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Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study


Saltybaeva, Natalia; Tanadini-Lang, Stephanie; Vuong, Diem; Burgermeister, Simon; Mayinger, Michael; Bink, Andrea; Andratschke, Nicolaus; Guckenberger, Matthias; Bogowicz, Marta (2022). Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study. Physics and imaging in radiation oncology, 22:131-136.

Abstract

Background and purpose

Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study.

Methods

Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset.

Results

Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images.

Conclusions

MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value.

Abstract

Background and purpose

Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study.

Methods

Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset.

Results

Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images.

Conclusions

MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Radiation Oncology
04 Faculty of Medicine > University Hospital Zurich > Clinic for Neuroradiology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Radiation
Health Sciences > Oncology
Health Sciences > Radiology, Nuclear Medicine and Imaging
Language:English
Date:April 2022
Deposited On:30 Sep 2022 14:19
Last Modified:28 Mar 2024 02:38
Publisher:Elsevier
ISSN:2405-6316
Additional Information:Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136]
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.phro.2022.05.006
Related URLs:https://www.zora.uzh.ch/id/eprint/220945/
PubMed ID:35633866
  • Content: Published Version
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)