Wang Tianheng, Yang Yi, Alqasemi Umar, Kumavor Patrick D, Wang Xiaohong, Sanders Melinda, Brewer Molly, Zhu Quing
University of Connecticut, Department of Electrical and Computer Engineering, Storrs, CT 06269, USA.
University of Connecticut Health Center, Division of Pathology, Farmington, CT 06030, USA.
Biomed Opt Express. 2013 Nov 6;4(12):2763-8. doi: 10.1364/BOE.4.002763. eCollection 2013.
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.
在本文中，利用光学分辨率光声显微镜（OR-PAM）系统对具有恶性和良性特征的人卵巢组织进行了离体成像。通过形态学比较光声显微镜图像，探讨了光声显微镜区分恶性和正常卵巢组织的可行性。基于在正常和恶性卵巢组织的光声显微镜图像中观察到的微血管特征和分布差异，从光声显微镜图像中定量提取了七个特征，并使用逻辑模型将卵巢分类为正常或恶性。研究了来自18个卵巢的106张光声显微镜图像。57张图像用于训练七参数逻辑模型，特异性达到92.1%，灵敏度达到89.5%；然后对49张图像进行测试，特异性达到81.3%，灵敏度达到88.2%。这些初步结果证明了我们的光声显微镜系统在绘制微血管网络以及表征卵巢组织方面的可行性，并且在协助外科医生在微创手术期间对卵巢组织进行体内评估方面可能具有极高的价值。
