The Detection of Neoplastic Cells Using Objective Cytomorphologic Parameters in Malignant Lymphoma

Pathologic evaluation is the most crucial method for diagnosing malignant lymphomas. However, there are no established diagnostic criteria for evaluating pathologic morphology. We manually circled cell nuclei in the lesions of 10 patients with diffuse large B-cell lymphoma (DLBCL), follicular lympho...

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Bibliographic Details
Published in:Laboratory investigation Vol. 104; no. 3; p. 100302
Main Authors: Nagaishi, Miharu, Miyoshi, Hiroaki, Kugler, Mauricio, Sato, Kensaku, Kohno, Kei, Takeuchi, Mai, Yamada, Kyohei, Furuta, Takuya, Hashimoto, Noriaki, Takeuchi, Ichiro, Hontani, Hidekata, Ohshima, Koichi
Format: Journal Article
Language:English
Published: United States 01.03.2024
Subjects:
Cell Nucleus
Humans
Lymphadenitis
Lymphoma, Follicular - diagnosis
Lymphoma, Large B-Cell, Diffuse - diagnosis
Lymphoma, Large B-Cell, Diffuse - pathology
artificial intelligence
pathology
lymphoma
ISSN:1530-0307, 1530-0307
Online Access:Get more information
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Summary:Pathologic evaluation is the most crucial method for diagnosing malignant lymphomas. However, there are no established diagnostic criteria for evaluating pathologic morphology. We manually circled cell nuclei in the lesions of 10 patients with diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, and reactive lymphadenitis. Seventeen parameters related to nuclear shape, color, and other characteristics were measured. We attempted to compare the statistical differences between these subtypes and extract distinctive disease-specific populations on the basis of these parameters. Statistically significant differences were observed between the different types of lymphoma for many of the 17 parameters. Through t-distributed stochastic neighbor embedding analysis, we extracted a cluster of cells that showed distinctive features of DLBCL and were not found in follicular lymphoma or reactive lymphadenitis. We created a decision tree to identify the characteristics of the cells within that cluster. Based on a 5-fold cross-validation study, the average sensitivity, specificity, and accuracy obtained were 84.1%, 98.4%, and 97.3%, respectively. A similar result was achieved using a validation experiment. Important parameters that indicate the features of DLBCL include Area, ConcaveCount, MaxGray, and ModeGray. By quantifying pathologic morphology, it was possible to objectively represent the cell morphology specific to each lymphoma subtype using quantitative indicators. The quantified morphologic information has the potential to serve as a reproducible and flexible diagnostic tool.
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ISSN:1530-0307
1530-0307
DOI:10.1016/j.labinv.2023.100302