ArticleAnalytical chemistry2019
Label-Free Estimation of Therapeutic Efficacy on 3D Cancer Spheres Using Convolutional Neural Network Image Analysis.
Article in Analytical chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Review
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Three-tier framework for high-throughput biofabrication: Integrating 3D bioprinting, assistive platforms, and translational opportunities.Bioactive materials · 2026Review
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects.Current issues in molecular biology · 2024Review
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.Biomicrofluidics · 2024Article
- High-throughput platform for label-free sorting of 3D spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes.Cancer heterogeneity and plasticity · 2024Article
- Evaluating the utility of brightfield image data for mechanism of action prediction.PLoS computational biology · 2023Article
- Microfluidics and Organoids, the Power Couple of Developmental Biology and Oncology Studies.International journal of molecular sciences · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Spheroid Engineering in Microfluidic Devices.ACS omega · 2023Review
- Organ-on-a-chip meets artificial intelligence in drug evaluation.Theranostics · 2023Review
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.Analytical chemistry · 2022Article
- Hydrogel-based microfluidic device with multiplexed 3D in vitro cell culture.Scientific reports · 2022Article
- Design and Modeling of a Microfluidic Coral Polyps Culture Chip with Concentration and Temperature Gradients.Micromachines · 2022Article
- Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Despite recent advances in cancer treatment, developing better therapeutic reagents remains an essential task for oncologists. To accurately characterize drug efficacy, 3D cell culture holds great promise as opposed to conventional 2D monolayer culture. Due to the advantages of cell manipulation in high-throughput, various microfluidic platforms have been developed for drug screening with 3D models. However, the dissemination of microfluidic technology is overall slow, and one missing part is fast and low-cost assay readout. In this work, we developed a microfluidic chip forming 1920 tumor spheres for drug testing, and the platform is supported by automatic image collection and cropping for analysis. Using conventional LIVE/DEAD staining as the ground truth of sphere viability, we trained a convolutional neural network to estimate sphere viability based on its bright-field image. The estimated sphere viability was highly correlated with the ground truth (
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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.