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dc.contributor.authorPohl, Kilian M
dc.contributor.authorKonukoglu, Ender
dc.contributor.authorNovellas, Sebastian
dc.contributor.authorAyache, Nicholas
dc.contributor.authorFedorov, Andriy
dc.contributor.authorTalos, Ion-Florin
dc.contributor.authorGolby, Alexandra Jacqueline
dc.contributor.authorWells, William Mercer
dc.contributor.authorKikinis, Ron
dc.contributor.authorBlack, Peter McLaren
dc.date.accessioned2017-11-08T19:54:56Z
dc.date.issued2011
dc.identifierQuick submit: 2013-11-15T10:51:13-05:00
dc.identifier.citationPohl, Kilian M, Ender Konukoglu, Sebastian Novellas, Nicholas Ayache, Andriy Fedorov, Ion-Florin Talos, Alexandra Golby, William M Wells, Ron Kikinis, and Peter M Black. 2011. “A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients.” Operative Neurosurgery 68 (March): ons225–ons233. doi:10.1227/neu.0b013e31820783d5.en_US
dc.identifier.issn0148-396Xen_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34341837
dc.description.abstractBACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.en_US
dc.language.isoen_USen_US
dc.publisherOxford University Press (OUP)en_US
dc.relation.isversionofdoi:10.1227/neu.0b013e31820783d5en_US
dc.relation.hasversionhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3099129/en_US
dash.licenseLAA
dc.subjectAutomatic Change Detection; Meningioma; Growth Rate; Time Series Analysis; Statistical Modeling; Longitudinal Studies; Slowly Evolving Pathologiesen_US
dc.titleA New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patientsen_US
dc.typeJournal Articleen_US
dc.date.updated2013-11-15T15:52:24Z
dc.description.versionAccepted Manuscripten_US
dc.rights.holderPohl KM, Konukoglu E, Novellas S, Ayache N, Fedorov A, Talos IF, Golby A, Wells WM, Kikinis R, Black PM
dc.relation.journalOperative Neurosurgeryen_US
dash.depositing.authorGolby, Alexandra Jacqueline
dc.date.available2017-11-08T19:54:56Z
dc.identifier.doi10.1227/neu.0b013e31820783d5*
workflow.legacycommentsaa.no Golby Golby emailed for aa 04-25-2017 MMen_US
dash.contributor.affiliatedGolby, Alexandra
dash.contributor.affiliatedBlack, Peter
dash.contributor.affiliatedWells, William
dash.contributor.affiliatedKikinis, Ron
dash.contributor.affiliatedFedorov, Andriy


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