title: Coregistered photoacoustic and ultrasound imaging and classification of ovarian cancer: ex vivo and in vivo studies.
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.
authors.affiliation: University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States.
authors.forename: Hassan S
authors.initials: HS
authors.lastname: Salehi
authors.affiliation: University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States.
authors.forename: Hai
authors.initials: H
authors.lastname: Li
authors.affiliation: University of Connecticut Health Center, Division of Radiology, Farmington, Connecticut 06030, United States.
authors.forename: Alex
authors.initials: A
authors.lastname: Merkulov
authors.affiliation: University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States.
authors.forename: Patrick D
authors.initials: PD
authors.lastname: Kumavor
authors.affiliation: University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States.
authors.forename: Hamed
authors.initials: H
authors.lastname: Vavadi
authors.affiliation: University of Connecticut Health Center, Department of Pathology, Farmington, Connecticut 06030, United States.
authors.forename: Melinda
authors.initials: M
authors.lastname: Sanders
authors.affiliation: University of Connecticut Health Center, Division of Gynecologic Oncology, Farmington, Connecticut 06030, United States.
authors.forename: Angela
authors.initials: A
authors.lastname: Kueck
authors.affiliation: University of Connecticut Health Center, Division of Gynecologic Oncology, Farmington, Connecticut 06030, United States.
authors.forename: Molly A
authors.initials: MA
authors.lastname: Brewer
authors.affiliation: University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United StatescUniversity of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States.
authors.forename: Quing
authors.initials: Q
authors.lastname: Zhu
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D010053:Ovary / Q000000981:diagnostic imaging*; D061088:Photoacoustic Techniques / Q000379:methods*; D060388:Support Vector Machine; D014463:Ultrasonography / Q000379:methods*