Medical use of (non-)thermal plasmas is an emerging field in gynaecology. However, data on plasma energy dispersion remain limited. This systematic review presents an overview of plasma devices, fields of effective application, and impact of use factors and device settings on tissues in the female pelvis, including the uterus, ovaries, cervix, vagina, vulva, colon, omentum, mesenterium, and peritoneum. A search of the literature was performed on 4 January 2023 in the Medline Ovid, Embase, Cochrane, Web of Science, and Google Scholar databases. Devices were classified as plasma-assisted electrosurgery (ES) using electrothermal energy, neutral argon plasma (NAP) using kinetic particle energy, or cold atmospheric plasma (CAP) using non-thermal biochemical reactions. In total, 8958 articles were identified, of which 310 were scanned, and 14 were included due to containing quantitative data on depths or volumes of tissues reached. Plasma-assisted ES devices produce a thermal effects depth of <2.4 mm. In turn, NAP effects remained superficial, <1.0 mm. So far, the depth and uniformity of CAP effects are insufficiently understood. These data are crucial to achieve complete treatment, reduce recurrence, and limit damage to healthy tissues (e.g., prevent perforations or preserve parenchyma). Upcoming and potentially high-gain applications are discussed, and deficits in current evidence are identified.@en

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. @en