Objective: Breast cancer has become one of the main diseases threatening women's health and lives. Ultrasound (US) is the first diagnostic option for several patients because of its non-radiation, convenient, and low-cost features. Conventional US combined with contrast-enhanced US (CEUS) has improved diagnostic accuracy, while due to the presence of numerous parameters, no international consensus on diagnostic criteria could be attained. Therefore, it is necessary to develop a reliable diagnostic model with the involvement of a few parameters while increasing the diagnostic accuracy.
Methods: Data from 265 patients, including conventional US, CEUS, and postoperative pathological results, were collected. 21 parameters from the conventional US and both qualitative and quantitative aspects of CEUS were analyzed through univariate and multivariate logistic regression analyses. Specific parameters with independent influential factors were identified. A nomogram was subsequently developed to visually represent the contribution and linear weighting of each parameter. The effectiveness of the new model was assessed through calibration curves and the Hosmer-Lemeshow goodness-of-fit test.
Results: Six independent influential factors for breast malignant tumors were identified, including homogeneous echo, lesion vascularity, enhancement mode, enhancement shape, nourishing vessels, and slope. The area under the curve (AUC) values in the training and test datasets were 0.933 and 0.860, respectively. The modified model exhibited satisfactory diagnostic accuracy and operability.
Conclusion: The modified model, despite incorporating fewer parameters, maintained diagnostic accuracy. It is exhibited as a convenient, effective, and easily deployable model for diagnosing malignant breast nodules.
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http://dx.doi.org/10.1016/j.asjsur.2024.08.104 | DOI Listing |
Cir Cir
January 2025
Department of Neurosurgery, Spinal Health Center, Memorial Hospital, Istanbul, Turkey.
Objective: We aimed to elucidate the histopathological pre-diagnosis of cranial gliomas with magnetic resonance imaging (MRI) techniques in gliomas.
Method: A total of 82 glioma patients were enrolled to our study. Pre-operative conventional MRI images (non-contrast T1/T2/flair/contrast-enhanced T1) and advanced MRI images (DAG and ADC mapping, MRI spectroscopy and perfusion MRI [PMRI]) were analyzed.
Noise Health
January 2025
Department of Internal Medicine, Faculty of Medicine, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Background: The effect of background noise on auscultation accuracy for different lung sound classes under standardised conditions, especially at lower to medium levels, remains largely unexplored. This article aims to evaluate the impact of three levels of Gaussian white noise (GWN) on the ability to identify three classes of lung sounds.
Methods And Materials: A pre-post pilot study assessing the impact of GWN on a group of students' ability to identify lung sounds was conducted.
J Neuroophthalmol
October 2024
Department of Ophthalmology (YM, MD, PAL, JWF, TJH, SY), University of Tennessee Health Science Center, Memphis, Tennessee; Department of Ophthalmology (MYK), University of Colorado School of Medicine, Aurora, Colorado; and Department of Genetics, Genomics, and Informatics (SY), University of Tennessee Health Science Center, Memphis, Tennessee.
Background: To evaluate the accuracy of Chat Generative Pre-Trained Transformer (ChatGPT), a large language model (LLM), to assist in diagnosing neuro-ophthalmic diseases based on case reports.
Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases commonly seen by neuro-ophthalmic subspecialists.
J Craniofac Surg
October 2024
Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy.
Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.
Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.
J Craniofac Surg
October 2024
Division of Oral, Facial y Maxillofacial Surgery, Faculty of Dentistry, Universidad de La Frontera.
Background: Artificial intelligence (AI) has been a contribution in recent years to the development of new tools for dental, surgical, and esthetic treatment. In the case of image diagnosis, AI allows automated analysis of some facial parameters. The aim of this study was to evaluate the precision and reproducibility of these IA analyses compared with a human operator.
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