Quantification of tumor heterogeneity using PET/MRI and machine learning

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URI: http://hdl.handle.net/10900/91946
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-919462
http://dx.doi.org/10.15496/publikation-33327
Dokumentart: Dissertation
Date: 2021-07-25
Language: English
Faculty: 4 Medizinische Fakultät
Department: Medizin
Advisor: Pichler, Bernd J. (Prof. Dr.)
Day of Oral Examination: 2019-06-07
DDC Classifikation: 610 - Medicine and health
Keywords: Maschinelles Lernen
Other Keywords:
Tumor heterogeneity
PET/MRI
Multiparametric imaging
Oncology
License: Publishing license including print on demand
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Inhaltszusammenfassung:

Dissertation ist gesperrt bis 25. Juli 2021 !

Abstract:

Despite a broad understanding that solid tumors exhibit significant tissue heterogeneity, clinical trials have not seen a remarkable development in techniques that aid in characterizing cancer. Needle biopsies often represent only a partial view of the tumor profile, lacking the ability to comprehensively reflect spatiotemporal phenotypic changes. Recent multimodal multiparametric imaging techniques could provide further valuable insights if the complementary imaging information is sufficiently analyzed. Therefore, in this work I developed and applied machine learning methods on multiparametric positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, acquired using mice bearing subcutaneous tumors, to obtain a precise spatio-temporal characterization of intratumor heterogeneity.

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