Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro

Hsing-Chuan Hsieh, Cho-Yi Chen, Chung-Hsien Chou, Bou-Yue Peng, Yi-Chen Sun, Tzu-Wei Lin, Yueh Chien, Shih-Hwa Chiou, Kai-Feng Hung and Henry Horng-Shing Lu

PLOS ONE, 2024, vol. 19, issue 11, 1-19

Abstract: Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and environmental challenges, including chemotherapy, cause a cell state transition, which is accompanied by a continuous morphological alteration that is often extremely difficult to recognize even by direct microscopic inspection. To determine whether deep learning-based image analysis enables the detection of cell shape reflecting a crucial cell state alteration, we used the oral cancer cell line resistant to chemotherapy but having cell morphology nearly indiscernible from its non-resistant parental cells. We then implemented the automatic approach via deep learning methods based on EfficienNet-B3 models, along with over- and down-sampling techniques to determine whether image analysis of the Convolutional Neural Network (CNN) can accomplish three-class classification of non-cancer cells vs. cancer cells with and without chemoresistance. We also examine the capability of CNN-based image analysis to approximate the composition of chemoresistant cancer cells within a population. We show that the classification model achieves at least 98.33% accuracy by the CNN model trained with over- and down-sampling techniques. For heterogeneous populations, the best model can approximate the true proportions of non-chemoresistant and chemoresistant cancer cells with Root Mean Square Error (RMSE) reduced to 0.16 by Ensemble Learning (EL). In conclusion, our study demonstrates the potential of CNN models to identify altered cell shapes that are visually challenging to recognize, thus supporting future applications with this automatic approach to image analysis.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0310304

DOI: 10.1371/journal.pone.0310304

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