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Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients

  • Zongrui Ma
  • , Jiang Zhang
  • , Xi Liu
  • , Xinzhi Teng
  • , Yu Hua Huang
  • , Xile Zhang
  • , Jun Li
  • , Yuxi Pan
  • , Jiachen Sun
  • , Yanjing Dong
  • , Tian Li
  • , Lawrence Wing Chi Chan
  • , Amy Tien Yee Chang
  • , Steven Wai Kwan Siu
  • , Andy Lai Yin Cheung
  • , Ruijie Yang
  • , Jing Cai

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Original languageEnglish
Article number2872
JournalCancers
Volume16
Issue number16
DOIs
Publication statusPublished - Aug 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • cervical cancer
  • dosiomics
  • image perturbation
  • quantitative imaging analysis
  • radiomics
  • repeatability

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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