Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

Abstract

Abstract. A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t -tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39 AE 3.35% and 97.82 AE 2.26%, and specificities of 98.92 AE 1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71 AE 3.55% and 92.64 AE 3.27%, and specificities of 87.52 AE 8.78% and 98.49 AE 2.05%, respectively.

Coregistered photoacoustic and ultrasound imaging and classification of ovarian cancer:ex vivoandin vivostudies

“…PAT and US images were constructed by a linear mapping of envelopes after scan conversion. 21 We paid special attention to selecting visually different images in the image sequence of each ovary to provide more independent data to classifiers. Similar to our earlier study, 17 features were initially extracted from the PAT data for classification of malignant and benign ovarian tissues; these features include two out of three PAT beam spectral features, five PAT beam envelope features, and ten PAT imaging features.…”

“…Briefly, spectral features (spectral slopes, mid-band fits, and 0 MHz intercepts) from PAT beams reveal the distribution of the frequency components within the frequency range of the transducer array, and are related to absorber dimensions and chromophore concentrations. 21,23,24 Features from the PAT beam envelopes, such as the summation of envelopes (PAT summation) and the maximum envelope, characterize the light fluence and absorption within the selected suspicious area or region of interest (ROI). The ROI was selected by first estimating the center (x 0 and y 0 ) using a Gaussian model fitted to 0 and 90 deg Radon transforms of a PAT image along the x-and y-axes, respectively.…”

“…The ROI was selected by first estimating the center (x 0 and y 0 ) using a Gaussian model fitted to 0 and 90 deg Radon transforms of a PAT image along the x-and y-axes, respectively. 20,21 The Gaussian model is given as E Q -T A R G E T ; t e m p : i n t r a l i n k -; s e c 2 . 3 ; 6 3 ; 6 2 0…”

“…20,21 To overcome the challenges of light delivery and photoacoustic/ultrasound data acquisition speed for patient studies, our coregistered PAT/US imaging system has made significant advances. This is from the initial free space light delivery and slow data acquisition, robustly characterize malignant from benign ovarian tissues, our feature extraction and classifiers have evolved from single threshold-based classifers 14 to image feature-based classifiers, 20 and to image and spectra features based classifiers.…”

“…This is from the initial free space light delivery and slow data acquisition, robustly characterize malignant from benign ovarian tissues, our feature extraction and classifiers have evolved from single threshold-based classifers 14 to image feature-based classifiers, 20 and to image and spectra features based classifiers. 21 In this study, we report results from a new set of 25 excised ovaries of 15 patients, and two patients studied both in vivo and ex vivo with malignant and benign pathology using our upgraded coregistered real-time PAT/US clinical prototype system 17 with optimized light delivery and improved signal-to-noise ratio. 18,19 In addition to features obtained from the PAT data, we extracted six new features with three of them from the US beam spectra, to improve the diagnostic performance of the logistic and support vector machine (SVM) classifiers.…”