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Rochester, NY, United States

Duryea J.,Harvard University | Neumann G.,Harvard University | Niu J.,Boston University | Totterman S.,United Virtual | And 6 more authors.
Arthritis Care and Research | Year: 2010

Objective. Magnetic resonance imaging (MRI) and radiography are established imaging modalities for the assessment of knee osteoarthritis (OA). The objective of our study was to compare the responsiveness of radiographic joint space width (JSW) with MRI-derived measures of cartilage morphometry for OA progression in participants from the Osteoarthritis Initiative (OAI). Methods. This study examined the baseline and 12-month visits of a subset of 150 subjects from the OAI. Measurement of radiographic JSW was facilitated by the use of automated software that delineated the femoral and tibial margins of the joint. Measures of medial compartment minimum JSW and JSW at fixed locations were compared with cartilage morphometry measures derived from MRI. The results were stratified by Kellgren/Lawrence (K/L) scale grade and by tibiofemoral anatomic axis angle. In order to examine the relative responsiveness of various techniques, we calculated the standardized response mean (SRM) between the 2 visits. Results. The SRM for radiographic JSW measured at the optimal location was -0.32 compared with -0.39 for the most responsive MRI measure. For the subgroup with a K/L scale grade of 2 or 3, the most responsive SRM values were -0.34 for radiographic JSW and -0.42 for MRI. Conclusion. Our study demonstrates that new measures using a software analysis of digital knee radiographic images are comparable with MRI in detecting OA progression, and potentially superior when considering the cost-effectiveness of the 2 imaging modalities. © 2010, American College of Rheumatology. Source

Shah V.,U.S. National Institutes of Health | Shah V.,VirtualScopics | Turkbey B.,U.S. National Institutes of Health | Turkbey B.,VirtualScopics | And 8 more authors.
Medical Physics | Year: 2012

Purpose: There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI). Methods: This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng/ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated. Results: For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient. Conclusions: This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accuratelylocalizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance. © 2012 American Association of Physicists in Medicine. Source

Turkbey B.,U.S. National Cancer Institute | Merino M.J.,U.S. National Institutes of Health | Gallardo E.C.,U.S. National Institutes of Health | Gallardo E.C.,Image science Institute | And 11 more authors.
Journal of Magnetic Resonance Imaging | Year: 2014

Purpose To compare utility of T2-weighted (T2W) MRI and diffusion-weighted MRI (DWI-MRI) obtained with and without an endorectal coil at 3 Tesla (T) for localizing prostate cancer. Materials and Methods This Institutional Review Board-approved study included 20 patients (median prostate-specific antigen, 8.4 ng/mL). Patients underwent consecutive prostate MRIs at 3T, first with a surface coil alone, then with combination of surface, endorectal coils (dual coil) followed by robotic assisted radical prostatectomy. Lesions were mapped at time of acquisition on dual-coil T2W, DWI-MRI. To avoid bias, 6 months later nonendorectal coil T2W, DWI-MRI were mapped. Both MRI evaluations were performed by two readers blinded to pathology with differences resolved by consensus. A lesion-based correlation with whole-mount histopathology was performed. Results At histopathology 51 cancer foci were present ranging in size from 2 to 60 mm the sensitivity of the endorectal dual-coil, nonendorectal coil MRIs were 0.76, 0.45, respectively. PPVs for endorectal dual-coil, nonendorectal coil MRI were 0.80, 0.64, respectively. Mean size of detected lesions with nonendorectal coil MRI were larger than those detected by dual-coil MRI (22 mm versus 17.4 mm). Conclusion Dual-coil prostate MRI detected more cancer foci than nonendorectal coil MRI. While nonendorectal coil MRI is an attractive alternative, physicians performing prostate MRI should be aware of its limitations. Copyright © 2013 Wiley Periodicals, Inc. Source

Rhodes C.,VirtualScopics
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2010

There is a global need for software to manage imaging based clinical trials to speed basic research and drug development. Such a system must comply with regulatory requirements. The U.S. Food and Drug Administration (FDA) has regulations regarding software development process controls and data provenance tracking. A key unanswered problem is the identification of which data changes are significant given a workflow model for image trial management. We report on the results of our study of provenance tracking requirements and define an architecture and software development process that meets U.S. regulatory requirements using open source software components. Source

Turkbey B.,U.S. National Institutes of Health | Mani H.,U.S. National Institutes of Health | Aras O.,U.S. National Institutes of Health | Ho J.,U.S. National Institutes of Health | And 16 more authors.
Radiology | Year: 2013

Purpose: To determine whether multiparametric magnetic resonance (MR) imaging can help identify patients with prostate cancer who would most appropriately be candidates for active surveillance (AS) according to current guidelines and to compare the results with those of conventional clinical assessment scoring systems, including the D'Amico, Epstein, and Cancer of the Prostate Risk Assessment (CAPRA) systems, on the basis of findings at prostatectomy. Materials and Methods: This institutional review board-approved HIPAA-compliant retrospectively designed study included 133 patients (mean age, 59.3 years) with a mean prostate-specific antigen level of 6.73 ng/mL (median, 4.39 ng/mL) who underwent multiparametric MR imaging at 3.0 T before radical prostatectomy. Informed consent was obtained from all patients. Patients were then retrospectively classified as to whether they would have met AS eligibility criteria or were better served by surgery. AS eligibility criteria for prostatectomy specimens were a dominant tumor smaller than 0.5 mL without Gleason 4 or 5 patterns or extracapsular or seminal vesicle invasion. Conventional clinical assessment scores (the D'Amico, Epstein, and CAPRA scoring systems) were compared with multiparametric MR imaging findings for predicting AS candidates. The level of significance of difference between scoring systems was determined by using the x2 test for categoric variables with the level of significance set at P , .05. Results: Among 133 patients, 14 were eligible for AS on the basis of prostatectomy results. The sensitivity, positive predictive value (PPV), and overall accuracy, respectively, were 93%, 25%, and 70% for the D'Amico system, 64%, 45%, and 88% for the Epstein criteria, and 93%, 20%, and 59% for the CAPRA scoring system for predicting AS candidates (P , .005 for all, x2 test), while multiparametric MR imaging had a sensitivity of 93%, a PPV of 57%, and an overall accuracy of 92% (P , .005). Conclusion: Multiparametric MR imaging provides useful additional information to existing clinicopathologic scoring systems of prostate cancer and improves the assignment of treatment (eg, AS or active treatment). © RSNA, 2013. Source

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