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Building reliable radiomic models using image perturbation.

Publication ,  Journal Article
Teng, X; Zhang, J; Zwanenburg, A; Sun, J; Huang, Y; Lam, S; Zhang, Y; Li, B; Zhou, T; Xiao, H; Liu, C; Li, W; Han, X; Ma, Z; Li, T; Cai, J
Published in: Sci Rep
June 16, 2022

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test-retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518-0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527-0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759-0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782-0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.

Duke Scholars

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

June 16, 2022

Volume

12

Issue

1

Start / End Page

10035

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prognosis
  • Humans
  • Cohort Studies
 

Citation

APA
Chicago
ICMJE
MLA
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Teng, X., Zhang, J., Zwanenburg, A., Sun, J., Huang, Y., Lam, S., … Cai, J. (2022). Building reliable radiomic models using image perturbation. Sci Rep, 12(1), 10035. https://doi.org/10.1038/s41598-022-14178-x
Teng, Xinzhi, Jiang Zhang, Alex Zwanenburg, Jiachen Sun, Yuhua Huang, Saikit Lam, Yuanpeng Zhang, et al. “Building reliable radiomic models using image perturbation.Sci Rep 12, no. 1 (June 16, 2022): 10035. https://doi.org/10.1038/s41598-022-14178-x.
Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, et al. Building reliable radiomic models using image perturbation. Sci Rep. 2022 Jun 16;12(1):10035.
Teng, Xinzhi, et al. “Building reliable radiomic models using image perturbation.Sci Rep, vol. 12, no. 1, June 2022, p. 10035. Pubmed, doi:10.1038/s41598-022-14178-x.
Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, Zhang Y, Li B, Zhou T, Xiao H, Liu C, Li W, Han X, Ma Z, Li T, Cai J. Building reliable radiomic models using image perturbation. Sci Rep. 2022 Jun 16;12(1):10035.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

June 16, 2022

Volume

12

Issue

1

Start / End Page

10035

Location

England

Related Subject Headings

  • Reproducibility of Results
  • Prognosis
  • Humans
  • Cohort Studies