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Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases

Publication ,  Conference
Zhang, J; Lam, S; Teng, X; Zhang, Y; Ma, Z; Lee, F; Au, KH; Yip, WY; Chang, TYA; Chan, WCL; Lee, V; Wu, QJ; Cai, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2022

We attempted to investigate the Radiomic feature (RF) repeatability and its agreements across imaging modalities and head-and-neck cancer (HNC) subtypes via image perturbations. Contrast-enhanced computed tomography (CECT), CET1-weight, T2-weight magnetic resonance images of 231 nasopharyngeal carcinoma (NPC) patients, and CECT images of 399 oropharyngeal carcinoma (OPC) patients were retrospectively analyzed. Randomized translation and rotation were implemented to the images for mimicking scanning position stochasticity. 1288 RFs from unfiltered, Laplacian-of-Gaussian-filtered (LoG), and wavelet-filtered images were subsequently computed per perturbed image. The intra-class correlation coefficient (ICC) was calculated to assess RF repeatability. The mean absolute difference (MAD) of the ICC and the binarized repeatability consistency between image sets were adopted to evaluate its agreements across imaging modalities and HNC subtypes. Bias from feature collinearity was also investigated. All the shape RFs and the majority of RFs from unfiltered (≥ 83.5%) and LoG-filtered (≥ 93%) images showed high repeatability (ICC ≥ 0.9) in all studied datasets, whereas more than 50% of the wavelet-filtered RFs had low repeatability (ICC < 0.9). RF repeatability agreements between imaging modalities within the NPC cohort were outstanding (MAD < 0.05, consistency > 0.9) and slightly higher between the NPC and OPC cohort (MAD = 0.06, consistency = 0.89). Minimum bias from feature collinearity was observed. We urge caution when handling wavelet-filtered RFs and advise taking initiatives to exclude underperforming RFs during feature pre-selection for robust model construction.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031172656

Publication Date

January 1, 2022

Volume

13574 LNCS

Start / End Page

21 / 34

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Zhang, J., Lam, S., Teng, X., Zhang, Y., Ma, Z., Lee, F., … Cai, J. (2022). Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13574 LNCS, pp. 21–34). https://doi.org/10.1007/978-3-031-17266-3_3
Zhang, J., S. Lam, X. Teng, Y. Zhang, Z. Ma, F. Lee, K. H. Au, et al. “Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13574 LNCS:21–34, 2022. https://doi.org/10.1007/978-3-031-17266-3_3.
Zhang J, Lam S, Teng X, Zhang Y, Ma Z, Lee F, et al. Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 21–34.
Zhang, J., et al. “Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13574 LNCS, 2022, pp. 21–34. Scopus, doi:10.1007/978-3-031-17266-3_3.
Zhang J, Lam S, Teng X, Zhang Y, Ma Z, Lee F, Au KH, Yip WY, Chang TYA, Chan WCL, Lee V, Wu QJ, Cai J. Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 21–34.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783031172656

Publication Date

January 1, 2022

Volume

13574 LNCS

Start / End Page

21 / 34

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences