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Three-dimensional (3D) tumor spheroid models have gained increased recognition as important tools in cancer research and anti-cancer drug development. However, currently available imaging approaches employed in high-throughput screening drug discovery platforms e.g. bright field, phase contrast, and fluorescence microscopies, are unable to resolve 3D structures deep inside (>50 μm) tumor spheroids. In this study, we established a label-free, non-invasive optical coherence tomography (OCT) imaging platform to characterize 3D morphological and physiological information of multicellular tumor spheroids (MCTS) growing from ~250 μm up to ~600 μm in height over 21 days. In particular, tumor spheroids of two cell lines glioblastoma (U-87 MG) and colorectal carcinoma (HCT 116) exhibited distinctive evolutions in their geometric shapes at late growth stages. Volumes of MCTS were accurately quantified using a voxel-based approach without presumptions of their geometries. In contrast, conventional diameter-based volume calculations assuming perfect spherical shape resulted in large quantification errors. Furthermore, we successfully detected necrotic regions within these tumor spheroids based on increased intrinsic optical attenuation, suggesting a promising alternative of label-free viability tests in tumor spheroids. Therefore, OCT can serve as a promising imaging modality to characterize morphological and physiological features of MCTS, showing great potential for high-throughput drug screening.
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Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning
Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.
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- PAR ID:
- 10493991
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Biomedical Optics Express
- Volume:
- 15
- Issue:
- 4
- ISSN:
- 2156-7085
- Format(s):
- Medium: X Size: Article No. 2014
- Size(s):
- Article No. 2014
- Sponsoring Org:
- National Science Foundation
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