Classification of drug-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning

Lu Xin; Wen Xiao; Huanzhi Zhang; Xiaosu Yi; Xiao ping Li; Feng Pan

doi:10.1117/12.2676101

11 August 2023 Classification of drug-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning

Lu Xin, Wen Xiao, Huanzhi Zhang, Xiaosu Yi, Xiao ping Li, Feng Pan

Author Affiliations +

Proceedings Volume 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI; 126220X (2023) https://doi.org/10.1117/12.2676101
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

For ovarian cancer patients, paclitaxel remains to be primary chemotherapy drug. Once drug resistance is developed, it will lead to tumor progression and metastasis during chemotherapy. Many studies have shown that the development of drug resistance in cancer cells can cause morphological changes. Digital holographic microscopy is an interferometric imaging technique that can obtain 3D quantitative morphological information of label-free cells. Combining with microfluidics enables high-throughput holographic image acquisition of suspended cells. In this work, four kinds of epithelial ovarian cancer cells with different drug sensitivity, SKOV3 cells, SKOV3_Ta_2μM cells, SKOV3_Ta_8μM cells, and SKOV3_Ta_20μM cells were studied. Several machine learning algorithms were used to perform multi-classification on the extracted morphological features of four types of cells. Then, we employ the SHapley Additive exPlanations (SHAP) method to interpret the classification model. The SHAP value of each feature is calculated and sorted to obtain the important morphological features.

Conference Presentation

Citation Download Citation

Lu Xin, Wen Xiao, Huanzhi Zhang, Xiaosu Yi, Xiao ping Li, and Feng Pan "Classification of drug-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning", Proc. SPIE 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, 126220X (11 August 2023); https://doi.org/10.1117/12.2676101

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