Abstract A010: Biofluid-Ensemble Analysis through Multi-modal Spectroscopy (BEAMS): A deep learning architecture for rapid early-stage liquid biopsy cancer diagnostics

1. Chiu, Kwan Lun ²
2. Guillen-Perez, Antonio ⁴
3. Mayer, Rebecca ²
4. Viner, Wesley ²
5. Koster, Hanna ²
6. Benson, Matthew ²
7. Rowe, Jeremy ¹
8. Navas-Moreno, Maria ³
9. Birkeland, Andrew ²
10. Cano, Maria-Dolores ⁴
11. Gomez-Diaz, J. Sebastián ²
12. Carney, Randy ²

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DOI: 10.1158/1557-3265.LIQBIOP24-A010 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Clinical Cancer Research

Abstract

Head and neck cancer (HNC) is one of the most common cancer types globally without an accessible rapid screening method. Conventional screening occurs at the clinician’s office, where grading and staging of the tumors are physically performed, and the clinician’s preliminary diagnosis is usually confirmed through laborious procedures of histology and CT/PET/MR scans. Patient’s quality of life and expected outcomes can be improved through early-stage screening, yet there is no existing technology on the market that tackles this dire need. As such, we propose a rapid liquid biopsy diagnostic platform empowered by deep learning algorithms that makes early-stage non-invasive screening possible. The platform utilizes the complementary vibrational spectral biomarkers information from Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy. We coupled the techniques with two biofluids: saliva and plasma, which offered information on circulating and localized metabolites.