Appendix 1 Description of Mr. Gascoigne’s Micrometer

Sellers, David

doi:10.1007/978-1-4614-4097-0_17

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“…MR-PDFF was performed according to a previously described protocol 21 22 23 using commercial hardware and software (Resoundant Inc., Rochester, MN, USA; Syngo MR E11, Siemens Healthineers). 13 For PDFF, complex-based chemical shift-encoded water-fat reconstruction techniques were used with six-echo three-dimensional gradient-recalled-echo images, an imaging matrix of 160 × 115, field of view of 380 × 304, and slice thickness of 3 mm without gaps. A low flip angle (4°) was used to minimize the T1 bias between fat and water.…”

Section: Methodsmentioning

confidence: 99%

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Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected Non-Alcoholic Fatty Liver Disease

et al. 2022

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Background This study aimed to assess the diagnostic feasibility of radiomics analysis based on magnetic resonance (MR)-proton density fat fraction (PDFF) for grading hepatic steatosis in patients with suspected non-alcoholic fatty liver disease (NAFLD). Methods This retrospective study included 106 patients with suspected NAFLD who underwent a hepatic parenchymal biopsy. MR-PDFF and MR spectroscopy were performed on all patients using a 3.0-T scanner. Following whole-volume segmentation of the MR-PDFF images, 833 radiomic features were analyzed using a commercial program. Radiologic features were analyzed, including median and mean values of the multiple regions of interest and variable clinical features. A random forest regressor was used to extract the important radiomic, radiologic, and clinical features. The model was trained using 20 repeated 10-fold cross-validations to classify the NAFLD steatosis grade. The area under the receiver operating characteristic curve (AUROC) was evaluated using a classifier to diagnose steatosis grades. Results The levels of pathological hepatic steatosis were classified as low-grade steatosis (grade, 0–1; n = 82) and high-grade steatosis (grade, 2–3; n = 24). Fifteen important features were extracted from the radiomic analysis, with the three most important being wavelet-LLL neighboring gray tone difference matrix coarseness, original first-order mean, and 90th percentile. The MR spectroscopy mean value was extracted as a more important feature than the MR-PDFF mean or median in radiologic measures. Alanine aminotransferase has been identified as the most important clinical feature. The AUROC of the classifier using radiomics was comparable to that of radiologic measures (0.94 ± 0.09 and 0.96 ± 0.08, respectively). Conclusion MR-PDFF-derived radiomics may provide a comparable alternative for grading hepatic steatosis in patients with suspected NAFLD.

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Section: Methodsmentioning

confidence: 99%

“… 4 10 11 12 However, conventional post-processing MR-PDFF methods are limited by the use of mean signal intensity values by drawing the region of interest (ROI). 13 14 …”

Section: Introductionmentioning

confidence: 99%