Detection and classification of hepatocytes and hepatoma cells using atomic force microscopy and machine learning algorithms

• Published in: Microscopy research and technique Vol. 86; no. 8; pp. 1047 - 1056
• Main Authors: Zeng, Yi, Liu, Xianping, Wang, Zuobin, Gao, Wei, Li, Li, Zhang, Shengli
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2023; Wiley Subscription Services, Inc
• Subjects: Algorithms; atomic force microscope; Atomic force microscopy; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - pathology; Cell culture; Cell morphology; Classification; Cytology; Datasets; Early Detection of Cancer; Hep G2 Cells; Hepatocellular carcinoma; Hepatocytes; Hepatocytes - classification; Hepatocytes - ultrastructure; Hepatoma; Humans; Image processing; Learning algorithms; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Machine Learning; Mechanical properties; Microscopy; Microscopy, Atomic Force; Modulus of elasticity; Morphology; Parameters; Support vector machines; Viscoelasticity; atomic force microscope; machine learning; hepatocellular carcinoma
• ISSN: 1059-910X; 1097-0029; 1097-0029
• DOI: 10.1002/jemt.24384

Hepatocellular carcinoma is a high‐risk malignant tumor. Hepatoma cells are transformed from normal cells and have unique surface nanofeatures in addition to the characteristics of the original cells. In this paper, atomic force microscopy was used to extract the three‐dimensional morphology and mechanical information of HL‐7702 human hepatocytes and SMMC‐7721 and HepG2 hepatoma cells in culture, such as the elastic modulus and viscoelasticity. The characteristics of different cells were compared and analyzed. Finally, the cell morphology and mechanics information were used for training machine learning algorithms. With the trained model, the detection of cells was realized. The classification accuracy was as high as 94.54%, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.99. Thus, hepatocytes and hepatoma cells were accurately identified and assessed. We also compared the classification effects of other machine learning algorithms, such as support vector machine and logistic regression. Our method extracts cellular nanofeatures directly from the surface of cells of unknown type for cell classification. Compared with microscope image‐based analysis and other methods, this approach can avoid the misjudgment that may occur when different doctors have different levels of experience. Thus, the proposed method provides an objective basis for the early diagnosis of hepatocellular carcinoma. Research Highlights The 3D appearance and mechanical characteristics of hepatocellular carcinoma cells are very similar to those of hepatocytes. Application of atomic force microscopy with machine learning algorithm. Collect the data set of nano‐characteristic parameters of the cell. The machine learning algorithms is trained by data set, and its classification effect is better than that of a single nano‐parameter. Atomic force microscopy was used to extract the three‐dimensional morphology and mechanical information of living cells, extract morphology and mechanical nano‐characteristics, and generate datasets.