Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT
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ORIGINAL ARTICLE
Accuracy of radiomics for differentiating diffuse liver diseases on non‑contrast CT Fatemeh Homayounieh1 · Sanjay Saini1 · Leila Mostafavi1 · Ruhani Doda Khera1 · Michael Sühling2 · Bernhard Schmidt2 · Ramandeep Singh1 · Thomas Flohr2 · Mannudeep K. Kalra1 Received: 30 January 2020 / Accepted: 2 June 2020 © CARS 2020
Abstract Purpose Radiomics help move cross-sectional imaging into the domain of quantitative imaging to assess the lesions, their stoma as well as in their temporal monitoring. We applied and assessed the accuracy of radiomics for differentiating healthy liver from diffuse liver diseases (cirrhosis, steatosis, amiodarone deposition, and iron overload) on non-contrast abdomen CT images in an institutional-reviewed board-approved, retrospective study. Methods Our study included 300 adult patients (mean age 63 ± 16 years; 171 men, 129 women) who underwent non-contrast abdomen CT and had either a healthy liver (n = 100 patients) or an evidence of diffuse liver disease (n = 200). The diffuse liver diseases included steatosis (n = 50), cirrhosis (n = 50), hyperdense liver due to amiodarone deposition (n = 50), or iron overload (n = 50). We manually segmented the liver in one section at the level of the porta hepatis (all 300 patients) and then over the entire liver volume (50 patients). Radiomics were estimated for the liver, and statistical comparison was performed with multiple logistic regression and random forest classifier. Results With random forest classifier, the AUC for radiomics ranged between 0.72 (iron overload vs. healthy liver) and 0.98 (hepatic steatosis vs. healthy liver) for differentiating diffuse liver disease from the healthy liver. Combined root mean square and gray-level co-occurrence matrix had the highest AUC (AUC:0.99, p
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