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Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
Published on: March 25, 2019
Emerging nanotechnologies and their role in early ovarian cancer detection, diagnosis and interventions
Mega Obukohwo Oyowvi1,2, Kehinde Henrietta Babawale3, Adedeji David Atere4,5
1Department of Physiology, Adeleke University, Ede, Osun State, Nigeria. megalect@gmail.com.
Abstract:
Ovarian cancer presents a significant public health challenge, often being diagnosed at advanced stages due to the limitations of current detection methods. This systematic review addresses the urgent need for innovative approaches to enhance early detection and diagnosis of ovarian cancer. We systematically evaluate recent advancements in nanotechnology, focusing specifically on their novel applications and potential in comparison to traditional diagnostic modalities. Our analysis encompasses a wide range of studies investigating nanoparticles, biosensors, advanced imaging techniques, and biomarker detection platforms, with an emphasis on evaluating key performance indicators such as detection rates, turnaround times, and the accuracy of distinguishing cancerous from non-cancerous tissues. Our findings indicate that nanotechnology-based approaches have the potential to significantly improve early detection capabilities for ovarian cancer. Notably, studies on nanoparticle-based imaging techniques and biosensors consistently demonstrate high sensitivity and specificity for identifying ovarian cancer biomarkers, with detection rates exceeding 90% reported for early-stage cancers in several instances. This review underscores the promise of emerging nanotechnologies to transform the landscape of early detection and diagnosis, offering a pathway toward earlier diagnoses, enhanced therapeutic interventions, and improved patient outcomes. We advocate for future research dedicated to the translational efforts required to move these technologies from bench to bedside, ensuring their effectiveness is validated across diverse clinical populations.
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