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Piansaddhayanon C, Koracharkornradt C, Laosaengpha N, Tao Q, Ingrungruanglert P, Israsena N, Chuangsuwanich E, Sriswasdi S. Label-free tumor cells classification using deep learning and high-content imaging. Sci Data 2023; 10:570. [PMID: 37634014 PMCID: PMC10460430 DOI: 10.1038/s41597-023-02482-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/16/2023] [Indexed: 08/28/2023] Open
Abstract
Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.
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Affiliation(s)
- Chawan Piansaddhayanon
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Chonnuttida Koracharkornradt
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Napat Laosaengpha
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Qingyi Tao
- NVIDIA AI Technology Center, Singapore, Singapore
| | - Praewphan Ingrungruanglert
- Center of Excellence for Stem Cell and Cell Therapy, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Nipan Israsena
- Center of Excellence for Stem Cell and Cell Therapy, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
- Department of Pharmacology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Ekapol Chuangsuwanich
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
- Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
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