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Updated: Dec 5, 2025

Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
Published on: March 25, 2019
Surgery-Guided Removal of Ovarian Cancer Using Up-Converting Nanoparticles
Christopher B Marotta1, Tom Haber2, Jacob M Berlin2
1Department of Chemistry and Chemical Engineering, California Institute of Technology, 1200 E. California Blvd, Pasadena, California 91125, United States.
Abstract:
Ovarian cancer survival and the recurrence rate are drastically affected by the amount of tumor that can be surgically removed prior to chemotherapy. Surgeons are currently limited to visual inspection, making smaller tumors difficult to be removed surgically. Enhancing the surgeon's ability to selectively remove cancerous tissue would have a positive effect on a patient's prognosis. One approach to aid in surgical tumor removal involves using targeted fluorescent probes to selectively label cancerous tissue. To date, there has been a trade-off in balancing two requirements for the surgeon: the ability to see maximal tumors and the ability to identify these tumors by eye while performing the surgery. The ability to see maximal tumors has been prioritized and this has led to the use of fluorophores activated by near-infrared (NIR) light as NIR penetrates most deeply in this surgical setting, but the light emitted by traditional NIR fluorophores is invisible to the naked eye. This has necessitated the use of specialty detectors and monitors that the surgeon must consult while performing the surgery. In this study, we develop nanoparticles that selectively label ovarian tumors and are activated by NIR light but emit visible light. This potentially allows for maximal tumor observation and real-time detection by eye during surgery. We designed two generations of up-converting nanoparticles that emit green light when illuminated with NIR light. These particles specifically label ovarian tumors most likely via tumor-associated macrophages, which are prominent in the tumor microenvironment. Our results demonstrate that this approach is a viable means of visualizing tumors during surgery without the need for complicated, expensive, and bulky detection equipment. Continued improvement and experimentation could expand our approach into a much needed surgical technique to aid ovarian tumor removal.

