ArticleACS omega2021
Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 43 citations in OpenAlex.
- Label-free detection of ovarian cancer cells in ascites-related cell models using digital holographic flow cytometry.Biomedical optics express · 2026Article
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Label-free imaging flow cytometry for cell classification based directly on multiple off-axis holographic projections.Journal of biomedical optics · 2025Article
- Imaging CAR-NK cells targeted to HER2 ovarian cancer with human sodium-iodide symporter-based positron emission tomography.European journal of nuclear medicine and molecular imaging · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- IFN‑γ induces apoptosis in gemcitabine‑resistant pancreatic cancer cells.Molecular medicine reports · 2024Article
- Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research.Discover nano · 2024Review
- All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria.Biomedical optics express · 2024Article
- Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer.World journal of surgical oncology · 2024Article
- Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry.APL bioengineering · 2023Article
- Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer.Communications biology · 2022Article
- Advances in Digital Holographic Interferometry.Journal of imaging · 2022Review
- Characterization and identification of cell death dynamics by quantitative phase imaging.Journal of biomedical optics · 2022Article
- Observational
- SPOCK2 Promotes the Malignant Behavior of Ovarian Cancer via Regulation of the Wnt/BioMed research international · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.