Longitudinal stability of HyperSightTM-CBCT based radiomic features in patients with CT guided adaptive SBRT for prostate cancer

CT-guided adaptive radiotherapy (aRT) based on HyperSight<sup>TM</sup>-CBCT provides high-quality imaging, allowing quantitative radiomic feature analysis as a monitoring tool. This study comprehensively evaluates the stability of radiomic features, as potential imaging biomarkers, in pelvic structures of prostate cancer patients treated with adaptive stereotactic body radiation therapy (SBRT). Between December 2023 and July 2024, 32 patients with localized prostate cancer underwent adaptive SBRT at the Ethos<sup>®</sup> linear accelerator (Varian, Siemens Healthineers) with HyperSight-CBCT imaging. Longitudinal stability was assessed by intraclass correlation coefficient (ICC) over five fractions of aRT for target structures and non-hollow organs at risk. In pooled organs at risk, 93.0% of features showed very high stability (ICC > 0.9) compared to 67.4% in pooled target structures, indicating significantly lower stability for target structures (p = 0.00009129). Second-order features demonstrated greater stability than conventional and shape-based features (p = 0.0433, p = 0.0252). Fraction number significantly affected longitudinal prostate feature variability (p = 0.0135). This study comprehensively analyzed HyperSight-CBCT imaging to evaluate longitudinal stability of radiomic features during adaptive SBRT for prostate cancer. The trends observed will provide a framework for future CT-guided aRT studies, facilitating quantitative imaging analysis of radiological biomarkers for clinical translation and improving personalized treatment.

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Publication Details

Journal
Scientific Reports
Published
2025-05-07
DOI
https://doi.org/10.1038/s41598-025-99920-x
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72

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article

Longitudinal stability of HyperSightTM-CBCT based radiomic features in patients with CT guided adaptive SBRT for prostate cancer

Michael R. Ehmann, Frank A. Giordano, Jens Fleckenstein, Victor Siefert et al.
2 citations
Scientific Reports
Radiomics and Machine Learning in Medical Imaging
2.72
article

Longitudinal stability of HyperSightTM-CBCT based radiomic features in patients with CT guided adaptive SBRT for prostate cancer

Michael R. Ehmann, Frank A. Giordano, Jens Fleckenstein, Victor Siefert, Sven Clausen, Marvin Willam, Judit Boda‐Heggemann, Constantin Dreher, Miriam Eckl, Daniel Buergy, Ralf Schmidt, Alicia S. Bicu, Stefan O. Schoenberg, Matthias F. Froelich, Paula Cvachovec
article en
2 citations

Abstract

CT-guided adaptive radiotherapy (aRT) based on HyperSight<sup>TM</sup>-CBCT provides high-quality imaging, allowing quantitative radiomic feature analysis as a monitoring tool. This study comprehensively evaluates the stability of radiomic features, as potential imaging biomarkers, in pelvic structures of prostate cancer patients treated with adaptive stereotactic body radiation therapy (SBRT). Between December 2023 and July 2024, 32 patients with localized prostate cancer underwent adaptive SBRT at the Ethos<sup>®</sup> linear accelerator (Varian, Siemens Healthineers) with HyperSight-CBCT imaging. Longitudinal stability was assessed by intraclass correlation coefficient (ICC) over five fractions of aRT for target structures and non-hollow organs at risk. In pooled organs at risk, 93.0% of features showed very high stability (ICC > 0.9) compared to 67.4% in pooled target structures, indicating significantly lower stability for target structures (p = 0.00009129). Second-order features demonstrated greater stability than conventional and shape-based features (p = 0.0433, p = 0.0252). Fraction number significantly affected longitudinal prostate feature variability (p = 0.0135). This study comprehensively analyzed HyperSight-CBCT imaging to evaluate longitudinal stability of radiomic features during adaptive SBRT for prostate cancer. The trends observed will provide a framework for future CT-guided aRT studies, facilitating quantitative imaging analysis of radiological biomarkers for clinical translation and improving personalized treatment.

Scientific ReportsVol. 15(1)
University of Mannheim (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), University Medical Centre Mannheim (DE)
Siemens Healthineers
Good health and well-being
Openalex Percentile: Top 11%
Radiomics and Machine Learning in Medical Imaging
2.72
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