A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas.
Classification
Diagnosis
Flow cytometry
Hierarchical tree
Lymphomas
Machine learning
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
04
12
2018
revised:
07
06
2019
accepted:
12
06
2019
entrez:
17
8
2019
pubmed:
17
8
2019
medline:
23
2
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND AND OBJECTIVE
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
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
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
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.
Identifiants
pubmed: 31416565
pii: S0169-2607(18)31736-X
doi: 10.1016/j.cmpb.2019.06.014
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
85-90Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.