AIMC Topic: Lymphoma, T-Cell

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AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.

Diagnostic pathology
Presently, pathologists need to stain biopsy samples with standard and antibody-based immunocytochemistry (ICC) reagents for final diagnosis. Antibody reagents take hours to days to perform staining, along with requiring specialized equipment and tec...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

British journal of haematology
There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), epigenetic modifiers (EM) or small molecule inhibitors (SMI) empirically. Ideal therapy at each line r...

Predicting T-Cell Lymphoma in Children From F-FDG PET-CT Imaging With Multiple Machine Learning Models.

Journal of imaging informatics in medicine
This study aimed to examine the feasibility of utilizing radiomics models derived from F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone F-FDG PET/CT scans. Lesions were extracted from PET/CT and...

Learning-Based Classification of B- and T-Cell Lymphoma on Histopathological Images: A Multicenter Study.

European journal of haematology
Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limited biopsy specimens often restrict the use of multiple stains, complicating diagnostic workflows. Lym...