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Purpose: To investigate the predictive values of gray-scale ultrasound (G-US) and strain elastic ultrasound (SE-US) radiomic features for cervical tuberculous lymphadenitis (CTL).
Material And Methods: The G-US and SE-US images of 147 patients with pathologically confirmed CTL and 69 non-CTL patients were retrospectively analyzed. A total of 851 imaging features were extracted. The patients were divided into the training set and test set in 7:3 ratio. In the training set, the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used for feature selection and modeling. The diagnostic power of G-US and SE-US ultrasound radiomics in identifying CTL was evaluated in test set.
Results: The G-US and SE-US have finally selected 10 and 14 features, respectively. In the G-US group, the diagnostic sensitivity, specificity and accuracy of the training set were 69.7%, 85.7% and 70.0%, respectively, and those values in the test set were 81.3%, 70.0% and 86.4%, respectively. The SE-US group had a sensitivity of 71.7%, a specificity of 81.6%, and an accuracy of 67.0% in the training set, and those parameters in the test set were 81.0%, 75.0%, and 83.7%, respectively. In the G-US group, the positive and negative predictive value of the training set were 0.519 and 0.901, respectively, and those values in the test set were 0.700 and 0.864, respectively. The SE-US group had a positive predictive value of 0.541, and a negative predictive value of 0.885 in the training set, and those parameters in the test set were 0.682 and 0.878, respectively. By Delong test, G-US and SE-US groups showed no significant differences in diagnostic performance between the training and test sets.
Conclusions: The ultrasound radiomic features of G-US and SE-US exhibited certain predictive potential in detecting CTL, providing a new non-invasive method for clinicians to more accurately evaluate patients with CTL.
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http://dx.doi.org/10.1016/j.clinimag.2022.03.005 | DOI Listing |
SAR QSAR Environ Res
September 2025
Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Evaluating the permeability of different molecular structures across the Caco-2 cell line is crucial for drug discovery and development. The present study primarily focuses on developing machine learning-based multiclass classification models for predicting the permeability of molecules across the Caco-2 cell line. However, the class imbalance in permeability datasets poses a significant challenge for developing predictive models in the case of multiclass analysis.
View Article and Find Full Text PDFBr J Nurs
September 2025
Senior Director Medical and Clinical Affairs, Convatec Technology Centre, Deeside, UK.
Background: The Neria™ Guard infusion set is indicated for the infusion of several medications for Parkinson's and pain-management therapy.
Aim: The aim of this study was to explore the impact of the Neria Guard infusion set on patients and health professionals from the perspective of nurses.
Method: Two surveys were distributed to nurses: one targeting nurses who use Neria Guard for Parkinson's patients, and one for those who use it for palliative care patients.
J Magn Reson Imaging
September 2025
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.
Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.
Study Type: Retrospective.
Int J Med Inform
September 2025
School of Psychology & Public Health, La Trobe University, Melbourne, Victoria, Australia.
Background: As healthcare systems increasingly embrace digital transformation, the need for a specialised digital health workforce, distinct from general clinical or IT roles, has become paramount. This study offers a national review of digital health education (DHE) offerings in Australian universities, with a focus on how current curricula support the development of advanced, workforce-ready skills in areas such as health informatics, data analytics, digital implementation, and leadership.
Methods: A systematic web-based review was conducted across all 42 Australian universities, drawing on publicly available resources including official handbooks, course catalogues, and subject guides.
J Cardiovasc Electrophysiol
September 2025
Northwell Cardiovascular Institute, Center for Arrhythmias, New Hyde Park, New York, USA.
Background: Atrial fibrillation (AF) and heart failure (HF) frequently coexist in patients, with the development of AF often preceding HF decompensation. We sought to evaluate whether daily remote monitoring of ICD parameters could predict AF occurrence using machine learning techniques in a real-world cohort.
Methods: Data from patients with primary prevention ICDs transmitted daily to the Northwell centralized remote monitoring center between 2012 and 2021 were extracted.