ArticleFrontiers in bioengineering and biotechnology2023
Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis.
Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Nature-Inspired Solutions: Biomimetic Materials and Adaptive Devices for Precision Urinary Oncology.Cancers · 2026Review
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- CRISPR Applications in HIV Management - Prevention, Diagnosis, Monitoring and Treatment.Current HIV/AIDS reports · 2026Review
- Functional material probes and advanced technologies in organ-on-a-chip characterization.Theranostics · 2026Review
- From molecular networks to translational intervention: current progress in the mechanisms of gemcitabine resistance in bladder cancer.Frontiers in immunology · 2026Review
- Organ-on-a-chip platforms for disease modeling and in vitro diagnostic applications.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence-Aided Microfluidic Cell Culture Systems.Biosensors · 2025Review
- Advances in microfluidic chip technology for cell analysis.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Review
- Review of recent molecular pathology of bladder urothelial carcinoma.Discover oncology · 2025Review
- Development of a Bladder Cancer-on-a-Chip Model to Assess Bladder Cancer Cell Invasiveness.Cancers · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of anticancer drug treatment varies significantly depending on individual patients and the extent of drug resistance. In this study, we developed a validation system based on an organ-on-a-chip integrated with artificial intelligence technologies to predict resistance to anticancer drugs in bladder cancer. As a proof-of-concept, we utilized the gemcitabine-resistant bladder cancer cell line T24 with four distinct levels of drug resistance (parental, early, intermediate, and late). These cells were co-cultured with endothelial cells in a 3D microfluidic chip. A dataset comprising 2,674 cell images from the chips was analyzed using a convolutional neural network (CNN) to distinguish the extent of drug resistance among the four cell groups. The CNN achieved 95.2% accuracy upon employing data augmentation and a step decay learning rate with an initial value of 0.001. The average diagnostic sensitivity and specificity were 90.5% and 96.8%, respectively, and all area under the curve (AUC) values were over 0.988. Our proposed method demonstrated excellent performance in accurately identifying the extent of drug resistance, which can assist in the prediction of drug responses and in determining the appropriate treatment for bladder cancer patients.
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