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http://dx.doi.org/10.1186/1758-2946-6-S1-O13 | DOI Listing |
Comput Methods Programs Biomed
November 2025
Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Greece. Electronic address:
Background: Lung cancer is the leading cause of cancer-related mortality globally. Early detection of high-risk patients for local or distant metastasis is challenging for better monitoring and treatment planning. Machine learning models have been proposed for diagnosis and prediction of metastasis risk.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
August 2025
From A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA (EB, CAT, AM, VTB, EH, JAS, ECK, NT, CM); from La Sapienza University, Department of Human Neurosciences, Rome, Italy (EB), From the Medical Physics Section, Faculty of Medicin
Background And Purpose: In multiple sclerosis (MS), the choroid plexus is thought to promote and sustain the disease immunopathological inflammatory process. However, its association with cortical pathology and disease progression is still uncertain. We aim to characterize choroid plexus enlargement and evolution in MS, its relationship with imaging markers of compartmentalized inflammation and clinical outcome.
View Article and Find Full Text PDFInsights Imaging
August 2025
Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Objectives: This study develops a machine learning model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and clinical data to preoperatively differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), addressing limitations of conventional diagnostics.
Materials And Methods: This retrospective study included 280 patients (HCC = 160, ICC = 80, HIPT = 40) who underwent DCE-MRI from 2008 to 2024 at three hospitals. Radiomics features and clinical data were extracted and analyzed using LASSO regression and machine learning algorithms (Logistic Regression, Random Forest, and Extreme Gradient Boosting), with class weighting (HCC:ICC:HIPT = 1:2:4) to address class imbalance.
Ophthalmol Sci
June 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Purpose: Age-related cataract is the leading cause of vision impairment. Researchers have utilized various imaging modalities, including slit beam, diffuse anterior segment, and retinal imaging, to develop deep learning (DL) algorithms for automated cataract analysis. However, the comparative performance of these algorithms across different ocular imaging modalities remains unevaluated, mainly due to the absence of standardized test sets across studies.
View Article and Find Full Text PDFComput Biol Chem
August 2025
Department of Biomedical Sciences, The Apollo University, Murukambattu, Chittoor 517127, Andhra Pradesh, India.. Electronic address:
Antigenic peptide (AP) prediction is one of the most important roles in improve vaccine design and interpreting immune responses. This paper develops a Multi-Level Pooling-based Transformer (MLPT) model, which improves the accuracy and efficiency of predicting T-cell epitopes (TCEs). The model has utilized peptide sequences from the Immune Epitope Database (IEDB) and utilized a refined Kolaskar & Tongaonkar algorithm for feature extraction as well as a Self-Improved Black-winged Kite optimization algorithm to optimize the scoring matrix.
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