Classification of human ovarian cancer using functional, spectral, and imaging features obtained from in vivo photoacoustic imaging

We report in this pilot study the diagnostic results of in vivo imaging of patients with ovarian lesions, using a co-registered photoacoustic and ultrasound (PAT/US) system. A total of 39 ovaries from 24 patients were imaged in vivo. PAT functional features, i.e., blood oxygen saturation (sO2) and relative total hemoglobin (rHbT), PAT image features, and PAT spectral features within a region of interest (ROI) in each ovarian tissue were extracted. To select the significant features, a t-test on each feature was performed, and the independent predictors were determined by evaluating correlation between each pair of predictors. To classify the ovarian lesions, we employed a generalized linear model (GLM) and a support vector machine (SVM). We used these classifiers first to distinguish benign/normal lesions from ovaries with invasive epithelial tumors and then to separate normal/benign lesions from all types of ovarian tumors. We developed classifiers once by inclusion of PAT functional features to assess the best diagnostic performance of the classifiers when multiple wavelengths data are available. Second time, we excluded the PAT functional features from the features set to evaluate the best diagnostic performance if only a single wavelength is available. Our results show that using functional features improves the classification performance, especially for distinguishing normal/benign ovarian lesions from all types of tumors. In this case, an area under ROC curve (AUC) of 0.92, 0.93 of testing data was achieved using a GLM and SVM classifier when functional features were included in the feature set while excluding these features resulted in an AUC of 0.89, 0.92, respectively.