Research Papers: Imaging

Coregistered photoacoustic and ultrasound imaging and classification of ovarian cancer: ex vivo and in vivo studies

[+] Author Affiliations
Hassan S. Salehi, Hai Li

University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States

Alex Merkulov

University of Connecticut Health Center, Division of Radiology, Farmington, Connecticut 06030, United States

Patrick D. Kumavor, Hamed Vavadi

University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States

Melinda Sanders

University of Connecticut Health Center, Department of Pathology, Farmington, Connecticut 06030, United States

Angela Kueck, Molly A. Brewer

University of Connecticut Health Center, Division of Gynecologic Oncology, Farmington, Connecticut 06030, United States

Quing Zhu

University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States

University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States

J. Biomed. Opt. 21(4), 046006 (Apr 18, 2016). doi:10.1117/1.JBO.21.4.046006
History: Received August 5, 2015; Accepted March 24, 2016
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Abstract.  Most ovarian cancers are diagnosed at advanced stages due to the lack of efficacious screening techniques. Photoacoustic tomography (PAT) has a potential to image tumor angiogenesis and detect early neovascular changes of the ovary. We have developed a coregistered PAT and ultrasound (US) prototype system for real-time assessment of ovarian masses. Features extracted from PAT and US angular beams, envelopes, and images were input to a logistic classifier and a support vector machine (SVM) classifier to diagnose ovaries as benign or malignant. A total of 25 excised ovaries of 15 patients were studied and the logistic and SVM classifiers achieved sensitivities of 70.4 and 87.7%, and specificities of 95.6 and 97.9%, respectively. Furthermore, the ovaries of two patients were noninvasively imaged using the PAT/US system before surgical excision. By using five significant features and the logistic classifier, 12 out of 14 images (86% sensitivity) from a malignant ovarian mass and all 17 images (100% specificity) from a benign mass were accurately classified; the SVM correctly classified 10 out of 14 malignant images (71% sensitivity) and all 17 benign images (100% specificity). These initial results demonstrate the clinical potential of the PAT/US technique for ovarian cancer diagnosis.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Hassan S. Salehi ; Hai Li ; Alex Merkulov ; Patrick D. Kumavor ; Hamed Vavadi, et al.
"Coregistered photoacoustic and ultrasound imaging and classification of ovarian cancer: ex vivo and in vivo studies", J. Biomed. Opt. 21(4), 046006 (Apr 18, 2016). ; http://dx.doi.org/10.1117/1.JBO.21.4.046006


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