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ePoster Viewing
AS11. Ovarian cancer
EP315/#771  Hyperspectral imaging for the in vivo detection of ovarian cancer
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  1. Laurie Van De Weerd1,
  2. Ralf Van De Laar1,
  3. Eva Maria Roes1,
  4. Helena C Doorn1,
  5. Jenny Dankelman2,
  6. Lucia Rijstenberg3,
  7. Nick Van De Berg1,
  8. Gatske Nieuwenhuyzen-De Boer1,4 and
  9. Heleen Van Beekhuizen1
  1. 1Erasmus MC Cancer Institute, University Medical Center Rotterdam, Gynecologic Oncology, Rotterdam, Netherlands
  2. 2Delft University of Technology, Biomedical Engineering, Delft, Netherlands
  3. 3Erasmus University Medical Centre, Pathology, Rotterdam, Netherlands
  4. 4Albert Schweitzer Hospital, Department of Obstetrics and Gynecology, Dordrecht, Netherlands

Abstract

Introduction For women diagnosed with advanced-stage epithelial ovarian cancer, complete cytoreductive surgery (CRS) is the most powerful independent parameter for prolonged survival. An intraoperative imaging technique to detect tumor deposits could help achieve complete CRS. Hyperspectral imaging (HSI) provides information on tissue composition, including tissue oxygenation, hemoglobin-, and tissue water and fat indices. In an earlier ex-vivo study it was shown that HSI can be used for ex-vivo tumor detection. Currently, we are evaluating whether HSI can be used in-vivo to distinguish tumor from healthy tissue.

Methods HSI data of healthy and tumorous peritoneum, omentum, ovary, and mesentery were obtained in-vivo and preprocessed by image calibration and glare removal. The data were correlated to histopathology and used to train classifiers. The ability to delineate tumorous from healthy tissues was determined using leave-one-out cross-validation.

Results A total of 18 images from 12 patients were included. In total 302.258 data points were extracted based on the knowledge of the surgeons and histopathological information. Our data shows that different organs that are affected by ovarian cancer yield different spectra. Additionally, we observe a difference in the spetra of tumor and non-tumor tissue.

Conclusion/Implications HSI enables the classification of various tissue types, including tumor and non-tumor tissue. To improve classification outcomes, it is crucial to obtain more data and to make separate groups of healthy and tumorous tissues for each of these tissue types. HSI is a promising technique to differentiate between healthy tissue and ovarian cancer lesions and eventually help surgeons to achieve complete CRS.

Abstract EP315/#771 Figure 1
Abstract EP315/#771 Figure 1

Mean spectra of tumorous and non-tumorous samples of the peritoneum and ovaries with the shaded areas representing the interquartile range, and n the number of datapoints

Abstract EP315/#771 Figure 2
Abstract EP315/#771 Figure 2

Mean spectra of tumorous and non-tumorous samples of the omentum and the mesentery with the shaded areas representing the interquartile range, and n the number of datapoints

https://doi.org/10.1136/ijgc-2023-IGCS.378

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