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

作者：Piaoyi Chen, Xin Zheng, Yinong Li, Jian Li, Xuan Zhou, Yawei Wen, Jialong Liu, Pengbo Wang, Xiaohui Li, Runzhe Chen, Zhiwei Lin · 发表于：Science Advances · 年份：2026 · DOI：10.1126/sciadv.aef9530 · 研究领域：Advanced biosensing and bioanalysis techniques、Nanopore and Nanochannel Transport Studies、Biosensors and Analytical Detection

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.