Abstract
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.
Highlights
Ovarian cancer has the highest mortality rate among all the gynecologic cancers because it is predominantly diagnosed at late stages due to the unreliable early symptoms and the poor screening techniques
In our initial study [9], optical scattering coefficient and phase retardation of 33 ex vivo ovaries obtained from 18 patients were extracted from time domain (TD) Polarization-sensitive Optical coherence tomography (OCT) (PS-OCT) intensity and phase images, respectively
A total of 43 ovaries were extracted from 23 patients undergoing Prophylactic oophorectomy (PO) at the University of Connecticut Health Center (UCHC). 33 ovaries from 18 patients were imaged using TD-PSOCT while 10 ovaries from 5 patients were imaged using Fourier domain (FD)-PS-OCT
Summary
Ovarian cancer has the highest mortality rate among all the gynecologic cancers because it is predominantly diagnosed at late stages due to the unreliable early symptoms and the poor screening techniques. There is an urgent need to develop effective tools to inspect ovaries, so that the mortality rate of ovarian cancer can be reduced and the quality of patients’ life can be improved. In our initial study [9], optical scattering coefficient and phase retardation of 33 ex vivo ovaries obtained from 18 patients were extracted from time domain (TD) PS-OCT intensity and phase images, respectively. A more sensitive parameter, the phase retardation rate, was extracted from PS-OCT phase images and used together with the scattering coefficient and phase retardation to characterize ovarian tissue. Pierce et al to quantify collagen denaturation in burned human skin [10] In our study, these three parameters extracted from 33 ovaries were used as inputs to a logistic model to predict or classify the malignant and benign ovaries. To the best of our knowledge, this is the first study of using multiple parameters extracted from PS-OCT images as predictors for ovarian tissue characterization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
