Abstract
In this paper, human ovarian tissue with malignant and benign features was imaged ex vivo using an optical-resolution photoacoustic microscopy (OR-PAM) system. The feasibility of PAM to differentiate malignant from normal ovarian tissues was explored by comparing the PAM images morphologically. Based on the observed differences between PAM images of normal and malignant ovarian tissues in microvasculature features and distributions, seven features were quantitatively extracted from the PAM images, and a logistic model was used to classify ovaries as normal or malignant. 106 PAM images from 18 ovaries were studied. 57 images were used to train the seven-parameter logistic model, and a specificity of 92.1% and a sensitivity of 89.5% were achieved; 49 images were then tested, and a specificity of 81.3% and a sensitivity of 88.2% were achieved. These preliminary results demonstrate the feasibility of our PAM system in mapping microvasculature networks as well as characterizing the ovarian tissue, and could be extremely valuable in assisting surgeons for in vivo evaluation of ovarian tissue during minimally invasive surgery.
Highlights
Ovarian cancer is the fifth most common cancer among women, and it has the lowest survival rate among all of the gynecologic cancers because it is predominantly diagnosed in late stages due to the lack of early symptoms as well as the lack of effective screening techniques [1]
106 PAM images from 18 ovaries were studied. 57 images were used to train the seven-parameter logistic model, and a specificity of 92.1% and a sensitivity of 89.5% were achieved; 49 images were tested, and a specificity of 81.3% and a sensitivity of 88.2% were achieved. These preliminary results demonstrate the feasibility of our PAM system in mapping microvasculature networks as well as characterizing the ovarian tissue, and could be extremely valuable in assisting surgeons for in vivo evaluation of ovarian tissue during minimally invasive surgery
Ex vivo ovarian tissue was imaged by using an optical-resolution photoacoustic microscopy (OR-PAM) system, and quantitative analysis was performed by extracting features from PAM images
Summary
Ovarian cancer is the fifth most common cancer among women, and it has the lowest survival rate among all of the gynecologic cancers because it is predominantly diagnosed in late stages due to the lack of early symptoms as well as the lack of effective screening techniques [1]. PAM in particular, is capable of mapping microvasculature networks in biological tissue and resolving blood vessels with much higher resolution than conventional photoacoustic images obtained with ultrasound array transducers [8,9,10,11,12,13,14,15,16,17,18,19,20]. Photoacoustic images obtained with conventional ultrasound array transducers in the central frequency range of 3-7 MHz have lower resolution in resolving microvasculature networks and distributions in ovarian tissue than that of PAM. To the best of our knowledge, this study is the first one reporting quantitative analysis and feature extraction of PAM images for classifying normal and malignant ovarian tissues. Quantitative analysis of PAM images is extremely valuable in assisting physicians to characterize and diagnose normal and malignant processes
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