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Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac : Characterization of Radiomics Features in Phantom and Patient Data

Ericsson-Szecsenyi, Rebecka LU ; Zhang, Geoffrey ; Redler, Gage ; Feygelman, Vladimir ; Rosenberg, Stephen ; Latifi, Kujtim ; Ceberg, Crister LU orcid and Moros, Eduardo G. (2022) In Technology in Cancer Research and Treatment 21.
Abstract

Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan® RT phantom over 13 months and 11 scans of a ViewRay... (More)

Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan® RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV < 5%). Results: We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. Conclusion: We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
biomarker, cancer, MRI, prediction, quantification, radiation therapy, validation
in
Technology in Cancer Research and Treatment
volume
21
publisher
Adenine Press
external identifiers
  • pmid:35521966
  • scopus:85129347617
ISSN
1533-0346
DOI
10.1177/15330338221099113
language
English
LU publication?
yes
id
7a89ed73-57cd-4855-9393-1a84be293994
date added to LUP
2022-08-15 11:09:00
date last changed
2024-04-18 13:04:30
@article{7a89ed73-57cd-4855-9393-1a84be293994,
  abstract     = {{<p>Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan<sup>®</sup> RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV &lt; 5%). Results: We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. Conclusion: We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.</p>}},
  author       = {{Ericsson-Szecsenyi, Rebecka and Zhang, Geoffrey and Redler, Gage and Feygelman, Vladimir and Rosenberg, Stephen and Latifi, Kujtim and Ceberg, Crister and Moros, Eduardo G.}},
  issn         = {{1533-0346}},
  keywords     = {{biomarker; cancer; MRI; prediction; quantification; radiation therapy; validation}},
  language     = {{eng}},
  publisher    = {{Adenine Press}},
  series       = {{Technology in Cancer Research and Treatment}},
  title        = {{Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac : Characterization of Radiomics Features in Phantom and Patient Data}},
  url          = {{http://dx.doi.org/10.1177/15330338221099113}},
  doi          = {{10.1177/15330338221099113}},
  volume       = {{21}},
  year         = {{2022}},
}