An optical study of drug resistance detection in endometrial cancer cells by dynamic and quantitative phase imaging

Abstract

Platinum chemosensitivity detection plays a vital role during endometrial cancer treatment because chemotherapy responses have profound influences on patient's prognosis. Although several methods can be used to detect drug resistance characteristics, studies on detecting drug sensitivity based on dynamic and quantitative phase imaging of cancer cells are rare. In this study, digital holographic microscopy was applied to distinguish drug‐resistant and nondrug‐resistant endometrial cancer cells. Based on the reconstructed phase images, temporal evolutions of cell height (CH), cell projected area (CPA) and cell volume were quantitatively measured. The results show that change rates of CH and CPA were significantly different between drug‐resistant and nondrug‐resistant endometrial cancer cells. Furthermore, the results demonstrate that morphological characteristics have the potential to be utilized to distinguish the drug sensitivity of endometrial cancer cells, and it may provide new perspectives to establish optical methods to detect drug sensitivity and guide chemotherapy in endometrial cancer.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

“…7 and 8 by the biophysical/geometrical parameters of phase height, area, and eccentricity, which are not in rank order for rank-ordered EM scores within the cancer cell lines. Whereas any single parameter suffers from heterogeneity from cell to cell or may lose sensitivity to some cells or cell responses, 39 multiple training features for machine learning classification regularly achieve higher performance than single-feature classification. 18 Score adaptability stems from flexibility in defining the training dataset-different cell lines or primary cells could be used-as well as the cells to be scored by transfer learning.…”

“…Quantitative imaging and machine learning have the potential to save time, labor, and reduce human error in phenotypic profiling, which could help pathologists and scientists to accurately detect circulating tumor cells, 35 classify cancer cells, 36,37 evaluate the metastatic potential of cancer cells, 38 and assess cancer drug resistance. 39 Thus, machine learning-assisted QPI has great power to aid in interpreting large-scale and high-dimensionality data from cells, potentially enhancing cancer diagnosis and treatment.…”

Simple adaptive mobile phone screen illumination for dual phone differential phase contrast (DPDPC) microscopy

“…However, in future studies quantitative phase information could be extracted, as described in [27], from images obtained with a similar or modified system. This quantitative phase information could expand the potential application space to exploring cellular response to drugs [11], cell malignancy [14], and myriad other applications.…”

“…In the last decade or so, several groups have developed QPI methods, and demonstrated their application to practical problems, including cell-level drug resistance [11], cancer diagnostics and dynamics [12][13][14], red blood cell imaging and characterization [15][16][17], malaria diagnosis [18,19], among many others. Several groups have extended QPI to compact measurement systems, such as mobile phone-based microscopes [20][21][22], or lab-on-chip devices [23].…”

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer

“…QPI is a real-time, high throughput tool for measuring the growth response of individual cells to therapy. However, previous applications of QPI have narrowly focused on measuring the overall sensitivity 26 or toxicity 27 of potential therapies with only limited studies of the heterogeneity of response 28 .…”