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http://dx.doi.org/10.1021/acs.jctc.2c01110 | DOI Listing |
BMC Nephrol
September 2025
School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, China.
J Nucl Med Technol
September 2025
Institute of Nuclear Medicine, First Faculty of Medicine, Charles University and the General University Hospital in Prague, Prague, Czech Republic;
The aim of the study was to validate a new method for semiautomatic subtraction of [Tc]Tc-sestamibi and [Tc]NaTcO SPECT 3-dimensional datasets using principal component analysis (PCA) against the results of parathyroid surgery and to compare its performance with an interactive method for visual comparison of images. We also sought to identify factors that affect the accuracy of lesion detection using the two methods. Scintigraphic data from [Tc]Tc-sestamibi and [Tc]NaTcO SPECT were analyzed using semiautomatic subtraction of the 2 registered datasets based on PCA applied to the region of interest including the thyroid and an interactive method for visual comparison of the 2 image datasets.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
September 2025
Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
J Refract Surg
September 2025
From the Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Purpose: To determine the accuracy of a new machine learning-based open-source IOL formula (PEARLS-DGS) in 100 patients who underwent uncomplicated cataract surgery and had a history of laser refractive surgery for myopic defects.
Methods: The setting for this retrospective study was HUMANITAS Research Hospital, Milan, Italy. Data from 100 patients with a history of photorefractive keratectomy or laser in situ keratomileusis were retrospectively analyzed to assess the accuracy of the formula.