Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Mri Radiomic Features Harmonization: A Multi-Center Phantom Study Publisher



Hajianfar G1 ; Hosseini SA2 ; Amini M3 ; Shiri I3 ; Zaidi H3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Iran University of Medical Sciences, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
  2. 2. Tehran University of Medical Sciences, Department of Medical Physics and Biomedical Engineering, Tehran, Iran
  3. 3. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva, 1211, Switzerland

Source: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference Published:2022


Abstract

In cancer imaging, radiomic features are a vital tool for diagnosing, quantifying intratumor heterogeneity, and predicting response to therapy and prognosis. However, the lack of a robust evaluation makes applying them across multi-center studies and larger patient populations complex. The main goal of this study is to use an MRI phantom to examine the reproducibility of MRI radiomic features that represent feature variability with ComBat harmonization in a multi-center examination with various acquisition settings and image pre-processing steps. An MRI phantom was used to acquire images using four scanners. Manual segmentation was used for 19 phantom compartments. Wavelet, bin discretization, and Laplacian of Gaussian were employed to pre-process each image. Ninety-two radiomic features were extracted. ComBat harmonization was applied to all feature sets. The Kruskal-(KW) Wallis's and the Intraclass Correlation Coefficient (ICC) tests were utilized to indicate radiomic features' variability. The number of non-significant p-values ranged from 29-74 and 0-5 features after and before harmonization over different image pre-processing. The results showed that the radiomic features are influenced most by various scanners. The impact of ComBat harmonization on the reproducibility of MRI radiomic features is considerable. © 2022 IEEE.
Related Docs
View other Related Docs
1. Machine Learning-Based Overall Survival Prediction in Gbm Patients Using Mri Radiomics, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)
2. Robust Versus Non-Robust Radiomic Features: Machine Learning Based Models for Nsclc Lymphovascular Invasion, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)
3. Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning, Computers in Biology and Medicine (2022)
4. [18F]Fdg-Pet/Ct Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications, Seminars in Nuclear Medicine (2022)
5. Differentiation of Covid-19 Pneumonia From Other Lung Diseases Using Ct Radiomic Features and Machine Learning: A Large Multicentric Cohort Study, International Journal of Imaging Systems and Technology (2024)
6. The Effect of Harmonization on the Variability of Pet Radiomic Features Extracted Using Various Segmentation Methods, Annals of Nuclear Medicine (2024)
7. Radiomics Predictive Modeling From Dual-Time-Point Fdg Pet Ki Parametric Maps: Application to Chemotherapy Response in Lymphoma, EJNMMI Research (2023)
8. Pet Image Radiomics Feature Variability in Lung Cancer: Impact of Image Segmentation, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)
Experts (# of related papers)
View all Related Experts
Parham Geramifar (4)
Amir Reza Radmard (3)
Other Related Docs
9. Robust Vs. Non-Robust Radiomic Features: The Quest for Optimal Machine Learning Models Using Phantom and Clinical Studies, Cancer Imaging (2025)
10. Differential Privacy Preserved Federated Learning for Prognostic Modeling in Covid-19 Patients Using Large Multi-Institutional Chest Ct Dataset, Medical Physics (2024)
11. Information Fusion for Fully Automated Segmentation of Head and Neck Tumors From Pet and Ct Images, Medical Physics (2024)
12. Pet Radiomics-Based Lymphovascular Invasion Prediction in Lung Cancer Using Multiple Segmentation and Multi-Machine Learning Algorithms, Physical and Engineering Sciences in Medicine (2024)
13. Machine Learning-Based Analysis of 68Ga-Psma-11 Pet/Ct Images for Estimation of Prostate Tumor Grade, Physical and Engineering Sciences in Medicine (2024)
14. Eltirads Framework for Thyroid Nodule Classification Integrating Elastography, Tirads, and Radiomics With Interpretable Machine Learning, Scientific Reports (2025)
15. Deep Vision Transformers for Prognostic Modeling in Covid-19 Patients Using Large Multi-Institutional Chest Ct Dataset, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)