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Updated: Feb 19, 2026
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
Published on: June 21, 2024
Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer
Aniek J G Even1, Bart Reymen2, Matthew D La Fontaine3
1Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, The Netherlands; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, The Netherlands.
Background And Purpose:
We aimed to identify tumour subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlate these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4-PET/CT) and tumour vasculature (DCE-CT).
Materials And Methods:
36 non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumour into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumours into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test.
Results:
Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumour type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035).
Conclusions:
We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.
