AIMC Topic: Rituximab
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Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.
Nature communications
Jul 2, 2025
Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the...
Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial.
BMJ open
Apr 2, 2025
INTRODUCTION: Membranous nephropathy is an autoimmune kidney disease and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. Rituximab is now recommended as first-line therapy for membranous nephropathy. However, Kidney Dise...
F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.
Journal of cancer research and clinical oncology
Mar 28, 2025
PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to predict treatment response in elderly patients with diffuse large B-cell lymphoma (DLBCL).
Cyclophosphamide
Fluorodeoxyglucose F18
Prednisone
Rituximab
Vincristine
Middle Aged
Antineoplastic Combined Chemotherapy Protocols
Treatment Outcome
Radiopharmaceuticals
Positron-Emission Tomography
Humans
Machine Learning
Prognosis
Retrospective Studies
Female
Aged
Aged, 80 and over
Radiomics
Lymphoma, Large B-Cell, Diffuse
Male
Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.
Scientific reports
Feb 15, 2025
This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acq...
Could machine learning revolutionize how we treat immune thrombocytopenia?
British journal of haematology
Aug 5, 2024
The absence of reliable biomarkers in immune thrombocytopenia (ITP) complicates treatment choice, necessitating a trial-and-error approach. Machine learning (ML) holds promise for transforming ITP treatment by analysing complex data to identify predi...
Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.
Frontiers in immunology
Jun 23, 2020
Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adve...
Vitamin D deficiency and supplementation in patients with aggressive B-cell lymphomas treated with immunochemotherapy.
Cancer medicine
Dec 22, 2017
Vitamin D deficiency has been reported to be a negative prognostic factor in elderly patients with aggressive B-cell lymphomas. In vitro data suggest that vitamin D supplementation may enhance rituximab-mediated cytotoxicity. We prospectively assesse...
Machine learning-based classification of diffuse large B-cell lymphoma patients by eight gene expression profiles.
Cancer medicine
Feb 11, 2016
Gene expression profiling (GEP) had divided the diffuse large B-cell lymphoma (DLBCL) into molecular subgroups: germinal center B-cell like (GCB), activated B-cell like (ABC), and unclassified (UC) subtype. However, this classification with prognosti...
Survival Analysis
Cyclophosphamide
Prednisone
Prognosis
Rituximab
Female
Genes, Neoplasm
Antineoplastic Combined Chemotherapy Protocols
Male
Vincristine
Aged
Antibodies, Monoclonal, Murine-Derived
Treatment Outcome
Sensitivity and Specificity
Middle Aged
Germinal Center
Support Vector Machine
Lymphoma, Large B-Cell, Diffuse
Gene Expression Profiling
Databases, Genetic
ROC Curve
Doxorubicin
Humans
Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.
PloS one
Feb 9, 2015
BACKGROUND: In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).
Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.
Clinical and experimental medicine
May 26, 2025
Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This study aimed to evaluate the efficacy of the ibrutinib plus R-ICE regimen and to leverage explainable mac...
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