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The potential of photon-counting CT for the improved precision of lung nodule radiomics.

Publication ,  Journal Article
McCabe, C; Abadi, E; Zarei, M; Segars, WP; Samei, E
Published in: Phys Med Biol
January 27, 2025

Objective.Lung nodule appearance may provide prognostic information, as the presence of spiculation increases the suspicion of a nodule being cancerous. Spiculations can be quantified using morphological radiomics features extracted from CT images. Radiomics features can be affected by the acquisition parameters and scanner technologies; thus, it is essential to identify imaging conditions that provide reliable measurements, particularly for emerging technologies like photon-counting CT (PCCT). This study aimed to systematically quantify the effect of imaging parameters on the radiomics measurements using a virtual imaging trial (VIT) platform, and further verify the findings with human clinical data.Approach.The VIT utilized nine virtual patients, each with three 6 mm nodules of varying spiculations. The virtual patients were run through a validated CT simulator (DukeSim) to acquire images at three dose levels (CTDIvol = 2.85, 5.69, and 11.38 mGy) with a clinical energy-integrating CT and a PCCT. The acquired projection images were reconstructed using multiple slice thicknesses, kernels, and matrix sizes. The reconstructed images were processed to extract morphological features using three segmentation methods. The features were clustered into three broad type categories. Features extracted from the acquired CT images were compared to their corresponding ground truth values, across all imaging conditions.Main results.Among all imaging conditions, slice thickness had the greatest effect on the radiomics measurements. When the thickest slices were used, the coefficient of variation increased by [1.19%-9.66%] in the energy integrating CT images, and [3.94%-24.43%] in the PCCT images. For both scanners, varying the kernel sharpness and dose affected the radiomics measurements insignificantly, while pixel size and segmentation method were observed to have stronger effects. Under varying imaging conditions, the trends and magnitude of radiomics features measurements were coherent with virtual trial results.Significance.The findings stress the importance of choosing optimal reconstruction settings for radiomics extraction to achieve precise feature quantifications.

Duke Scholars

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

January 27, 2025

Volume

70

Issue

3

Location

England

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiomics
  • Photons
  • Nuclear Medicine & Medical Imaging
  • Lung Neoplasms
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering
 

Citation

APA
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ICMJE
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McCabe, C., Abadi, E., Zarei, M., Segars, W. P., & Samei, E. (2025). The potential of photon-counting CT for the improved precision of lung nodule radiomics. Phys Med Biol, 70(3). https://doi.org/10.1088/1361-6560/adaad2
McCabe, Cindy, Ehsan Abadi, Mojtaba Zarei, W Paul Segars, and Ehsan Samei. “The potential of photon-counting CT for the improved precision of lung nodule radiomics.Phys Med Biol 70, no. 3 (January 27, 2025). https://doi.org/10.1088/1361-6560/adaad2.
McCabe C, Abadi E, Zarei M, Segars WP, Samei E. The potential of photon-counting CT for the improved precision of lung nodule radiomics. Phys Med Biol. 2025 Jan 27;70(3).
McCabe, Cindy, et al. “The potential of photon-counting CT for the improved precision of lung nodule radiomics.Phys Med Biol, vol. 70, no. 3, Jan. 2025. Pubmed, doi:10.1088/1361-6560/adaad2.
McCabe C, Abadi E, Zarei M, Segars WP, Samei E. The potential of photon-counting CT for the improved precision of lung nodule radiomics. Phys Med Biol. 2025 Jan 27;70(3).
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

January 27, 2025

Volume

70

Issue

3

Location

England

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiomics
  • Photons
  • Nuclear Medicine & Medical Imaging
  • Lung Neoplasms
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 1103 Clinical Sciences
  • 0903 Biomedical Engineering