ArticleACS nano2018
Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings.
Article in ACS nano, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 38 citations in OpenAlex.
- A lateral flow assay strip based on AND logic gate and DNA self-assembly for dual miRNA detection in breast cancer diagnosis.Mikrochimica acta · 2026Article
- Integrated Multi-Channel Automatic Cellular Profiling (iMAP) System for Cholangiocarcinoma Diagnosis.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- MIDAS: rapid, multiplexed molecular profiling for integrated host-pathogen analysis.Nature communications · 2025Article
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.Journal of nanobiotechnology · 2025Article
- Breast cancer: pathogenesis and treatments.Signal transduction and targeted therapy · 2025Review
- Deep learning model for automatic image quality assessment in PET.BMC medical imaging · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Article
- Integrated Analytical System for Clinical Single-Cell Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Article
- Multiplexed imaging in oncology.Nature biomedical engineering · 2022Review
- Computational Optics for Point-of-Care Breast Cancer Profiling.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Deep Learning Perspective on Dropwise Condensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
- New technology on the horizon: Fast analytical screening technique FNA (FAST-FNA) enables rapid, multiplex biomarker analysis in head and neck cancers.Cancer cytopathology · 2020Review
- Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning.Annals of biomedical engineering · 2020Review
- CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis.Science translational medicine · 2020Article
- Glass Chemistry to Analyze Human Cells under Adverse Conditions.ACS omega · 2019Article
- Point-of-care cervical cancer screening using deep learning-based microholography.Theranostics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid, and treatment-informative diagnostics. On the basis of advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning-based analysis of sample aspirates. The image algorithm is fast, enabling cellular analyses at high throughput (∼3 s per 1000 cells), and the unsupervised processing allows use by lower skill health care workers. AIDA can perform quantitative molecular profiling on individual cells, revealing intratumor molecular heterogeneity, and has the potential to improve cancer diagnosis and treatment. The system could be further developed for other cancers and thus find widespread use in global health.
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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.