Notes

Mobile phone request for teens along with anorexia therapy: a primary acceptability as well as buyer experience evaluation.
e nodule and positively correlated to the energy delivered per volume. • When planning the treatment, the total energy to deliver can be calculated by using a simple formula nodular volume × 2670 J.
Cardiac dysfunction is commonly noted in patients with idiopathic inflammatory myopathies (IIMs). This study aimed to investigate the characteristics of cardiac dysfunction using cardiac magnetic resonance (CMR) in polymyositis (PM), dermatomyositis (DM) and necrotising myositis (NM).

Fifty-one patients with IIMs and 20 matched healthy controls (HCs) were assessed using CMR examination. selleck chemicals The clinical data, cardiac serum markers and autoimmune antibodies were determined for all patients. Cardiac involvement was identified by myocardial native T1, extracellular volume (ECV), late gadolinium enhancement (LGE) and left ventricular ejection fraction (LVEF).

Different subtypes of IIMs showed different patterns of LGE and varying degrees of myocardial damage. The PM subgroup showed higher native T1 (p = 0.010) and ECV (p = 0.000) than the HCs. The prevalence of LGE was comparable between the PM and DM subgroups (40.0% vs. 31.6%, p = 0.741); however, it was higher in the PM subgroup than in the NM subgroup (40% magnetic resonance. • The NT-proBNP levels could reflect focal and diffuse myocardial damage in patients with IIMs.
The strategically acquired gradient echo (STAGE) protocol, developed for 3T scanners, allows one to derive quantitative maps such as T1, T2*, proton density, and quantitative susceptibility mapping in about 5 min. Our aim was to adapt the STAGE sequences for 1.5T scanners which are still commonly used in clinical practice. Furthermore, the accuracy and repeatability of the STAGE-derived T1 estimate were tested.

Flip angle (FA) optimization was performed using a theoretical simulation by maximizing signal-to-noise ratio, contrast-to-noise ratio, and T1 precision. The FA choice was further refined with the ISMRM/NIST phantom and in vivo acquisitions. The accuracy of the T1 estimate was assessed by comparing STAGE-derived T1 values with T1 maps obtained with an inversion recovery sequence. T1 accuracy was investigated for both the phantom and in vivo data. Finally, one subject was acquired 10 times once a week and a group of 27 subjects was scanned once. The T1 coefficient of variation (COV) was computed to protocol makes it possible to derive quantitative maps (i.e., T1, T2*, PD, and QSM) in about 7 min at 1.5T. • The T1 estimate derived from the STAGE protocol showed good accuracy and repeatability.
Liver Imaging Reporting and Data System (LI-RADS) for hepatocellular carcinoma (HCC) diagnosis in high-risk patients is a dynamic system, which was lastly updated in 2018. We aimed to evaluate the accuracy for HCC diagnosis of LI-RADS v2018 with magnetic resonance imaging (MRI) with extracellular contrast for solitary nodules ≤ 20 mm detected during ultrasound (US) surveillance in cirrhotic patients, with particular interest in those observations categorized as LI-RADS 3.

Between November 2003 and February 2017, we included 262 consecutive cirrhotic patients with a newly US-detected solitary ≤ 20-mm nodule. A LI-RADS (LR) v2018 category was retrospectively assigned. The diagnostic accuracy for each LR category was described, and the main MRI findings associated with HCC diagnosis were analyzed.

Final diagnoses were as follows 197 HCC (75.2%), 5 cholangiocarcinoma (1.9%), 2 metastasis (0.8%), and 58 benign lesions (22.1%); 0/15 (0%) LR-1, 6/26 (23.1%) LR-2, 51/74 (68.9%) LR-3, 11/12 (91.7%) LR-4, 126/127e diagnosed as HCC. • The high probability of HCC in US-detected LR-3 observations justifies triggering an active diagnostic work-up if intended to diagnose HCC at a very early stage.
 15 mm (95.2%) were diagnosed as HCC. • The high probability of HCC in US-detected LR-3 observations justifies triggering an active diagnostic work-up if intended to diagnose HCC at a very early stage.
To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT.

Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists.

In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92,ICH patients.
• Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.
To compare the performance of current guidelines applicable to the diagnosis of hepatocellular carcinomas (HCCs) using gadoxetic acid-enhanced magnetic resonance imaging (MRI).

Two hundred and forty-one hepatic lesions (149 HCCs, six other malignancies, 86 benign lesions) in 177 patients at risk of HCC without a history of previous treatment for hepatic malignancy in a tertiary center were retrospectively reviewed. Either histopathology results or long-term (> 24 months) follow-up images were used as a standard of reference. All lesions were categorized according to the Liver Imaging Reporting and Data System (LI-RADS), European Association for the Study of the Liver (EASL), Asian Pacific Association for the Study of the Liver (APASL), and Korean Liver Cancer Study Group-National Cancer Center (KLCSG-NCC) guidelines. The sensitivity and specificity thereof were assessed using a generalized estimation equation.

For gadoxetic acid-enhanced MRI, LI-RADS (95%, 95% confidence interval [CI] 88-98) and EASL (94%, 95% CI 86-97) yielded the highest specificity, while EASL yielded the lowest sensitivity (54% [95% CI 46-62]).
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