PT  - JOURNAL ARTICLE
AU  - Doris Leithner
AU  - Heiko Schöder
AU  - Alexander Haug
AU  - H. Alberto Vargas
AU  - Peter Gibbs
AU  - Ida Häggström
AU  - Ivo Rausch
AU  - Michael Weber
AU  - Anton S. Becker
AU  - Jazmin Schwartz
AU  - Marius E. Mayerhoefer
TI  - Impact of ComBat Harmonization on PET Radiomics-Based Tissue Classification: A Dual-Center PET/MRI and PET/CT Study
AID  - 10.2967/jnumed.121.263102
DP  - 2022 Oct 01
TA  - Journal of Nuclear Medicine
PG  - 1611--1616
VI  - 63
IP  - 10
4099  - http://jnm.snmjournals.org/content/63/10/1611.short
4100  - http://jnm.snmjournals.org/content/63/10/1611.full
SO  - J Nucl Med2022 Oct 01; 63
AB  - Our purpose was to determine whether ComBat harmonization improves 18F-FDG PET radiomics-based tissue classification in pooled PET/MRI and PET/CT datasets. Methods: Two hundred patients who had undergone 18F-FDG PET/MRI (2 scanners and vendors; 50 patients each) or PET/CT (2 scanners and vendors; 50 patients each) were retrospectively included. Gray-level histogram, gray-level cooccurrence matrix, gray-level run-length matrix, gray-level size-zone matrix, and neighborhood gray-tone difference matrix radiomic features were calculated for volumes of interest in the disease-free liver, spleen, and bone marrow. For individual feature classes and a multiclass radiomic signature, tissue was classified on ComBat-harmonized and unharmonized pooled data, using a multilayer perceptron neural network. Results: Median accuracies in training and validation datasets were 69.5% and 68.3% (harmonized), respectively, versus 59.5% and 58.9% (unharmonized), respectively, for gray-level histogram; 92.1% and 86.1% (harmonized), respectively, versus 53.6% and 50.0% (unharmonized), respectively, for gray-level cooccurrence matrix; 84.8% and 82.8% (harmonized), respectively, versus 62.4% and 58.3% (unharmonized), respectively, for gray-level run-length matrix; 87.6% and 85.6% (harmonized), respectively, versus 56.2% and 52.8% (unharmonized), respectively, for gray-level size-zone matrix; 79.5% and 77.2% (harmonized), respectively, versus 54.8% and 53.9% (unharmonized), respectively, for neighborhood gray-tone difference matrix; and 86.9% and 84.4% (harmonized), respectively, versus 62.9% and 58.3% (unharmonized), respectively, for radiomic signature. Conclusion: ComBat harmonization may be useful for multicenter 18F-FDG PET radiomics studies using pooled PET/MRI and PET/CT data.