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Introduction <p>This study aimed to evaluate the repeatability of radiomic features extracted from cone-beam computed tomography (CBCT) images and to identify specific radiomic features suitable for use in CBCT studies.</p> Methods <p>In this study, radiomic analysis was conducted using the 3D Slicer program on two CBCT scans obtained at different time points from each of 33 individuals, using the same CBCT device. A total of 107 radiomics features were extracted from the segmented C2 (Axis) data in all cases. The results of the radiomic analysis were evaluated using the Intraclass Correlation Coefficient (ICC) in the SPSS program to assess repeatability.</p> Results <p>As a result of the analysis, 25 out of 107 radiomic features demonstrated excellent repeatability (ICC &gt; 0.90). These included nine of the 14 shape-based features, four of the 18 first-order features, two of the 24 GLCM features, three each from the 14 GLDM, 14 GLRLM, and 16 GLSZM features, and one of the five NGTDM features.</p> Conclusion <p>The grey-value dependent second-order radiomic features obtained from CBCT images have been found to exhibit lower repeatability compared to shape and first-order features. The poor repeatability of grayscale parameters should be taken into account when performing radiomics analysis of CBCT volumes.</p>

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Repeatability of radiomics parameters in cone beam computed tomography volumes

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Alican Kuran,
  • Aytaç Üzel,
  • Oğuz Baysal,
  • Umut Seki,
  • Enver Alper Sinanoğlu

摘要

Introduction

This study aimed to evaluate the repeatability of radiomic features extracted from cone-beam computed tomography (CBCT) images and to identify specific radiomic features suitable for use in CBCT studies.

Methods

In this study, radiomic analysis was conducted using the 3D Slicer program on two CBCT scans obtained at different time points from each of 33 individuals, using the same CBCT device. A total of 107 radiomics features were extracted from the segmented C2 (Axis) data in all cases. The results of the radiomic analysis were evaluated using the Intraclass Correlation Coefficient (ICC) in the SPSS program to assess repeatability.

Results

As a result of the analysis, 25 out of 107 radiomic features demonstrated excellent repeatability (ICC > 0.90). These included nine of the 14 shape-based features, four of the 18 first-order features, two of the 24 GLCM features, three each from the 14 GLDM, 14 GLRLM, and 16 GLSZM features, and one of the five NGTDM features.

Conclusion

The grey-value dependent second-order radiomic features obtained from CBCT images have been found to exhibit lower repeatability compared to shape and first-order features. The poor repeatability of grayscale parameters should be taken into account when performing radiomics analysis of CBCT volumes.