Machine learning applications for Radiomics: towards robust non-invasive predictors in clinical oncology

Chintan Parmar

Research output: ThesisDoctoral ThesisExternal prepared

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In this big-data era, like every other field, healthcare is also turning towards artificial intelligence (AI) and machine-learning (ML). In this thesis, state-of-the-art machine-learning methods were investigated for radiomic analyses. An unbiased evaluation of these advanced computational methods in terms of their accuracy and reliability is presented. Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice. With ever increasing patient specific data, this work could stimulate further research towards brining AI and precision medicine in routine clinical oncology.
Original languageEnglish
Awarding Institution
  • Maastricht University
  • Lambin, Philippe, Supervisor
  • Aerts, Hugo, Co-Supervisor
Award date18 May 2017
Place of PublicationMaastricht
Print ISBNs9789461596956
Publication statusPublished - 2017


  • oncology
  • precision medicine
  • medical imaging
  • radiomic analyses
  • artificial intelligence
  • machine learning methods

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