A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas
A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas
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DOI：
10.1016/j.cmpb.2019.06.014
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发表时间：
2019-09-01
影响因子：
6.1
通讯作者：
Orfao, A.
中科院分区：
文献类型：
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作者：
Moraes, L. O.;Pedreira, C. E.;Orfao, A.
背景和目的：在这里，我们提出了一个决策树的方法，不同的世界卫生组织类别的B细胞慢性淋巴细胞增生性疾病，使用流式细胞术数据的鉴别诊断。流式细胞术是白血病和淋巴瘤的免疫表型表征的首选方法，能够处理和注册的多参数数据约数万cells per second.Methods：建议的决策树是由逻辑功能节点，分支整个树成（可能的）不同的白血病/淋巴瘤诊断集。为了避免过度拟合，使用了通过Lasso算法的正则化。该代码可以运行在https://codeocean.com/2018/03/08/a-decision-tree-approach-for-the-differential-diagnosis-of-chronic-lymphoid-leukemias-and-peripheral- b细胞淋巴瘤/或从https://github.com/lauramoraes/bioinformatics-sourcecode下载执行在Matlab.Results：所提出的方法进行了验证，在诊断外周血和骨髓样本从283成熟的淋巴细胞白血病/淋巴瘤患者。所提出的方法在交叉验证测试阶段实现了95%的正确性（100%样本内），61%给出了单一诊断和34%（可能）多个疾病诊断。在样本外验证数据集中获得了类似的结果。生成的树达到最后的诊断后，七个决策nodes.Conclusions：在这里，我们提出了一个成熟的淋巴白血病/淋巴瘤的鉴别诊断，证明是准确的样本验证的决策树方法。整个过程通过七个二进制透明决策节点完成。(C)2019爱思唯尔B. V.保留所有权利。
Background and Objective: Here we propose a decision-tree approach for the differential diagnosis of distinct WHO categories B-cell chronic lymphoproliferative disorders using flow cytometry data. Flow cytometry is the preferred method for the immunophenotypic characterization of leukemia and lymphoma, being able to process and register multiparametric data about tens of thousands of cells per second.Methods: The proposed decision tree is composed by logistic function nodes that branch throughout the tree into sets of (possible) distinct leukemia/lymphoma diagnoses. To avoid overfitting, regularization via the Lasso algorithm was used. The code can be run online at https://codeocean.com/2018/03/08/a-decision-tree-approach-for-the-differential-diagnosis-of-chronic-lymphoid-leukemias-and-peripheral- b-cell-lymphomas/ or downloaded from https://github.com/lauramoraes/bioinformatics-sourcecode to be executed in Matlab.Results: The proposed approach was validated in diagnostic peripheral blood and bone marrow samples from 283 mature lymphoid leukemias/lymphomas patients. The proposed approach achieved 95% correctness in the cross-validation test phase (100% in-sample), 61% giving a single diagnosis and 34% (possible) multiple disease diagnoses. Similar results were obtained in an out-of-sample validation dataset. The generated tree reached the final diagnoses after up to seven decision nodes.Conclusions: Here we propose a decision-tree approach for the differential diagnosis of mature lymphoid leukemias/lymphomas which proved to be accurate during out-of-sample validation. The full process is accomplished through seven binary transparent decision nodes. (C) 2019 Elsevier B.V. All rights reserved.