Evidence map›Paper›PMID 42525763›Full record

ArticleScience advances2026

Integrating structurally defined DNA-carbon nanotube sensors with machine learning for cancer detection.

Piaoyi Chen, Xin Zheng, Yinong Li, Jian Li, Xuan Zhou, Yawei Wen, Jialong Liu, Pengbo Wang, Xiaohui Li, Runzhe Chen and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Piaoyi ChenSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.ORCID 0009-0000-2181-0414
Xin ZhengDepartment of Clinical Laboratory Medicine, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.ORCID 0000-0003-1489-7886
Yinong LiSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.ORCID 0000-0001-7095-3083
Jian LiSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Xuan ZhouSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Yawei WenSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Jialong LiuSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Pengbo WangSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Xiaohui LiDepartment of Clinical Laboratory Medicine, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Runzhe ChenDepartment of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Zhiwei LinSouth China Advanced Institute for Soft Matter Science and Technology, State Key Laboratory of Luminescent Materials and Devices, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.ORCID 0000-0001-9194-1145

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liquid biopsy is a promising, noninvasive approach for cancer detection, but current methods often trade off accuracy, operability, and cost. To address these limitations, we introduce an artificial perception system (APS) for liquid biopsy that combines a structurally defined DNA-carbon nanotube sensor array with machine learning (ML) models. The array produces multichannel fluorescence fingerprints from serum, which are decoded by ML models to classify disease state. In a total of 253 serum samples spanning liver, lung, and ovarian cancers and noncancer controls, the APS achieved mean sensitivity of 89% and specificity of 96%. Notably, early-stage lung cancer was detected with 92% sensitivity and 95% specificity at an estimated cost of ∼$4 USD per test. Insights from SHAP analysis and Mantel test revealed the detection mechanisms of APS, supporting biological plausibility and clinical translation. These results highlight a path toward accurate, scalable, and affordable multicancer detection and early cancer screening.

Indexed as

Biosensing TechniquesDNAEarly Detection of CancerMachine LearningNanotubes, CarbonNeoplasmsFemaleHumansDNANanotubes, Carbon

Identifiers

PMID42525763
PMCPMC13418527

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.