Annually, ovarian cancer is responsible for 184,000 deaths worldwide. Survival rates are strongly linked to the stage at which the disease is detected, with just a 29% five-year survival rate if the cancer has metastasized—the stage at which most cases are diagnosed. But the survival rate rises to more than 90% if the disease is caught before the cancer has spread outside the ovaries. However, few serum biomarker tests—blood tests that reveal tumor development—are highly specific yet sufficiently sensitive enough to detect early-stage disease.
Researchers YuHuang Wang, a professor of chemistry and biochemistry, Daniel Heller and Mijin Kim of Memorial Sloan-Kettering Cancer Center, Anand Jagota of Lehigh University, and Ming Zheng of the NIST Material Measurement Laboratory (pictured above) have addressed this problem by using a new type of sensor technology. Based on quantum defect-tailored carbon nanotubes augmented by machine learning algorithms, this new sensor technology can detect the "disease fingerprint" of high-grade serous ovarian carcinoma in serum samples, providing a much more accurate and reliable diagnosis. This breakthrough has the potential to save lives by allowing doctors to catch ovarian cancer early, when it is most treatable.
To See Life in a Drop of Blood
Blog post by Ming Zheng