(Invited) Developing Optical Nanosensors for the Early Detection of Gynecologic Cancers

Uterine and ovarian cancer patients' prognosis and quality of life are significantly affected by the failure to diagnose the disease accurately. Early detection may save more than 80% of women diagnosed with ovarian cancer. Therefore, developing new platforms for better detecting potential biomarkers in both biofluid samples and in-situ may significantly improve early detection and better survival. Here we propose two platforms to detect multiple cancer biomarkers in cancer patients' specimens and detect specific biomarkers in-situ using implantable sensors. These technologies harness the unique optical properties of single-walled carbon nanotubes (SWCNTs)-based sensors and artificial intelligent algorithms. We developed an array of DNA-SWCNT sensors that composed a library of ssDNA sequences and multiple chiralities of SWCNTs to detect various biomarkers within patient clinical samples. Harnessing machine learning algorithms to process the optical response of the sensors enabled the precise detection of the unique spectroscopic fingerprints of each of the biomarkers in uterine washing clinical samples. For detecting biomarkers within the uteri of the patients, we developed an implantable sensor that serves as an implantable intrauterine device. The sensor can detect cancer biomarkers and differentiate between benign and malignant cases.