Cargando…

Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies

Multicenter studies are needed to demonstrate the clinical potential value of radiomics as a prognostic tool. However, variability in scanner models, acquisition protocols and reconstruction settings are unavoidable and radiomic features are notoriously sensitive to these factors, which hinders pool...

Descripción completa

Detalles Bibliográficos
Autores principales:	Da-ano, R., Masson, I., Lucia, F., Doré, M., Robin, P., Alfieri, J., Rousseau, C., Mervoyer, A., Reinhold, C., Castelli, J., De Crevoisier, R., Rameé, J. F., Pradier, O., Schick, U., Visvikis, D., Hatt, M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314795/ https://www.ncbi.nlm.nih.gov/pubmed/32581221 http://dx.doi.org/10.1038/s41598-020-66110-w

Ejemplares similares

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets
por: Da-ano, Ronrick, et al.
Publicado: (2021)

Improved generalized ComBat methods for harmonization of radiomic features
por: Horng, Hannah, et al.
Publicado: (2022)

The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization
por: Ibrahim, Abdalla, et al.
Publicado: (2021)

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects
por: Horng, Hannah, et al.
Publicado: (2022)

ComBat Harmonization: Empirical Bayes versus fully Bayes approaches
por: Reynolds, Maxwell, et al.
Publicado: (2023)

The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model
por: Ibrahim, Abdalla, et al.
Publicado: (2022)

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
por: Torbati, Mahbaneh Eshaghzadeh, et al.
Publicado: (2021)

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data()
por: Beer, Joanne C., et al.
Publicado: (2020)

Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics
por: Maikusa, Norihide, et al.
Publicado: (2021)

AutoComBat: a generic method for harmonizing MRI-based radiomic features
por: Carré, Alexandre, et al.
Publicado: (2022)

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA
por: Radua, Joaquim, et al.
Publicado: (2020)

Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI
por: Ma, Huan, et al.
Publicado: (2023)

Multicentric validation of radiomics findings: challenges and opportunities
por: Hatt, Mathieu, et al.
Publicado: (2019)

White matter changes following electroconvulsive therapy for depression: a multicenter ComBat harmonization approach
por: Belge, Jean-Baptiste, et al.
Publicado: (2022)

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center “Blue Sky Radiomics” study on stage III unresectable NSCLC
por: Cabini, Raffaella Fiamma, et al.
Publicado: (2022)

MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer
por: Bourbonne, Vincent, et al.
Publicado: (2019)

Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort
por: Richter, Sophie, et al.
Publicado: (2022)

Characterization of the effects of outliers on ComBat harmonization for removing inter-site data heterogeneity in multisite neuroimaging studies
por: Han, Qichao, et al.
Publicado: (2023)

Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848
por: Orlhac, Fanny, et al.
Publicado: (2021)

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting
por: Iantsen, Andrei, et al.
Publicado: (2021)

External Validation of a Radiomics Model for the Prediction of Complete Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer
por: Bordron, Anaïs, et al.
Publicado: (2022)

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics
por: Tixier, Florent, et al.
Publicado: (2020)

A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents
por: Shiohama, Tadashi, et al.
Publicado: (2023)

Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients
por: Bourbonne, Vincent, et al.
Publicado: (2021)

A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity
por: Bostami, Biozid, et al.
Publicado: (2022)

Reply to Orlhac, F.; Buvat, I. Comment on “Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848”
por: Ibrahim, Abdalla, et al.
Publicado: (2021)

Harmonization of multi-site diffusion tensor imaging data for cervical and thoracic spinal cord at 1.5 T and 3 T using longitudinal ComBat
por: Middleton, Devon M., et al.
Publicado: (2023)

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer
por: Lucia, François, et al.
Publicado: (2021)

ComBat-seq: batch effect adjustment for RNA-seq count data
por: Zhang, Yuqing, et al.
Publicado: (2020)

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?
por: Bourbonne, Vincent, et al.
Publicado: (2023)

Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy
por: Bourbonne, Vincent, et al.
Publicado: (2022)

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data
por: Hu, Fengling, et al.
Publicado: (2023)

Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat
por: Stein, Caleb K, et al.
Publicado: (2015)

External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer
por: Bourbonne, Vincent, et al.
Publicado: (2020)

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data
por: Müller, Christian, et al.
Publicado: (2016)

Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies
por: Zindler, Tristan, et al.
Publicado: (2020)

The importance of feature aggregation in radiomics: a head and neck cancer study
por: Fontaine, Pierre, et al.
Publicado: (2020)

[(18)F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation
por: Ferreira, Marta, et al.
Publicado: (2021)

Correction to: [(18)F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation
por: Ferreira, Marta, et al.
Publicado: (2021)

Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study
por: Zhuo, Yaoyao, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_683aapeail74jo072gf0542qkk