Radiomics imaging biomarkers from the perspective of tumor biology

Sebastian Sanduleanu

Research output: ThesisDoctoral ThesisInternal

296 Downloads (Pure)

Abstract

This thesis investigated broadly speaking the association between biology-based endpoints and artificial intelligence derived imaging biomarkers. The most important group of biomarkers (also called ‘radiomics signature’) in this thesis derived from CT and FDG-PET was able to accurately classify both lung and head and neck cancer patients as hypoxic (low tumour oxygen) or non-hypoxic in external datasets not seen before by the AI models. Other important findings in this thesis were that peritumoral regions (3 and 5mm around head and neck tumours) on CT did not have predictive value for overall survival, recurrence or metastasis. This thesis for instance also demonstrated that we can potentially enhance frozen section histology results by the addition of radiomics CT imaging biomarkers in solitary pulmonary nodules, having the potential to aid the surgeon in decision making about the most adequate type of surgery to remove the solitary nodule.
Original languageEnglish
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Lambin, Philippe, Supervisor
  • Jochems, Arthur, Co-Supervisor
Award date11 Jan 2022
Place of PublicationMaastricht
Publisher
DOIs
Publication statusPublished - 2022

Keywords

  • Radiomics
  • hypoxia
  • radiotherapy
  • tumour biology

Fingerprint

Dive into the research topics of 'Radiomics imaging biomarkers from the perspective of tumor biology'. Together they form a unique fingerprint.

Cite this