During post chemotherapy surgery for ovarian cancer, it is important to ensure that any residual disease is carefully assessed and removed. The assessment remains subjective, despite clear evidence of the benefits of complete macroscopic resection. In this work, we have considered Raman spectroscopy as a possible tool for residual ...
During post chemotherapy surgery for ovarian cancer, it is important to ensure that any residual disease is carefully assessed and removed. The assessment remains subjective, despite clear evidence of the benefits of complete macroscopic resection. In this work, we have considered Raman spectroscopy as a possible tool for residual disease assessment by exploring its ability to correctly classify ovarian cancer from benign and borderline tissues. Samples from seventy-three participants were analysed (n = 20 benign, n = 11 borderline and n = 42 cancer) using a multivariate analysis model. All models shown utilised validation with leave one participant out cross-validation. In ovarian tissue this model achieved 94% sensitivity and 98% specificity for prediction of cancer from benign and 98% sensitivity and 89% specificity for prediction of cancer from borderline. Thorough assessment of the surrounding peritoneal tissues is extremely important. For these peritoneal tissues taken from participants with advanced ovarian cancer, the model achieved 78% sensitivity and 84% specificity for prediction of cancerous peritoneum from benign peritoneum in participants who had primary surgery and 68% sensitivity and 81% specificity in participants who had post chemotherapy surgery. This demonstrates viability of Raman spectroscopy for assessment of ovarian cancer.
