Statistical Approaches for Interpretable Radiomics

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dc.contributor.advisorPeterson, Christine B.en_US
dc.contributor.committeeMemberVannucci, Marinaen_US
dc.creatorShoemaker, Katherineen_US
dc.date.accessioned2019-05-17T19:12:37Zen_US
dc.date.available2020-05-01T05:01:09Zen_US
dc.date.created2019-05en_US
dc.date.issued2019-04-17en_US
dc.date.submittedMay 2019en_US
dc.date.updated2019-05-17T19:12:37Zen_US
dc.description.abstractImaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The emerging field of radiomics aims to extract quantitative features from these images which can be used for downstream modeling. Much of the current work in radiomics relies on methods that do not lend themselves to communicating results to physicians. In order for radiomics to be used in clinically accepted tools, there is a motivation to move away from black box methods towards more interpretable approaches. In this thesis, we present two projects that aim to address the need for meaningful features in radiomic analyses. In the first project, we develop a hierarchical tree structure on the image pixels, creating a feature that captures intra-tumor heterogeneity. We demonstrate that this feature can be used in the classification of adrenal lesions. In the second project, to classify subjects on the basis of their radiomic features, we propose a Bayesian variable selection approach that favors the inclusion of more reliable features, and can additionally identify relevant genomic covariates if available. We apply this model to radiomic data from CT scans of head and neck cancer patients, using as our prior information a reliability metric obtained from a study on the impact of different scanners on feature stability.en_US
dc.embargo.terms2020-05-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationShoemaker, Katherine. "Statistical Approaches for Interpretable Radiomics." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/106005">https://hdl.handle.net/1911/106005</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/106005en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectradiomicsen_US
dc.subjectBayesianen_US
dc.subjecttreesen_US
dc.subjectvariable selectionen_US
dc.titleStatistical Approaches for Interpretable Radiomicsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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