AI Medical Compendium Topic

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Lymphoma, B-Cell

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Novel in Vivo and in Vitro Pharmacokinetic/Pharmacodynamic-Based Human Starting Dose Selection for Glofitamab.

Journal of pharmaceutical sciences
We present a novel approach for first-in-human (FIH) dose selection of the CD20xCD3 bispecific antibody, glofitamab, based on pharmacokinetic/pharmacodynamic (PKPD) assessment in cynomolgus monkeys to select a high, safe starting dose, with cytokine ...

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Blood cancer journal
Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in ma...

Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self-organiz...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

Frontiers in immunology
High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed depending on fluorescence hybridization (FISH), an expensive and laborious analysis. In order to identify H...

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Cytometry. Part B, Clinical cytometry
Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artifici...

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

PLoS computational biology
The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are importan...

A Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

British journal of haematology
Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytogenetics and molecular biology) provides comprehensive diagnostic understandings. However, MBN subtypi...