ArticleScientific reports2022
Improved generalized ComBat methods for harmonization of radiomic features.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- MRI-based Radiomic Features for Risk Stratification of Ductal Carcinoma in Situ in a Multicenter Setting (ECOG-ACRIN E4112 Trial).Radiology · 2025Trial
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- Undertraining is the floor, not the ceiling: when statistical and validation failures compound in radiomics.European radiology · 2026Article
- Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules.Scientific reports · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Radiomics and Back Pain.Global spine journal · 2026Review
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction.Computers in biology and medicine · 2026Article
- Reconstruction-aware radiomics for risk stratification of subsolid pulmonary nodules: technical evidence, clinical limits, and translation priorities.Frontiers in medicine · 2026Review
- Category-ComBat with distribution coefficient: a scale-adaptive harmonization algorithm for multicenter radiomic feature standardization.Frontiers in oncology · 2026Article
- Consensus-driven feature selection for transparent and robust loan default prediction.Scientific reports · 2025Article
- Brain morphological characteristics predicting clinical response to selective serotonin reuptake inhibitors or cholinesterase inhibitors: A study of electronic medical records in patients with cognitive disorders.International psychogeriatrics · 2025Observational
- Development of a prognostic multiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first line immunotherapy.Scientific reports · 2025Article
- Training Set Augmentation and Harmonization Enables Radiomic Models to Detect Early Onset of Lung Cancer.Research square · 2025Article
- Radiomic Parenchymal Phenotypes of Breast Texture from Mammography and Association with Risk of Breast Cancer.Radiology · 2025Article
- Improved Injury Detection Through Harmonizing Multi-Site Neuroimaging Data after Experimental TBI: A Translational Outcomes Project in NeuroTrauma (TOP-NT) Consortium Study.bioRxiv : the preprint server for biology · 2025Article
- Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.European radiology · 2025Article
- Improved injury detection through harmonizing multi-site neuroimaging data after experimental TBI: a Translational Outcomes Project in Neurotrauma consortium study.Frontiers in neurology · 2025Article
- The Challenge of External Generalisability: Insights from the Bicentric Validation of a [Cancers · 2024Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.Scientific data · 2024Article
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9 authors.
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Abstract
Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested - GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested - GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.
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