Category-ComBat with distribution coefficient: a scale-adaptive harmonization algorithm for multicenter radiomic feature standardization.

Introduction: Multicenter radiomic studies are fundamentally constrained by inter-center feature variability arising from differences in scanner hardware, acquisition protocols, and reconstruction parameters. Existing harmonization methods, including ComBat, treat these sources of variability as undifferentiated batch noise and primarily correct feature distribution location, leaving scale-level heterogeneity unaddressed. This study proposes Category-ComBat with Distribution Coefficient (Category-ComBat-ω), a two-level harmonization framework for multicenter CT radiomics. Methods: A total of 1,789 pulmonary nodule CT images were retrospectively collected from three public datasets (LIDC-IDRI, NSCLC-Radiomics, and Lung-PET-CT-Dx), with 93 radiomic features extracted per case. Category-ComBat reconstructs the ComBat framework by explicitly separating imaging-related variability into systematic components, including scanner type, acquisition protocol, and reconstruction kernel, and random components. Individualized least-squares estimation was used to estimate systematic biases, together with category-stratified mean normalization. A distribution coefficient ω ∈ (0, 1] was introduced as an orthogonal scale-adjustment mechanism to adaptively compress feature distribution spread without perturbing category-level means. Performance was evaluated using Wilcoxon tests, Q-Q plots, three-dimensional principal component analysis (PCA), probability density plots, coefficient of variation (COV), intraclass correlation coefficient (ICC), and downstream machine learning classification experiments. Results: Category-ComBat effectively removed systematic inter-center variability while preserving benign-malignant discriminative separation. Introduction of ω progressively improved feature reproducibility and inter-center consistency as ω decreased. The best overall performance, evaluated using ICC, COV, and classification AUC, was observed for ω values between 0.5 and 0.9. Scale compression remained independent of location adjustment, confirming the orthogonality of the two-level design. Category-ComBat achieved the highest classification performance, with the mean AUC increasing from 0.84 for the Original features and 0.82 for ComBat to 0.89 after Category-ComBat harmonization. Discussion: Category-ComBat-ω provides a principled approach to multicenter radiomic harmonization by addressing both systematic imaging-related confounding and scale heterogeneity. Its tunable distribution coefficient enables flexible adjustment of feature distribution spread while preserving category-level means, providing an adaptable framework for different multicenter radiomic study requirements.