Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning
Abstract Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenge...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32955-0 |
| _version_ | 1797850098024054784 |
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| author | Cheng Shen Siddarth Rawal Rebecca Brown Haowen Zhou Ashutosh Agarwal Mark A. Watson Richard J. Cote Changhuei Yang |
| author_facet | Cheng Shen Siddarth Rawal Rebecca Brown Haowen Zhou Ashutosh Agarwal Mark A. Watson Richard J. Cote Changhuei Yang |
| author_sort | Cheng Shen |
| collection | DOAJ |
| description | Abstract Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (± 0.2%) and 96% (± 0.2%) for mCTC detection, and 93% (± 1.7%) and 84% (± 3.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (± 0.2%) and 78% (± 0.3%) for mCTC and 58% (± 3.9%) and 56% (± 3.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis. |
| first_indexed | 2024-04-09T18:54:51Z |
| format | Article |
| id | doaj.art-5b629dc6fde54111a85b345a31042892 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T18:54:51Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5b629dc6fde54111a85b345a310428922023-04-09T11:14:10ZengNature PortfolioScientific Reports2045-23222023-04-0113111310.1038/s41598-023-32955-0Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learningCheng Shen0Siddarth Rawal1Rebecca Brown2Haowen Zhou3Ashutosh Agarwal4Mark A. Watson5Richard J. Cote6Changhuei Yang7Department of Electrical Engineering, California Institute of TechnologyDepartment of Pathology and Immunology, Washington University School of MedicineDepartment of Pathology and Immunology, Washington University School of MedicineDepartment of Electrical Engineering, California Institute of TechnologyDepartment of Biomedical Engineering, DJTMF Biomedical Nanotechnology Institute, University of MiamiDepartment of Pathology and Immunology, Washington University School of MedicineDepartment of Pathology and Immunology, Washington University School of MedicineDepartment of Electrical Engineering, California Institute of TechnologyAbstract Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (± 0.2%) and 96% (± 0.2%) for mCTC detection, and 93% (± 1.7%) and 84% (± 3.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (± 0.2%) and 78% (± 0.3%) for mCTC and 58% (± 3.9%) and 56% (± 3.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis.https://doi.org/10.1038/s41598-023-32955-0 |
| spellingShingle | Cheng Shen Siddarth Rawal Rebecca Brown Haowen Zhou Ashutosh Agarwal Mark A. Watson Richard J. Cote Changhuei Yang Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning Scientific Reports |
| title | Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| title_full | Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| title_fullStr | Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| title_full_unstemmed | Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| title_short | Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| title_sort | automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning |
| url | https://doi.org/10.1038/s41598-023-32955-0 |
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