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Ko BS, Wang YF, Li JL, Li CC, Weng PF, Hsu SC, Hou HA, Huang HH, Yao M, Lin CT, Liu JH, Tsai CH, Huang TC, Wu SJ, Huang SY, Chou WC, Tien HF, Lee CC, Tang JL. Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome. EBioMedicine 2018; 37:91-100. [PMID: 30361063 PMCID: PMC6284584 DOI: 10.1016/j.ebiom.2018.10.042] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/14/2018] [Accepted: 10/14/2018] [Indexed: 12/27/2022] Open
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 < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML. Interpretation Through 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.
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Affiliation(s)
- Bor-Sheng Ko
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Fen Wang
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Jeng-Lin Li
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Chi-Cheng Li
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan; Center of Stem Cell and Precision Medicine, Buddhist Tzu Chi General Hospital, Hualien, Taiwan
| | - Pei-Fang Weng
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Szu-Chun Hsu
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsin-An Hou
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Huai-Hsuan Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming Yao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chien-Ting Lin
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Jia-Hau Liu
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Cheng-Hong Tsai
- Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan
| | - Tai-Chung Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shang-Ju Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shang-Yi Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Chien Chou
- Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hwei-Fang Tien
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chi-Chun Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan; Joint Research Center for AI Technology and All Vista Healthcare, Ministry of Science and Technology, Taiwan.
| | - Jih-Luh Tang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Tai-Cheng Stem Cell Therapy Center, National Taiwan University, Taipei, Taiwan.
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Cheng M, Zhang X, Guo XJ, Wu ZF, Weng PF. The interaction effect and mechanism between tea polyphenols and intestinal microbiota: Role in human health. J Food Biochem 2017. [DOI: 10.1111/jfbc.12415] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Mei Cheng
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P.R. China
| | - Xin Zhang
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P.R. China
- Key Laboratory of Animal Protein Deep Processing Technology of Zhejiang; Ningbo University; Ningbo 315211 P.R. China
| | - Xiao-Jing Guo
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P.R. China
| | - Zu-Fang Wu
- Key Laboratory of Animal Protein Deep Processing Technology of Zhejiang; Ningbo University; Ningbo 315211 P.R. China
| | - Pei-Fang Weng
- Key Laboratory of Animal Protein Deep Processing Technology of Zhejiang; Ningbo University; Ningbo 315211 P.R. China
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Jiang JS, Cheng M, Zhang X, Wu ZF, Weng PF. Effects of (-)-epigallocatechin 3-O
-(3-O
-methyl) gallate (EGCG3″Me)- phospholipids complex on pancreatic α-amylase and lipase activities. J Food Biochem 2017. [DOI: 10.1111/jfbc.12388] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Jin-Shu Jiang
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P. R. China
| | - Mei Cheng
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P. R. China
| | - Xin Zhang
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P. R. China
- Key Laboratory of Animal Protein Deep Processing Technology of Zhejiang; Ningbo University; Ningbo 315211 P. R. China
| | - Zu-Fang Wu
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P. R. China
| | - Pei-Fang Weng
- Department of Food Science and Engineering, School of Marine Sciences; Ningbo University; Ningbo 315211 P. R. China
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