Impact of ComBat harmonization on PET radiomics-based tissue classification: a dual-center PET/MR and PET/CT study

Abstract

Rationale: To determine whether ComBat harmonization improves ¹⁸F-FDG-PET radiomics-based tissue classification in pooled PET/MR and PET/CT datasets. Methods: Two-hundred patients who had undergone ¹⁸F-FDG-PET/MR (two scanners/vendors; 50 patients each) or -PET/CT (two scanners/vendors; 50 patients each) were retrospectively included. Grey-level histogram (GLH), co-occurrence matrix (GLCM), run-length matrix (GLRLM), size-zone matrix (GLSZM), and neighborhood grey-tone difference matrix (NGTDM) radiomic features were calculated for volumes of interest in the disease-free liver, spleen, and bone marrow. For individual feature classes and a multi-class radiomic signature, tissue classification was performed on ComBat-harmonized and unharmonized pooled data, using a multi-layer perceptron neural network. Results: Median accuracies in training/validation datasets were: GLH, 69.5/68.3% (harmonized) vs. 59.5/58.9% (unharmonized); GLCM, 92.1/86.1% vs. 53.6/50.0%; GLRLM, 84.8/82.8% vs. 62.4/58.3%; GLSZM, 87.6/85.6% vs. 56.2/52.8%; NGTDM, 79.5/77.2% vs. 54.8/53.9%, and radiomic signature, 86.9/84.4% vs. 62.9/58.3%. Conclusion: ComBat harmonization may be useful for multi-center ¹⁸F-FDG-PET radiomics studies using pooled PET/MR and PET/CT data.