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Therapy response monitoring in blood plasma from esophageal adenocarcinoma patients using cell-free DNA methylation profiling.
Sci Rep
December 2024
Translational Oncogenomics and Bioinformatics Lab, Center for Medical Biotechnology, VIB-UGent & CRIG, Technologiepark-Zwijnaarde 75, 9052, Ghent, Belgium.
Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Current follow-up methods (serum carcinoembryonic antigen detection and PET-CT) lack sensitivity and reliability, necessitating a novel approach. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue.
View Article and Find Full Text PDFMachine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [F]-FDG-PET-radiomic features.
Jpn J Radiol
December 2024
Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
Objectives: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cancer patients.
Materials And Methods: The study analyzed 52 gallbladder cancer patients who underwent pre-treatment [F]-FDG-PET/CT scans between January 2011 and December 2021. Twenty-seven patients were assigned to the training cohort between January 2011 and January 2018, and the data randomly split into training (70%) and validation (30%) sets.
Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.
Acad Radiol
December 2024
School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.); Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.). Electronic address:
Rationale And Objectives: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
Materials And Methods: A total of 724 pathologically confirmed early invasive lung adenocarcinoma patients were retrospectively included from two centers. Clinical and CT semantic features of the patients were collected, and 3D radiomics features were extracted from nonenhanced CT images.
Impedimetric Biosensors for the Quantification of Serum Biomarkers for Early Detection of Lung Cancer.
Biosensors (Basel)
December 2024
Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Lung cancer is the most common type of cancer diagnosed worldwide and is also among the most fatal. Early detection, before symptoms become evident, is fundamental for patients' survival. Therefore, several lung cancer biomarkers have been proposed to enable a prompt diagnosis, including neuron-specific enolase (NSE) and carcinoembryonic antigen (CEA).
View Article and Find Full Text PDFPoint-of-Care Detection of Carcinoembryonic Antigen (CEA) Using a Smartphone-Based, Label-Free Electrochemical Immunosensor with Multilayer CuONPs/CNTs/GO on a Disposable Screen-Printed Electrode.
Biosensors (Basel)
December 2024
Research Laboratory for Analytical Instrument and Electrochemistry Innovation, Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand.
In order to identify carcinoembryonic antigen (CEA) in serum samples, an innovative smartphone-based, label-free electrochemical immunosensor was created without the need for additional labels or markers. This technology presents a viable method for on-site cancer diagnostics. The novel smartphone-integrated, label-free immunosensing platform was constructed by nanostructured materials that utilize the layer-by-layer (LBL) assembly technique, allowing for meticulous control over the interface.
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