Ovarian cancer patient prognosis and quality of life are significantly affected by the failure to accurately diagnose disease at early stage. Currently, less than 15% of ovarian cancer patients are diagnosed at stage 1, when 5-year survival rates are near 93%. Early detection may save more than 80% of women diagnosed with ovarian cancer. Our goal is to develop clinical long-term implantable carbon nanotube-based sensors for ovarian cancer biomarkers to transiently detect and monitor their levels in patients. The proposed sensors will harness the unique optical properties and sensitivity of single-walled carbon nanotubes. To improve sensitivity and specificity of such sensors, previously developed to detect ovarian cancer biomarkers in vivo, we used molecular perceptron—data analytics-based methods to detect specific spectroscopic fingerprints from the binding of protein biomarkers to the nanotube surface.
Citation Format: Zvi Yaari, Yoona Yang, Anand Jagota, Ming Zheng, Daniel A. Heller. Developing ovarian cancer sensors using molecular perceptron [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-068.