Salehi Publishes Paper Based on NIH-Funded Research
Project in "Journal of Biomedical Optics"
Dr. Hassan S. Salehi, visiting assistant professor of electrical and computer
engineering in the College of Engineering, Technology, and Architecture,
published an article based on the NIH-funded research in the SPIE
Journal of Biomedical Optics, Volume 21, Issue 4, 046006, 2016. The paper,
“Coregistered
photoacoustic and ultrasound imaging and classification of ovarian cancer: ex
vivo and in vivo studies,” was written by lead author Dr. Salehi
along with his collaborators at the University of Connecticut, Department of
Electrical and Computer Engineering, Department of Biomedical Engineering, and
the University of Connecticut Health Center (UConn Health).
The paper reports on a study testing
capabilities of a novel co-registered photoacoustic and ultrasound imaging
system and classification algorithms using machine learning techniques for
ovarian cancer diagnosis. Most ovarian cancers are diagnosed at advanced stages
due to the lack of efficacious screening techniques. As a result, there is an
urgent need to improve the current clinical practice by advancing the
conventional imaging systems and detecting early malignancies in the ovary.
Photoacoustic tomography (PAT) is an emerging imaging modality with great
potential to assist transvaginal ultrasound for ovarian cancer
screening. Therefore, the authors have developed their co-registered
photoacoustic tomography (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
non-invasively 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.
This research was supported by the National
Institutes of Health (NIH grant number: R01CA151570).
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