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Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

Torna all'elenco Tesi anno 2019

Tesi

Tipo: Tesi

Titolo: Cancer tissue classification from DCE-MRI data using pattern recognition techniques

Anno di pubblicazione: 2019

Formato: Elettronico Cartaceo

Tipologia di tesi: Dottorato

Autori: Venianaki M.

Affiliazioni autori: IMT School for Advanced Studies, Lucca, Italy and CNR-ISTI, Pisa, Italy

Autori CNR:

  • MARIA VENIANAKI

Lingua: inglese

Sintesi: Cancer research has significantly advanced in recent years mainly through developments in medical genomics and bioinformatics. It is expected that such approaches will result in more durable tumour control and fewer side effects compared with conventional treatments such as radiotherapy or chemotherapy. From the imaging standpoint, non-invasive imaging biomarkers (IBs) that assess angiogenic response and tumor environment at an early stage of therapy are of utmost importance, since they could provide useful insights into therapy planning. However, the extraction of IBs is still an open problem, since there are no standardized imaging protocols yet or established methods for the robust extraction of IBs. DCE-MRI is amongst the most promising non-invasive functional imaging modalities while compartmental pharmacokinetic (PK) modelling is the most common technique used for DCE-MRI data analysis. However, PK models suffer from a number of limitations such as modelling complexity, which often leads to variability in the computed biomarkers. To address these problems, alternative DCE-MRI biomarker extraction strategies coupled with a profound understanding of the physiological meaning of IBs is a sine qua non condition. To this end, a more recent model-free approach has been suggested in the literature for DCE-MRI data analysis, which relies on the shape classification of the time-signal uptake curves of image pixels in a selected tumour region of interest. This thesis is centred on this classification approach and the clinical question of whether model-free DCE-MRI data analysis has the potential to provide robust, clinically significant biomarkers using pattern recognition and image analysis techniques.

Lingua sintesi: eng

Pagine totali: 138

Parole chiave:

  • Pattern recognition
  • Cancer tissue classification
  • Image segmentation
  • DCE-MRI
  • Medical imaging

Relatore: Ovidio Salvetti

Data discussione tesi: 28/02/2019

Altre informazioni: Program Coordinator: Prof. Rocco de Nicola, IMT School for Advanced Studies Lucca Advisor: Prof. Rocco De Nicola, IMT School for Advanced Studies Lucca Co-advisor: Dr. Ovidio Salvetti, National Research Council of Italy Co-advisor: Prof. Kostas Marias, Technological Educational Institute of Crete & Foundation for Research and Technology - Hellas The dissertation of Maria Venianaki has been reviewed by: Dr. Nikolaos Papanikolaou, Champalimaud Foundation Prof. Leontios Hadjileontiadis, Aristotle University of Thessaloniki & Khalifa University The evaluation commission was composed by: Dr. Davide Moroni, National Research Council of Italy Prof. Mirco Tribastone, IMT School for Advanced Studies Lucca Prof. Leontios Hadjileontiadis, Aristotle University of Thessaloniki & Khalifa University

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