Multivariate Statistical Techniques for Accurately and Noninvasively Localizing Tumors Subject to Respiration-Induced Motion

dc.contributor.advisorD'Souza, Warren D.en_US
dc.contributor.advisorTao, Yangen_US
dc.contributor.authorMalinowski, Kathleen Theresaen_US
dc.contributor.departmentBioengineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2012-07-07T06:08:03Z
dc.date.available2012-07-07T06:08:03Z
dc.date.issued2012en_US
dc.description.abstractTumors in the lung, liver, and pancreas can move considerably with normal respiration. The tumor motion extent, path, and baseline position change over time. This creates a complex "moving target" for external beam radiation and is a major obstacle to treating cancer. Real-time tumor motion compensation systems have emerged, but device performance is limited by tumor localization accuracy. Direct tumor tracking is not feasible for these tumors, but tumor displacement can be predicted from surrogate measurements of respiration. In this dissertation, we have developed a series of multivariate statistical techniques for reliably and accurately localizing tumors from respiratory surrogate markers affixed to the torso surface. Our studies utilized radiographic tumor localizations measured concurrently with optically tracked respiratory surrogates during 176 lung, liver, and pancreas radiation treatment and dynamic MR imaging sessions. We identified measurement precision, tumor-surrogate correlation, training data selection, inter-patient variations, and algorithm design as factors impacting localization accuracy. Training data timing was particularly important, as tumor localization errors increased over time in 63% of 30-min treatments. This was a result of the changing relationship between surrogate signals and tumor motion. To account for these changes, we developed a method for detecting and correcting large localization errors. By monitoring the surrogate-to-surrogate and surrogate-to-model relationships, tumor localization errors exceeding 3 mm could be detected at a sensitivity of 95%. The method that we have proposed and validated in this dissertation leads to 69% fewer treatment interruptions than conventional respiratory surrogate model monitoring techniques. Finally, we extended respiratory surrogate-based tumor motion prediction to the otherwise time-consuming process of contouring respiratory-correlated computed tomography scans. This dissertation clarifies the scope and significance of problems underlying existing surrogate-based tumor localization models. Furthermore, it presents novel solutions that make it possible to improve radiation delivery to tumors without increasing the time required to plan and deliver radiation treatments.en_US
dc.identifier.urihttp://hdl.handle.net/1903/12718
dc.subject.pqcontrolledBiomedical engineeringen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledRoboticsen_US
dc.subject.pquncontrolledradiotherapyen_US
dc.subject.pquncontrolledregression analysisen_US
dc.subject.pquncontrolledrespirationen_US
dc.subject.pquncontrolledtumor motionen_US
dc.titleMultivariate Statistical Techniques for Accurately and Noninvasively Localizing Tumors Subject to Respiration-Induced Motionen_US
dc.typeDissertationen_US

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