Abstract A010: Biofluid-Ensemble Analysis through Multi-modal Spectroscopy (BEAMS): A deep learning architecture for rapid early-stage liquid biopsy cancer diagnostics
Authors: Kwan Lun Chiu, Antonio Guillen-Perez, Rebecca Mayer, Wesley Viner, Hanna Koster, Matthew Benson, Jeremy Rowe, Maria Navas-Moreno, Andrew Birkeland, Maria-Dolores Cano, J. Sebastián Gomez-Diaz, Randy Carney
Journal: Clinical Cancer Research (2024)

Abstract Introduction: 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. Materials and Methods: The patient’s biofluid is first drop-casted on top of a substrate and air-dried at room temperature. Using an FTIR microscope or a Raman microscope, 25 spot measurements are made in a 5 by 5 grid pattern to produce a comprehensive scan of the sample. The measured spectra are subsequently fed into the deep learning framework to perform classification. The deep machine learning framework is composed of spectrum and subject-level models. Specifically, the spectrum-level model employs cascading convolutional layers to identify the prominent features within the spectrum. This is followed by a sequence of linear layers that further extract features from the convolutional layers and carry out binary classification. For each patient, preliminary classification results of the spectral level model are then evaluated on a per- modality basis across our four modalities (FTIR/Raman measurements made on saliva/plasma) where the patient is classified as positive if any of the modality models deem it so. This framework is applied to a cohort of 96 patients, with 36 non-cancerous patients, 28 early-stage patients, and 32 late-stage patients. Results and Discussion: Our model optimized for general cancer detection has a sensitivity of 91.7%, specificity of 77.8%, and accuracy of 86.5%. Late- stage cancer has proven to be less challenging to detect since our model optimized for late-stage cancer diagnosis has a sensitivity of 93.8%, a specificity of 83.3%, and an accuracy of 88.2%. Our best early-stage model has a sensitivity of 89.3%, a specificity of 69.4%, and an accuracy of 78.1%. Conclusions: Our proposed framework has demonstrated promising capability as the preliminary screening method for high-risk patients (e.g. tobacco users) while providing further interpretability of the deep learning model that is often characterized as a “black box”. The modular nature of our model structure offers substantial flexibility, enabling effective performance even when specific biofluids or spectroscopy modalities are unavailable. Citation Format: Kwan Lun Chiu, Antonio Guillen-Perez, Rebecca Mayer, Wesley Viner, Hanna Koster, Matthew Benson, Jeremy Rowe, Maria Navas-Moreno, Andrew Birkeland, Maria-Dolores Cano, J. Sebastián Gomez-Diaz, Randy Carney. Biofluid-Ensemble Analysis through Multi-modal Spectroscopy (BEAMS): A deep learning architecture for rapid early-stage liquid biopsy cancer diagnostics [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A010.