An artificial intelligence-enhanced early ovarian cancer diagnosis biosensor

Abstract

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 EV detection, employing a novel biosensor made with a molybdenum disulfide (MoS₂) composite film on silicon and demonstrating a lower limit of detection (LOD) and multi-marker synchronous quantitative testing performance compared to existing methodologies. This biosensor efficiently measures EV concentrations and precisely detects three specific proteins on ovarian cancer EVs simultaneously (CD63, CD24, and CA125). Using the ovarian cancer cell line HO8910, the sensor demonstrated a detection limit of 1.4 × 10⁴ particles per mL and a wide linear range of 3.4 × 10⁴ particles per mL to 3.4 × 10⁸ particles per 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 a 97.78% prediction accuracy. This highlights the sensor's potential in revolutionizing early cancer detection and establishing new diagnostic models.