The Detection of Neoplastic Cells Using Objective Cytomorphologic Parameters in Malignant Lymphoma
Copyright © 2023 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:104 |
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| Enthalten in: |
Laboratory investigation; a journal of technical methods and pathology - 104(2024), 3 vom: 05. März, Seite 100302 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nagaishi, Miharu [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.labinv.2023.100302 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a artificial intelligence | |
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| 700 | 1 | |a Kugler, Mauricio |e verfasserin |4 aut | |
| 700 | 1 | |a Sato, Kensaku |e verfasserin |4 aut | |
| 700 | 1 | |a Kohno, Kei |e verfasserin |4 aut | |
| 700 | 1 | |a Takeuchi, Mai |e verfasserin |4 aut | |
| 700 | 1 | |a Yamada, Kyohei |e verfasserin |4 aut | |
| 700 | 1 | |a Furuta, Takuya |e verfasserin |4 aut | |
| 700 | 1 | |a Hashimoto, Noriaki |e verfasserin |4 aut | |
| 700 | 1 | |a Takeuchi, Ichiro |e verfasserin |4 aut | |
| 700 | 1 | |a Hontani, Hidekata |e verfasserin |4 aut | |
| 700 | 1 | |a Ohshima, Koichi |e verfasserin |4 aut | |
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