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[F]FDG PET/CT volumetric biomarkers for non-invasive prediction of HER2 expression in breast cancer patients.
Asia Ocean J Nucl Med Biol
January 2025
Nuclear Medicine Unit, National Cancer Institute (NCI), Cairo University, Cairo, Egypt.
Objectives: to investigate the capability of F-fluorodeoxyglucose positron emission tomography/computed tomography ([F]-FDG PET/CT) derived volumetric parameters to predict human epidermal growth factor receptor 2 (HER2) status in breast cancer patients.
Methods: retrospective study enrolled 47 female patients with breast cancer. All patients had pretreatment [F]-FDG PET/CT.
Phenotyping atherosclerotic plaque and perivascular adipose tissue: signalling pathways and clinical biomarkers in atherosclerosis.
Nat Rev Cardiol
January 2025
Department of Biomedical Sciences, and Department of Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA, USA.
Computed tomography coronary angiography provides a non-invasive evaluation of coronary artery disease that includes phenotyping of atherosclerotic plaques and the surrounding perivascular adipose tissue (PVAT). Image analysis techniques have been developed to quantify atherosclerotic plaque burden and morphology as well as the associated PVAT attenuation, and emerging radiomic approaches can add further contextual information. PVAT attenuation might provide a novel measure of vascular health that could be indicative of the pathogenetic processes implicated in atherosclerosis such as inflammation, fibrosis or increased vascularity.
View Article and Find Full Text PDFMRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies.
Clin Imaging
December 2024
Department of Radiology, Neuroradiology Section, UT Southwestern Medical Center, Dallas, TX 75390, USA. Electronic address:
We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) status. We conducted a comprehensive search across four major databases-Web of Science, PubMed, Scopus, and Embase. All the studies that assessed the performance of radiomics and/or end-to-end DL models for predicting glioma ATRX status were included.
View Article and Find Full Text PDFThe role of ctDNA from liquid biopsy in predicting survival outcomes in HPV-negative head and neck cancer: A meta-analysis.
Oral Oncol
December 2024
Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Pathology, McGill University, Montreal, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada. Electronic address:
The incidence of head and neck cancer (HNC) is on the rise, making it a significant clinical challenge. Human papillomavirus (HPV)-related and HPV-negative HNC exhibit distinct etiopathogenesis and prognoses, requiring targeted approaches for effective management. Conventional tissue biopsies are essential for confirming the diagnosis and locating solid tumors.
View Article and Find Full Text PDFNon-invasive screening for laryngeal cancer using the oral cavity as a proxy for differentiation of laryngeal cancer versus leukoplakia: A novel application of ESS technology and artificial intelligence supported statistical modeling.
Am J Otolaryngol
December 2024
Boston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA. Electronic address:
Objective: This preliminary study tested whether non-invasive, remote Elastic Scattering Spectroscopy (ESS) measurements obtained in the oral cavity can be used as a proxy to accurately differentiate between patients with laryngeal cancer versus laryngeal leukoplakia.
Methods: 20 patients with laryngeal lesions [cancer (n = 10),leukoplakia (n = 10)] were clinically assessed and categorized by otolaryngologists per standard clinical practice. Patient demographics of age, race, sex, and smoking history were collected.
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