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Development and Psychometric Validation of a Comprehensive Questionnaire to Assess Oncologists' Knowledge of Chemotherapy-Drug Interaction.
J Cancer Educ
January 2025
Department of Pharmacy, Al Rafidain University College, 10001, Baghdad, Iraq.
Chemotherapy-drug interactions (CDIs) pose significant challenges in oncology, affecting treatment efficacy and patient safety. Despite their importance, there is a lack of validated tools to assess oncologists' knowledge of CDIs. This study aimed to develop and validate a comprehensive questionnaire to address this gap and ensure the reliability and validity of the instrument.
View Article and Find Full Text PDFHallmarks of Appetite: A Comprehensive Review of Hunger, Appetite, Satiation, and Satiety.
Curr Obes Rep
January 2025
CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy.
Purpose Of Review: The present review describes the available literature on the physiologic mechanisms that modulate hunger, appetite, satiation, and satiety with a particular focus on well-established and emerging factors involved in the classic satiety cascade model.
Recent Finding: Obesity is a significant risk factor for numerous chronic conditions like cancer, cardiovascular diseases, and diabetes. As excess energy intake is considered by some to be the primary driver of weight gain, tremendous collective effort should be directed toward reducing excessive feeding at the individual and population levels.
Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFWound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning.
J Imaging Inform Med
January 2025
Computer Science Department, University of Geneva, Geneva, Switzerland.
Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area.
View Article and Find Full Text PDFUnlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.
J Imaging Inform Med
January 2025
College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar.
The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics.
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