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10.1016/j.ejmp.2020.06.025 http://scihub22266oqcxt.onion/10.1016/j.ejmp.2020.06.025 32673824!ä!32673824 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32673824&cmd=llinks): Failed to open stream: HTTP request failed! 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Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms |pdf=|usr=}}{{32673824}} gab.com Text Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms JO: Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=32673824 #FOAvip PURPOSE: To explore the variation of the discriminative power of CT radiomic features (RF) against image discretization/interpolation in characterizing pancreatic neuro-endocrine (PanNEN) neoplasms. MATERIALS AND METHODS: Thirty-nine PanNEN patients with pre-surgical high contrast CT available were considered. Image interpolation and discretization parameters were intentionally changed, including pixel size (0.73-2.19 mm(2)), slice thickness (2-5 mm) and binning (32-128 grey levels) and their combination generated 27 parameter's set. The ability of 69 RF in discriminating post-surgically assessed tumor grade (>G1), positive nodes, metastases and vascular invasion was tested: AUC changes when changing the parameters were quantified for selected RF, significantly associated to each end-point. The analysis was repeated for the corresponding images with contrast medium and in a sub-group of 29/39 patients scanned on a single scanner. RESULTS: The median tumor volume was 1.57 cm(3) (16%-84% percentiles: 0.62-34.58 cm(3)). RF variability against discretization/interpolation parameters was large: only 21/69 RF showed %COV < 20%. Despite this variability, AUC changes were limited for all end-points: with typical AUC values around 0.75-0.85, AUC ranges for the 27 parameter's set were on average 0.062 (1SD:0.037) for all end-points with maximum %COV equal to 5.5% (mean:2.3%). Performances significantly improved when excluding the 5 mm thickness case and fixing the binning to 64 (mean AUC range: 0.036, 1SD:0.019). Using contrast images or limiting the population to single-scanner patients had limited impact on AUC variability. CONCLUSIONS: The discriminative power of CT RF for panNEN is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning. Twit Text FOAVip Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms ????Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=32673824 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32673824 #FOAvip English Wikipedia <ref>{{Cite pmid|32673824}}</ref> Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms #MMPMID32673824 Loi S; Mori M; Benedetti G; Partelli S; Broggi S; Cattaneo GM; Palumbo D; Muffatti F; Falconi M; De Cobelli F; Fiorino C Phys Med 2020[Aug]; 76 (ä): 125-133 PMID32673824show ga PURPOSE: To explore the variation of the discriminative power of CT radiomic features (RF) against image discretization/interpolation in characterizing pancreatic neuro-endocrine (PanNEN) neoplasms. MATERIALS AND METHODS: Thirty-nine PanNEN patients with pre-surgical high contrast CT available were considered. Image interpolation and discretization parameters were intentionally changed, including pixel size (0.73-2.19 mm(2)), slice thickness (2-5 mm) and binning (32-128 grey levels) and their combination generated 27 parameter's set. The ability of 69 RF in discriminating post-surgically assessed tumor grade (>G1), positive nodes, metastases and vascular invasion was tested: AUC changes when changing the parameters were quantified for selected RF, significantly associated to each end-point. The analysis was repeated for the corresponding images with contrast medium and in a sub-group of 29/39 patients scanned on a single scanner. RESULTS: The median tumor volume was 1.57 cm(3) (16%-84% percentiles: 0.62-34.58 cm(3)). RF variability against discretization/interpolation parameters was large: only 21/69 RF showed %COV < 20%. Despite this variability, AUC changes were limited for all end-points: with typical AUC values around 0.75-0.85, AUC ranges for the 27 parameter's set were on average 0.062 (1SD:0.037) for all end-points with maximum %COV equal to 5.5% (mean:2.3%). Performances significantly improved when excluding the 5 mm thickness case and fixing the binning to 64 (mean AUC range: 0.036, 1SD:0.019). Using contrast images or limiting the population to single-scanner patients had limited impact on AUC variability. CONCLUSIONS: The discriminative power of CT RF for panNEN is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning. |Area Under Curve[MESH] |Contrast Media[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Neoplasm Grading[MESH] |Neuroendocrine Tumors/*diagnostic imaging/pathology/surgery[MESH] |Pancreatic Neoplasms/*diagnostic imaging/pathology/surgery[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH] |Tomography, X-Ray Computed/*methods[MESH]Robustness of CT radiomic features against image discretization and in(epmc).pdf DeepDyve Pubget Overpricing