Artificial intelligence-enhanced Early Ovarian Cancer Diagnosis Biosensor

In early cancer diagnosis, extracellular vesicles (EVs) are more advantageous than circulating tumor cells due to their smaller size, greater stability, and enhanced tissue penetration. These qualities lead to higher EV concentrations in body fluids, facilitating early detection. This study leverages surface-enhanced Raman scattering (SERS) for EVs detection, employing a novel biosensor made with a molybdenum disulfide (MoS2) composite film on silicon, noted for its high sensitivity and easy labeling. This biosensor efficiently measures EVs concentrations and precisely detects three specific proteins on ovarian cancer EVs simultaneously (CD63, CD24, CA125). Using the ovarian cancer cell line HO8910, the sensor demonstrated a detection limit of 1.4×104 particles/mL and a wide linear range of 3.4×104 particles/mL to 3.4×108 particles/mL . It also effectively discriminated between serum samples from healthy individuals and ovarian cancer patients at different stages. Additionally, machine learning was applied to analyze detection data, resulting in a diagnostic model with 97.78% prediction accuracy. This highlights the sensor's potential in revolutionizing early cancer detection and establishing new diagnostic models.