3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.

Automated summary

This study developed a flexible and highly absorptive three-dimensional plasmonic nanostructure sensor for rapid lung cancer screening and diagnosis. The sensor successfully detected and classified lung cancer in patient saliva samples. Additionally, the study investigated important Raman peak positions related to different stages of lung cancer, providing insights for early-stage cancer diagnosis.