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dc.contributor.authorKirlik, G.
dc.contributor.authorGullapalli, R.
dc.contributor.authorD’Souza, W.
dc.date.accessioned2019-06-05T18:28:20Z
dc.date.available2019-06-05T18:28:20Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057723082&doi=10.1177%2f1176935118786260&partnerID=40&md5=f54b43ae89f68651652216feecebab50
dc.identifier.urihttp://hdl.handle.net/10713/9471
dc.description.abstractProstate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, K trans using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision. Copyright The Author(s) 2018.en_US
dc.description.sponsorshipThis work was supported in part by US Department of Defense through the grant number W81XWH-04-1-0249.en_US
dc.description.urihttps://dx.doi.org/10.1177/1176935118786260en_US
dc.language.isoen-USen_US
dc.publisherSAGE Publications Ltden_US
dc.relation.ispartofCancer Informatics
dc.subjectdiagnostic imagingen_US
dc.subjectmultiparametric MRI/MRSIen_US
dc.subjectpredictive modelingen_US
dc.subjectprostate canceren_US
dc.titleA Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imagingen_US
dc.typeArticleen_US
dc.identifier.doi10.1177/1176935118786260


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