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Robust imaging habitat computation using voxel-wise radiomics features.

Kinga Bernatowicz1, Francesco Grussu2, Marta Ligero2

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Tumor heterogeneity in lung cancer impacts treatment. This study identifies repeatable radiomics features for stable imaging habitats, enabling better non-invasive cancer biomarker discovery.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Radiomics

Background:

  • Tumor heterogeneity is a key challenge in cancer treatment resistance and cure.
  • Radiomics, using quantitative imaging features, can reveal tumor heterogeneity.
  • Imaging habitats, based on radiomics, show promise but face variability issues.

Purpose of the Study:

  • To assess the repeatability and reproducibility of voxel-wise radiomics features.
  • To evaluate the impact of radiomics variability on imaging habitats.
  • To identify robust radiomics features for stable imaging habitat computation in lung cancer.

Main Methods:

  • Analyzed voxel-wise radiomics features from over 500 lung cancer CT scans for repeatability and reproducibility.
  • Evaluated the effect of radiomics variability on imaging habitats using test-retest CT images from 30 lung cancer patients.
  • Identified nine specific voxel-wise radiomics features that are repeatable and reproducible.

Main Results:

  • Repeatable voxel-wise features effectively characterize texture heterogeneity.
  • Reproducibility of these features is independent of feature extraction parameters.
  • Imaging habitats derived from robust radiomics features demonstrate greater stability in test-retest scans.
  • Nine specific features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn, Idmn) were found to be repeatable and reproducible.

Conclusions:

  • Robust voxel-wise radiomics features enhance the stability of imaging habitats.
  • These stable imaging habitats can help elucidate lung tumor heterogeneity.
  • The identified repeatable and reproducible features support the development of non-invasive imaging biomarkers for precision medicine.