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Milanese, Gianluca; Mannil, Manoj; Martini, Katharina; Maurer, Britta; Alkadhi, Hatem; Frauenfelder, Thomas

Quantitative CT texture analysis for diagnosing systemic sclerosis : Effect of iterative reconstructions and radiation doses

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  • Media type: E-Article
  • Title: Quantitative CT texture analysis for diagnosing systemic sclerosis : Effect of iterative reconstructions and radiation doses : Effect of iterative reconstructions and radiation doses
  • Contributor: Milanese, Gianluca; Mannil, Manoj; Martini, Katharina; Maurer, Britta; Alkadhi, Hatem; Frauenfelder, Thomas
  • Published: Ovid Technologies (Wolters Kluwer Health), 2019
  • Published in: Medicine, 98 (2019) 29, Seite e16423
  • Language: English
  • DOI: 10.1097/md.0000000000016423
  • ISSN: 0025-7974; 1536-5964
  • Keywords: General Medicine
  • Origination:
  • Footnote:
  • Description: <jats:sec> <jats:title>Abstract</jats:title> <jats:p>To test whether texture analysis (TA) can discriminate between Systemic Sclerosis (SSc) and non-SSc patients in computed tomography (CT) with different radiation doses and reconstruction algorithms.</jats:p> <jats:p>In this IRB-approved retrospective study, 85 CT scans at different radiation doses [49 standard dose CT (SDCT) with a volume CT dose index (CTDIvol) of 4.86 ± 2.1 mGy and 36 low-dose (LDCT) with a CTDIvol of 2.5 ± 1.5 mGy] were selected; 61 patients had Ssc (“cases”), and 24 patients had no SSc (“controls”). CT scans were reconstructed with filtered-back projection (FBP) and with sinogram-affirmed iterative reconstruction (SAFIRE) algorithms. 304 TA features were extracted from each manually drawn region-of-interest at 6 pre-defined levels: at the midpoint between lung apices and tracheal carina, at the level of the tracheal carina, and 4 between the carina and pleural recesses. Each TA feature was averaged between these 6 pre-defined levels and was used as input in the machine learning algorithm artificial neural network (ANN) with backpropagation (MultilayerPerceptron) for differentiating between SSc and non-SSc patients.</jats:p> <jats:p>Results were compared regarding correctly/incorrectly classified instances and ROC-AUCs.</jats:p> <jats:p>ANN correctly classified individuals in 93.8% (AUC = 0.981) of FBP-LDCT, in 78.5% (AUC = 0.859) of FBP-SDCT, in 91.1% (AUC = 0.922) of SAFIRE3-LDCT and 75.7% (AUC = 0.815) of SAFIRE3-SDCT, in 88.1% (AUC = 0.929) of SAFIRE5-LDCT and 74% (AUC = 0.815) of SAFIRE5-SDCT.</jats:p> <jats:p>Quantitative TA-based discrimination of CT of SSc patients is possible showing highest discriminatory power in FBP-LDCT images.</jats:p> </jats:sec>
  • Access State: Open Access