RT Journal Article
SR Electronic
T1 Impact of ComBat harmonization on PET radiomics-based tissue classification: a dual-center PET/MR and PET/CT study
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.121.263102
DO 10.2967/jnumed.121.263102
A1 Doris Leithner
A1 Heiko Schoder
A1 Alexander Robert Haug
A1 Hebert Alberto Vargas
A1 Peter Gibbs
A1 Ida Häggström
A1 Ivo Rausch
A1 Michael Weber
A1 Anton S Becker
A1 Jazmin Schwartz
A1 Marius E Mayerhoefer
YR 2022
UL http://jnm.snmjournals.org/content/early/2022/02/24/jnumed.121.263102.abstract
AB 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.