Background: The aim of surgery for advanced-stage ovarian cancer is a complete cytoreduction, because this is the most important independent prognostic factor for prolonged survival. Yet this can be difficult to achieve when there are micrometastases on the intestinal mesentery or intestines. The PlasmaJet device is an instrument to remove these micrometastases, but little is known about the depth of damage in human tissue compared to electrocoagulation devices. Methods: A prospective study was performed for the ex-vivo comparison of the histological depth of thermal damage of neutral argon plasma (PlasmaJet®) and electrocoagulation devices, in a series of 106 histological slides of 17 advanced-stage ovarian cancer patients. Depending on the tissue types resected during complete cytoreductive surgery, samples were collected from reproductive organs (uterus, ovaries), intestines (ileum, colon, rectum) and omentum, intestinal mesentery and peritoneum. Results: Average thermal damage depth was 0.15 mm (range 0.03–0.60 mm) after use of neutral argon plasma and 0.33 mm (range 0.08–1.80 mm) after use of electrocoagulation (p < 0.001). Greater disruption of the tissue surface was often observed after electrocoagulation. Conclusion: Our case series suggests that the use of neutral argon plasma during cytoreductive surgery produces significantly less thermal damage than electrocoagulation treatment. It is therefore considered a thermally safe alternative, aiding in the achievement of cytoreductive surgery.

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.