Faculty, Staff and Student Publications
Publication Date
8-1-2022
Journal
Modern Pathology
Abstract
Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL) is characterized morphologically by numerous small lymphocytes and pale nodules composed of prolymphocytes and paraimmunoblasts known as proliferation centers (PCs). Patients with CLL can undergo transformation to a more aggressive lymphoma, most often diffuse large B-cell lymphoma (DLBCL), known as Richter transformation (RT). An accelerated phase of CLL (aCLL) also may be observed which correlates with subsequent transformation to DLBCL, and may represent an early stage of transformation. Distinguishing PCs in CLL from aCLL or RT can be diagnostically challenging, particularly in small needle biopsy specimens. Available guidelines pertaining to distinguishing CLL from its' progressive forms are limited, subject to the morphologist's experience and are often not completely helpful in the assessment of scant biopsy specimens. To objectively assess the extent of PCs in aCLL and RT, and enhance diagnostic accuracy, we sought to design an artificial intelligence (AI)-based tool to identify and delineate PCs based on feature analysis of the combined individual nuclear size and intensity, designated here as the heat value. Using the mean heat value from the generated heat value image of all cases, we were able to reliably separate CLL, aCLL and RT with sensitive diagnostic predictive values.
Keywords
Artificial Intelligence, Cell Proliferation, Cell Transformation, Neoplastic, Humans, Leukemia, Lymphocytic, Chronic, B-Cell, Lymphoma, Large B-Cell, Diffuse
DOI
10.1038/s41379-022-01015-9
PMID
35132162
PMCID
PMC9329234
PubMedCentral® Posted Date
8-1-2022
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
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