Abstract
About 85-90% of all ovarian cancers are carcinomas; these manifest clinically as mass-forming epithelial proliferations involving the ovary. In this study, a visible light spatial frequency domain imaging (SFDI) system was used for multispectral ex vivo imaging and quantitative evaluation of freshly excised benign and malignant human ovarian tissues. A total of 14 ovaries from 11 patients undergoing oophorectomy were investigated. Using a logistic regression model with seven significant spectral and spatial features extracted from SFDI images, a sensitivity of 94.06% and specificity of 93.53% were achieved for prediction of histologically confirmed invasive carcinoma.
Highlights
Cancer of the ovary remains the deadliest of all the gynecological malignancies, accounting for over 14,000 deaths annually in the US
A visible light spatial frequency domain imaging (SFDI) system was used for multispectral ex vivo imaging and quantitative evaluation of freshly excised benign and malignant human ovarian tissues
Using a logistic regression model with seven significant spectral and spatial features extracted from SFDI images, a sensitivity of 94.06% and specificity of 93.53% were achieved for prediction of histologically confirmed invasive carcinoma
Summary
Cancer of the ovary remains the deadliest of all the gynecological malignancies, accounting for over 14,000 deaths annually in the US. Due to the lack of reliable early symptoms and efficacious screening techniques, most of ovarian cancers are diagnosed at late stages (III and IV) [1] Optical imaging modalities such as optical coherence tomography (OCT), photoacoustic imaging (PAI), have been used separately or in combination for high-resolution imaging of benign and malignant ovarian tissue microstructures [2,3,4]. These studies have established that absorption parameters are important biomarkers related to tumor angiogenesis and tumor metabolism, whereas, changes in the scattering properties have been found to be associated with neoplastic alterations in the ovarian collagen structure as well as tumor necrosis. Using a logistic regression classifier, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), as well as the area under the receiver operator characteristic (ROC) curve (AUC) were evaluated
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