AFM Assessing of Multi-Mechanical Cellular Properties for Classification of Graded Bladder Cancer Cell Via Machine Learning Analysis: the Probability of Cancer Early Prognosis

Cellular mechanical properties (CMPs) have been frequently reported as biomarker for cell cancerization to assist objective cytology, compared to the current subjective method dependent on cytomorphology. However, single or dual CMPs cannot always successfully distinguish every kind of malignant cell from its benign counterpart. In this work, we extract 4 CMPs of four different graded bladder cancer (BC) cell lines by AFM-based nanoindentation to generate a CMP database, which is used to train a cancerization-grade classifier by machine learning. The classifier is tested on 4 categories of BC cells at different cancer grades. The classification shows split-independent robustness and an accuracy of 91.25% with an AUC-ROC value of 97.98%. Finally, we also compare our proposed method with traditional invasive cancer prognosis and noninvasive diagnosis relying on cytomorphology, in terms of accuracy, sensitivity and specificity. Unlike former studies focusing on the discrimination between normal and cancerous cells, our study fulfills the classification of 4 graded cell lines at different cancerization stages, and thus provides a potential method for early detection of cancerization.Funding Information: This research was supported by National Key R&D Program of China (no. 2017YFE0112100), the founding from Tianjin Key Laboratory of Equipment Design and Manufacturing Technology (Tianjin University) and independent innovation fund issued by Tianjin University.Declaration of Interests: The other authors declare no conflicts of interest.