ArticleNature biomedical engineering2022
Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.
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Who cites it
77 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Advances in Nanotechnology in the Diagnosis and Treatment of Gynecological Cancers.International journal of nanomedicine · 2025Pooled it
- High-Throughput SWCNT NIR-II Spectroscopy Enables Cell and In Vivo Applications.ACS nanoscience Au · 2026Article
- Integrating structurally defined DNA-carbon nanotube sensors with machine learning for cancer detection.Science advances · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Optical Spectral Fingerprinting Enables Sensitive Detection of Anthracycline Chemotherapeutics in Synthetic Clinical Biofluids.Nano letters · 2026Article
- Inverse design of guanine-defects in carbon nanotubes for high-resolution emission tuning.Science advances · 2026Article
- A bibliometric analysis of artificial intelligence in ovarian cancer research from 2006 to 2025.Discover oncology · 2026Article
- Chemical efflux imaging using an annular nanosensor array for in situ bladder cancer detection.Nature nanotechnology · 2026Article
- Large-Scale Quantitative Morphometry of Platelet α-Granules via SIM Super-Resolution Microscopy for Cancer Liquid Biopsy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.Cancers · 2026Review
- Biomaterials for biomarker imaging and detection.Journal of advanced research · 2026Review
- Optical Spectral Fingerprinting Enables Sensitive Detection of Anthracycline Chemotherapeutics in Synthetic Clinical Biofluids.bioRxiv : the preprint server for biology · 2026Article
- Industrial Adoption of Carbon Nanotubes.Nano letters · 2026Review
- Urine-Based Machine Learning Assay Detects Prostate Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Writing DNA Bases into spResearch square · 2026Article
- Rational Design of Optical Single-Walled Carbon Nanotube-Based Nanosensors with Biological Recognition Elements.Advanced sensor research · 2026Article
- Review
- High-throughput SWCNT NIR-II screening enables cell andbioRxiv : the preprint server for biology · 2026Article
- Machine perception liquid biopsy identifies brain tumours via systemic immune and tumour microenvironment signature.Nature nanotechnology · 2026Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In ovarian cancer, the few established serum biomarkers are highly specific, yet insufficiently sensitive to detect early-stage disease and to impact the mortality rates of patients with this cancer. Here we show that a 'disease fingerprint' acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best clinical screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography). We used 269 serum samples to train and validate several machine-learning classifiers for the discrimination of patients with ovarian cancer from those with other diseases and from healthy individuals. The predictive values of the best classifier could not be attained via known protein biomarkers, suggesting that the array of nanotube sensors responds to unidentified serum biomarkers.
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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.