A three-parameter logistic model to characterize ovarian tissue using polarization-sensitive optical coherence tomography.

In this paper, a logistic prediction model is introduced to characterize the ovarian tissue. A new parameter, the phase retardation rate, was extracted from phase images of polarization-sensitive optical coherence tomography (PS-OCT). Statistical significance of this parameter between normal and malignant ovarian tissues was demonstrated (p<0.0001). Linear regression analysis showed that this parameter was positively correlated (R = 0.74) with collagen content, which was associated with the development of ovarian tissue malignancy. When this parameter and the optical scattering coefficient and the phase retardation estimated from the 33 ovaries were used as input predictors to the logistic model, 100% sensitivity and specificity in classifying malignant and normal ovaries were achieved. Ten additional ovaries were imaged and used to validate the prediction model and 100% sensitivity and 83.3% specificity were achieved. These results showed that the three-parameter prediction model based on quantitative parameters estimated from PS-OCT images could be a powerful tool to detect and diagnose ovarian cancer.