최예라 교수

This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.

Magnetic resonance elastography was superior to the gadoxetate disodium-enhancement MRI for HF staging.

Single-slice L2-3 (abdominal CT range) and L1 (chest CT range) analysis best correlated with whole-body composition around 0.90 (coefficient). Multi-slice waist averaging provided a slightly higher correlation of 0.92.

A cutoff of ±2.2 mm can be reliably used to determine true nodule growth on follow-up CT. Solid portion measurements were not reliable in evaluating SSNs' change when readers of initial and follow-up CT were different.

The DLA provided fair-to-good stand-alone performance for the detection of referable thoracic abnormalities in a multicenter consecutive health screening cohort. The DLA showed varied performance according to the different methods of ground truth.

This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. A subset of 244 subjects (60% abnormal CRs) was evaluated. Abnormal findings included mass/nod

The aim of this study was to investigate the association between image characteristics on preoperative chest CT and severe pleural adhesion during surgery in lung cancer patients. We included consecutive 124 patients who underwent lung cancer surgeries. Preoperative chest CT was retrospectively reviewed to assess pleural thickening or calcification, pulmonary calcified nodules, active pulmonary inflammation, extent of emphysema, interstitial pneumonitis, and bronchiectasis in the operated thorax

3D US tumor volume using a personalized 3D-printed model is an accurate and reliable method for the response evaluation in comparison with CT tumor volume.

We observed a meaningful disease control rate of 72% during treatment of WD HG NETs with 177Lu-DOTATATE. In this heavily pre-treated population, more than half of patients received all four treatment cycles with toxicities largely bone marrow-related. As would be expected in WD NETs, the vast majority had alterations in chromatin remodeling genes and no RB1 alterations.

Many subcentimeter sized hypervascular HCCs were frequently missed or not evident on CT at the initial diagnostic workup. CT has limitations for diagnosing HCCs that are <1 cm in size or have atypical enhancement patterns.

<b>BACKGROUND.</b> Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. <b>OBJECTIVE.</b> The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. <b>METHODS.</b> In this retrospective study,

This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.