ArticleLab on a chip2024
Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.
Article in Lab on a chip, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
15 citing papers in PubMed.
- Review
- High-throughput drug screening for targeting polyploid cancer cells with an interactive web portal.Cancer letters · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Applying microfluidic technology to interpret the tumor immune microenvironment and cancer immunotherapy.Cancer biology & medicine · 2026Review
- A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography.PloS one · 2026Article
- Deep Tumor Penetration Using Nanoparticle Delivery Systems: Programmed Design Strategies and Emerging Evaluation Platforms.International journal of nanomedicine · 2026Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- A knowledge-driven deep learning framework for organoid morphological segmentation and characterization.BMC biology · 2025Article
- Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.Bioengineering (Basel, Switzerland) · 2025Review
- Label-Free Machine Learning Prediction of Chemotherapy on Tumor Spheroids Using a Microfluidics Droplet Platform.Small science · 2025Article
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- Mimicking the Complexity of Solid Tumors: How Spheroids Could Advance Cancer Preclinical Transformative Approaches.Cancers · 2025Article
- A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids.Biology methods & protocols · 2025Article
- Bridging the gap: the role of 3D cell cultures in mimicking tumor microenvironment for enhanced drug testing accuracy.Frontiers in bioengineering and biotechnology · 2025Review
- Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes.Cancer heterogeneity and plasticity · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed
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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.