2024.111891

To investigate the effect of ComBat harmonization on the stability of myocardial radiomic features derived from multi-energy CT reconstructions.RATIONALE AND OBJECTIVESTo investigate the effect of ComBat harmonization on the stability of myocardial radiomic features derived from multi-energy CT reconstructions.A retrospective study was conducted on 205 patients who underwent dual-energy chest CTA at a single center. The data was reconstructed into multiple spectral reconstructions (mixed energy simulating standard 120 Kv acquisition and monoenergetic images ranging from 40 to 190 keV in increments of 10). The left ventricle myocardium was segmented using semiautomated software (Syngo.Via FRONTIER, version 5.0.2; Siemens). Radiomic features were extracted from multiple spectral reconstructions (batches). The consistency of these radiomics features across different batches was evaluated with and without ComBat harmonization using Cohen's d and Principal component analysis (PCA). Both parametric and nonparametric ComBat methods were considered.MATERIALS AND METHODSA retrospective study was conducted on 205 patients who underwent dual-energy chest CTA at a single center. The data was reconstructed into multiple spectral reconstructions (mixed energy simulating standard 120 Kv acquisition and monoenergetic images ranging from 40 to 190 keV in increments of 10). The left ventricle myocardium was segmented using semiautomated software (Syngo.Via FRONTIER, version 5.0.2; Siemens). Radiomic features were extracted from multiple spectral reconstructions (batches). The consistency of these radiomics features across different batches was evaluated with and without ComBat harmonization using Cohen's d and Principal component analysis (PCA). Both parametric and nonparametric ComBat methods were considered.Without any ComBat technique, 43.40% of features remained consistent across all multienergy reconstructions. Applying ComBat harmonization increased this consistency to 98.37% with parametric empirical bayes (EB) ComBat and EB M-ComBat, and to 91.52% and 92.33% with nonparametric EB ComBat and nonparametric EB M-ComBat, respectively. PCA without ComBat revealed noticeable differences in the first two principal components between batches, indicating a batch effect or unstable radiomic features. Following ComBat harmonization, the principal components showed more consistency between batches, demonstrating radiomics feature stability between batches.RESULTSWithout any ComBat technique, 43.40% of features remained consistent across all multienergy reconstructions. Applying ComBat harmonization increased this consistency to 98.37% with parametric empirical bayes (EB) ComBat and EB M-ComBat, and to 91.52% and 92.33% with nonparametric EB ComBat and nonparametric EB M-ComBat, respectively. PCA without ComBat revealed noticeable differences in the first two principal components between batches, indicating a batch effect or unstable radiomic features. Following ComBat harmonization, the principal components showed more consistency between batches, demonstrating radiomics feature stability between batches.ComBat harmonization enhanced the consistency of radiomic features from multi-energy CT data. Integrating ComBat harmonization may lead to more reproducible results in multienergy CT radiomics studies.CONCLUSIONComBat harmonization enhanced the consistency of radiomic features from multi-energy CT data. Integrating ComBat harmonization may lead to more reproducible results in multienergy CT radiomics studies.

Addressing Inter-reconstruction variability in multi-energy myocardial CT Radiomics: The Benefits of combat harmonization

View Online

Abstract

Details

Metrics