Dataset Information

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

ABSTRACT:

Background

Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.

Methods

From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n?=?4039) and the validation set (n?=?1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set.

Findings

Promising accuracies (84·6% to 92·4%) and AUCs (0·921-0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9?months, p?InterpretationThrough large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests. FUND: This work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103-2314-B-002-185-MY2) of Taiwan.

SUBMITTER: Ko BS

PROVIDER: S-EPMC6284584 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

Ko Bor-Sheng BS Wang Yu-Fen YF Li Jeng-Lin JL Li Chi-Cheng CC Weng Pei-Fang PF Hsu Szu-Chun SC Hou Hsin-An HA Huang Huai-Hsuan HH Yao Ming M Lin Chien-Ting CT Liu Jia-Hau JH Tsai Cheng-Hong CH Huang Tai-Chung TC Wu Shang-Ju SJ Huang Shang-Yi SY Chou Wen-Chien WC Tien Hwei-Fang HF Lee Chi-Chun CC Tang Jih-Luh JL

EBioMedicine 20181022

<h4>Background</h4>Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.<h4>Methods</h4>From 2009 to 2016, 5333 M ...[more]

PMID: 30361063

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Dataset Information

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

Background

Methods

Findings

Publications

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

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