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Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.
BMC Med Imaging
January 2025
Department of Thoracic Surgery, The Fifth Clinical Medical College of Henan, University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, 450003, China.
Objective: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.
Methods: Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People's Hospital, etc.
MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.
BMC Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China.
Background: Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.
View Article and Find Full Text PDFValidating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare.
Learn Health Syst
January 2025
Bioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USA.
Objectives: Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.
Study Design: Cross-sectional survey.
Enhancing AI Chatbot Responses in Health Care: The SMART Prompt Structure in Head and Neck Surgery.
OTO Open
January 2025
Maxillofacial Surgery Operative Unit, Department of Medicine, Surgery and Pharmacy University of Sassari Sassari Italy.
Objective: This study aims to evaluate the impact of prompt construction on the quality of artificial intelligence (AI) chatbot responses in the context of head and neck surgery.
Study Design: Observational and evaluative study.
Setting: An international collaboration involving 16 researchers from 11 European centers specializing in head and neck surgery.
Predictive modeling of breast cancer-related lymphedema using machine learning algorithms.
Gland Surg
December 2024
Department of Breast Oncology, Hainan Cancer Hospital, Haikou, China.
Background: Breast cancer-related lymphedema (BCRL) is one of the common complications after breast cancer surgery. It can easily lead to limb swelling, deformation and upper limb dysfunction, which has a serious impact on the physical and mental health and quality of life of patients. Previous studies have mostly used statistical methods such as linear regression and logistic regression to analyze the influencing factors, but all of them have certain limitations.
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