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Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials

Author

Listed:
  • Clément Bailly
  • Caroline Bodet-Milin
  • Solène Couespel
  • Hatem Necib
  • Françoise Kraeber-Bodéré
  • Catherine Ansquer
  • Thomas Carlier

Abstract

Purpose: This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. Methods: The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. Results: The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Conclusion: Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

Suggested Citation

  • Clément Bailly & Caroline Bodet-Milin & Solène Couespel & Hatem Necib & Françoise Kraeber-Bodéré & Catherine Ansquer & Thomas Carlier, 2016. "Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0159984
    DOI: 10.1371/journal.pone.0159984
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    References listed on IDEAS

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    1. Hugo J. W. L. Aerts & Emmanuel Rios Velazquez & Ralph T. H. Leijenaar & Chintan Parmar & Patrick Grossmann & Sara Carvalho & Johan Bussink & René Monshouwer & Benjamin Haibe-Kains & Derek Rietveld & F, 2014. "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    2. Hugo J.W.L. Aerts & Emmanuel Rios Velazquez & Ralph T.H. Leijenaar & Chintan Parmar & Patrick Grossmann & Sara Carvalho & Johan Bussink & René Monshouwer & Benjamin Haibe-Kains & Derek Rietveld & Fran, 2014. "Correction: Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Communications, Nature, vol. 5(1), pages 1-1, December.
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    1. Rachel B Ger & Joseph G Meier & Raymond B Pahlka & Skylar Gay & Raymond Mumme & Clifton D Fuller & Heng Li & Rebecca M Howell & Rick R Layman & R Jason Stafford & Shouhao Zhou & Osama Mawlawi & Lauren, 2019. "Effects of alterations in positron emission tomography imaging parameters on radiomics features," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-12, September.
    2. George Amadeus Prenosil & Thilo Weitzel & Markus Fürstner & Michael Hentschel & Thomas Krause & Paul Cumming & Axel Rominger & Bernd Klaeser, 2020. "Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    3. Ivan S Klyuzhin & Jessie F Fu & Andy Hong & Matthew Sacheli & Nikolay Shenkov & Michele Matarazzo & Arman Rahmim & A Jon Stoessl & Vesna Sossi, 2018. "Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-20, November.

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