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

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 68 Ga-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 delin-eation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimi-larity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.
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Article dans une revue
PLoS ONE, Public Library of Science, 2016, 11, 〈10.1371/journal.pone.0159984.s004〉
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Clément Bailly, Caroline Bodet-Milin, Solène Couespel, Hatem Necib, Françoise Kraeber-Bodéré, et al.. Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials. PLoS ONE, Public Library of Science, 2016, 11, 〈10.1371/journal.pone.0159984.s004〉. 〈inserm-01414301〉



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