Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2023-08-04 DOI:10.1002/cyto.b.22136

Inès Vergnolle, Theo Ceccomarini, Alban Canali, Jean-Baptiste Rieu, François Vergez

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引用次数: 1

Abstract

Background: Mature B-cell neoplasms are challenging to diagnose due to their heterogeneity and overlapping clinical and biological features. In this study, we present a new workflow strategy that leverages a large amount of flow cytometry data and an artificial intelligence approach to classify these neoplasms.

Methods: By combining mathematical tools, such as classification algorithms and regression tree (CART) models, with biological expertise, we have developed a decision tree that accurately identifies mature B-cell neoplasms. This includes chronic lymphocytic leukemia (CLL), for which cytometry has been extensively used, as well as other non-CLL subtypes.

Results: The decision tree is easy to use and proposes a diagnosis and classification of mature B-cell neoplasms to the users. It can identify the majority of CLL cases using just three markers: CD5, CD43, and CD200.

Conclusion: This approach has the potential to improve the accuracy and efficiency of mature B-cell neoplasm diagnosis.

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使用混合智能决策树来识别成熟的B细胞肿瘤。

背景：成熟的B细胞肿瘤由于其异质性以及重叠的临床和生物学特征，诊断起来很有挑战性。在这项研究中，我们提出了一种新的工作流程策略，该策略利用大量流式细胞术数据和人工智能方法对这些肿瘤进行分类。方法：通过将分类算法和回归树（CART）模型等数学工具与生物学专业知识相结合，我们开发了一种准确识别成熟B细胞肿瘤的决策树。这包括细胞术已被广泛应用的慢性淋巴细胞白血病（CLL），以及其他非CLL亚型。结果：决策树易于使用，为用户提供了成熟B细胞肿瘤的诊断和分类。仅使用CD5、CD43和CD200三种标记物即可识别大多数CLL病例。结论：该方法有可能提高成熟B细胞肿瘤诊断的准确性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464