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Lymphoma, Follicular

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Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Archives of pathology & laboratory medicine
CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relative...

Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Computers in biology and medicine
Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of...

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...

Lymphoma triage from H&E using AI for improved clinical management.

Journal of clinical pathology
AIMS: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes del...