Publications by authors named "D Stocker"

Purpose: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Methods: This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy.

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Purpose: To assess the feasibility and performance of MR elastography (MRE) for quantifying liver fibrosis in patients with and without hepatic iron overload.

Methods: This retrospective single-center study analyzed 139 patients who underwent liver MRI at 3 Tesla including MRE (2D spin-echo EPI sequence) and R2* mapping for liver iron content (LIC) estimation. MRE feasibility and diagnostic performance between patients with normal and elevated LIC were compared.

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 The biodistribution of gallium-68-dotatate (Ga-68-dotatate) and standardized uptake values (SUVs) using non-time-of-flight (TOF) positron emission tomography/computed tomography (PET/CT) cameras is well established. However, with the eventual retirement of older PET cameras and their replacement with newer, highly sensitive TOF PET/CT cameras, where SUV measurements are reportedly higher, updated knowledge of normal SUV range is needed and, to our knowledge, not previously reported. Our objectives are as follows: To establish normal Ga-68-dotatate TOF SUV database for common structures and to aid the visual detection of abnormalities objectively.

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Purpose: To investigate the differences in the visibility and size of abdominal wall hernias in computed tomography (CT) with and without Valsalva maneuver.

Methods: This single-center retrospective study included consecutive patients who underwent abdominal CTs with Valsalva maneuver between January 2018 and January 2022. Inclusion criteria was availability of an additional non-Valsalva CT within 6 months.

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Optimizing complex imaging procedures within Computed Tomography, considering both dose and image quality, presents significant challenges amidst rapid technological advancements and the adoption of machine learning (ML) methods. A crucial metric in this context is the Difference-Detailed Curve, which relies on human observer studies. However, these studies are labor-intensive and prone to both inter- and intra-observer variability.

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