Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, the...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2073-4409/14/1/14 |
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| author | Mikhail Petrov Nadezhda Makarova Amir Monemian Jean Pham Małgorzata Lekka Igor Sokolov |
| author_facet | Mikhail Petrov Nadezhda Makarova Amir Monemian Jean Pham Małgorzata Lekka Igor Sokolov |
| author_sort | Mikhail Petrov |
| collection | DOAJ |
| description | The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique’s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (<i>p</i> < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection. |
| format | Article |
| id | doaj-art-99f11a8c7c044092a3ba1a1bee3379b8 |
| institution | Kabale University |
| issn | 2073-4409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Cells |
| spelling | doaj-art-99f11a8c7c044092a3ba1a1bee3379b82025-01-10T13:16:15ZengMDPI AGCells2073-44092024-12-011411410.3390/cells14010014Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine LearningMikhail Petrov0Nadezhda Makarova1Amir Monemian2Jean Pham3Małgorzata Lekka4Igor Sokolov5Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USADepartment of Mechanical Engineering, Tufts University, Medford, MA 02155, USACellens, Inc., 529 Main Street, Suite 1M6, Boston, MA 02129, USACellens, Inc., 529 Main Street, Suite 1M6, Boston, MA 02129, USADepartment of Biophysical Microstructures, Institute of Nuclear Physics PAN, PL-31342 Kraków, PolandDepartment of Mechanical Engineering, Tufts University, Medford, MA 02155, USAThe development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique’s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (<i>p</i> < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection.https://www.mdpi.com/2073-4409/14/1/14imagingnanomedicinecanceratomic force microscopyringing modeartificial intelligence |
| spellingShingle | Mikhail Petrov Nadezhda Makarova Amir Monemian Jean Pham Małgorzata Lekka Igor Sokolov Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning Cells imaging nanomedicine cancer atomic force microscopy ringing mode artificial intelligence |
| title | Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning |
| title_full | Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning |
| title_fullStr | Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning |
| title_full_unstemmed | Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning |
| title_short | Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning |
| title_sort | detection of human bladder epithelial cancerous cells with atomic force microscopy and machine learning |
| topic | imaging nanomedicine cancer atomic force microscopy ringing mode artificial intelligence |
| url | https://www.mdpi.com/2073-4409/14/1/14 |
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