Ovarian Cancer Coverage from Every Angle

New Imaging Method May Improve Accuracy of Classifying Peritoneal Metastases

By: Kayci Reyer
Posted: Thursday, November 7, 2019

According to research published by Thomas Schnelldorfer, MD, PhD, of Lahey Hospital in Massachusetts, and colleagues in Biomedical Optics Express, a novel multiphoton laser scanning technique can identify small metastatic peritoneal lesions better than conventional white-light laparoscopy alone. The new approach combines microscopy and algorithmic computations to detect metastatic areas without chemical tissue processing, making it potentially feasible for use during surgery.

“Our results indicate that two-photon second harmonic generation and fluorescence imaging in combination with automated analytic approaches enables in near real-time noninvasive, user-unbiased tissue classification,” concluded the investigators. “As these analytic techniques can be coupled with rapidly advancing miniaturization of two-photon imaging systems to afford their use in clinical situations, they hold great potential in assisting surgical assessment of peritoneal tissues at the bedside.”

The study included healthy and cancerous biopsy samples from eight patients with suspected or confirmed ovarian malignancy, each of whom was treated with laparotomy. All eight participants had samples of healthy parietal peritoneum collected, whereas four patients also provided samples of peritoneal metastasis. Using second-harmonic generation at 900 nm excitation and fluorescent imaging at 755 nm excitation, 30 images from healthy samples and 11 from diseased samples were captured and analyzed.

The imaging method relied on short bursts of laser light, which reflected off the unique type of each tissue sample. Due to these textural differences, a variety of signals were elicited during the imaging. The microscope received these signals and analyzed them using programmed algorithms, a system that led to remarkable accuracy in disease identification. Overall, 40 of the 41 healthy images and 11 of the 11 diseased images were correctly classified using this technique.

Disclosure: The study authors reported no conflicts of interest.

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