Computational Flow Cytometry Accurately Identifies Sezary Cells Based on Simplified Aberrancy and Clonality Features
Flow cytometric identification of circulating neoplastic cells (Sezary cells) in patients with mycosis fungoides (MF) and Sezary syndrome (SS) is essential for diagnosis, staging and prognosis. While recent advances have improved the performance of this laboratory assay, the complex immunophenotype of Sezary cells and overlap with reactive T cells demand a high level of analytic expertise. We utilized machine learning to simplify this analysis using only 2 pre-defined Sezary cell-gating plots. We studied 114 samples from 59 patients with SS/MF, and 66 samples from unique patients with inflammatory dermatoses. (Source: Jour...
Source: Journal of Investigative Dermatology - January 16, 2024 Category: Dermatology Authors: Jansen N. Seheult, Matthew J. Weybright, Dragan Jevremovic, Min Shi, Horatiu Olteanu, Pedro Horna Tags: Original Article Source Type: research
Allogeneic transplantation and cellular therapies in cutaneous T-cell lymphoma
Expert Rev Anticancer Ther. 2024 Jan-Feb;24(1-2):41-58. doi: 10.1080/14737140.2024.2305356. Epub 2024 Feb 12.ABSTRACTINTRODUCTION: Mycosis fungoides (MF) and Sezary syndrome (SS) are the most common types of cutaneous T-cell lymphoma. Although many available treatments offer temporary disease control, allogeneic hematopoietic stem cell transplant (allo-HSCT) is the only curative treatment option for advanced stage MF and SS. CAR T-cell therapy is a promising new avenue for treatment.AREAS COVERED: In this review, we discuss the evidence supporting the use of allo-HSCT for the treatment of MF/SS, including disease status at...
Source: Expert Review of Anticancer Therapy - January 15, 2024 Category: Cancer & Oncology Authors: Amrita Goyal Francine Foss Source Type: research
Unsupervised SoftOtsuNet Augmentation for Clinical Dermatology Image Classifiers
AMIA Annu Symp Proc. 2024 Jan 11;2023:329-338. eCollection 2023.ABSTRACTData Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clinical dermatology images often contain irrelevant background information,such as furniture and objects in the frame. DNNs make use of that information when optimizing the loss function. Data augmentation methods that preserve this information risk creating biases in the DNN's understanding (for example, that...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Miguel Dominguez John T Finnell Source Type: research
