ArticleNature biomedical engineering2018
Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.
Article in Nature biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 78 citations in OpenAlex.
- Translational Barriers and AI-Driven Challenges of Microfluidics-Enabled Wearables and Implantable Systems in Personalized Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- The Latest Advances in Microfluidic DLD Cell Sorting Technology: The Optimization of Channel Design.Biosensors · 2025Review
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Impact of Nanomedicine in Women's Metastatic Breast Cancer.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Effectiveness of Artificial Intelligence Technologies in Cancer Treatment for Older Adults: A Systematic Review.Journal of clinical medicine · 2024Review
- Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.Eye (London, England) · 2024Review
- Enhancing single-cell biology through advanced AI-powered microfluidics.Biomicrofluidics · 2023Article
- Multifunctional nanostructures: Intelligent design to overcome biological barriers.Materials today. Bio · 2023Review
- Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.Micromachines · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Review
- Superhuman cell death detection with biomarker-optimized neural networks.Science advances · 2021Article
- A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.Cell reports methods · 2021Article
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis.Frontiers in bioengineering and biotechnology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 3 institutions in 1 country.
Funding
Abstract
The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.
Identifiers
What OpenQuestion holds
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.