HYPERSPECTRAL IMAGING FOR TISSUE CLASSIFICATION AFTER OVARIAN CANCER SURGERY

Introduction/Background*The most important prognostic factor for the survival of advanced-stage ovarian cancer is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumours would be of great value. The aim of this pilot study is to assess feasibility of near infrared (NIR) hyperspectral imaging (HSI) for the detection of malignant ovarian cancer, using ex vivo tissue samples collected during CRS. Methodology In this pilot-study, patients with proven or suspected ovarian cancer planned for CRS were enrolled in the study. Hyperspectral images with 25 spectral bands were acquired from the resected tissues in the wavelength range of 665-975 nm. All hyperspectral data were processed by image calibration and min-max normalisation, glare removal, feature selection and linear support vector machine (SVM) classifier training. The performance of the classification was evaluated by leave-one-out cross-validation. Result(s)*Ten patients who underwent cytoreductive surgery for advanced-stage epithelial ovarian cancer (EOC) were included in the study, from which 26 tissue samples were imaged, with a total of 26.446 data points that were matched to histopathology. Samples included tissue of the ovaries, fallopian tubes, uterus, omentum and/or part of the intestines. Overall, HSI combined with the SVM classifier was capable to discriminate tumour tissue from non-tumour tissue with a sensitivity of 0.81, specificity of 0.75, area under the curve of 0.83, and Matthew’s correlation coefficient of 0.41. Conclusion*This pilot study shows that hyperspectral imaging is a promising technique to discriminate ovarian carcinomas from the surrounding tissue. Hyperspectral imaging can scan a whole area, is fast, non-contact, non-invasive and can be used inside the operation room