Comprehensive Analysis of Lung Cancer Prognostic Factors

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dc.contributor.advisorGerber, David E.en
dc.contributor.committeeMemberXie, Yangen
dc.contributor.committeeMemberXiao, Guanghuaen
dc.contributor.committeeMemberZhan, Xiaoweien
dc.contributor.committeeMemberHoshida, Yujinen
dc.creatorWang, Shidanen
dc.creator.orcid0000-0002-0001-3261
dc.date.accessioned2021-09-17T17:59:42Z
dc.date.available2021-09-17T17:59:42Z
dc.date.created2019-08
dc.date.issued2019-07-29
dc.date.submittedAugust 2019
dc.date.updated2021-09-17T17:59:43Z
dc.description.abstractLung cancer is the leading cause of death from cancer. It is remarkably heterogeneous in histopathological features and highly variable in prognosis. Analysis of prognostic factor is anticipated to guide clinicians for treatment selection, enhance patient care, and help understanding biological mechanism of tumor progression. To extend current knowledge about lung cancer prognosis, this dissertation analyzed lung cancer prognostic factors in three levels. First, in tumor level, deep learning aided pathology image analysis was used to extract tumor geometry and microenvironment features, upon which an image-based survival prediction model was built and independently validated for lung adenocarcinoma. Second, in patient level, a nomogram was built with demographic and clinical variables for patients with small cell lung cancer. The nomogram was implemented online for public usage. Third, in population level, how facility type and volume affect survival outcome and surgery selection for early stage non-small cell lung cancer was analyzed.en
dc.format.mimetypeapplication/pdfen
dc.identifier.oclc1268338267
dc.identifier.urihttps://hdl.handle.net/2152.5/9624
dc.language.isoenen
dc.subjectAlgorithmsen
dc.subjectDeep Learningen
dc.subjectDiagnostic Imagingen
dc.subjectImage Processing, Computer-Assisteden
dc.subjectLung Neoplasmsen
dc.subjectPathology, Clinicalen
dc.titleComprehensive Analysis of Lung Cancer Prognostic Factorsen
dc.typeThesisen
dc.type.materialtexten
thesis.degree.departmentGraduate School of Biomedical Sciencesen
thesis.degree.disciplineIntegrative Biologyen
thesis.degree.grantorUT Southwestern Medical Centeren
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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