Machine learning identifies multi-parametric functional PET/MR imaging cluster to predict radiation resistance in preclinical head and neck cancer models.
Simon BoekeRené M WinterSara LeibfarthMarcel A KruegerGregory BowdenJonathan CottonBernd J PichlerDaniel ZipsDaniela ThorwarthPublished in: European journal of nuclear medicine and molecular imaging (2023)
A quantitative imaging metric was described in a preclinical study indicating that radiation-resistant subvolumes in HNC may be detected by clusters of ADC and FMISO using combined PET/MRI which are potential targets for future functional image-guided RT dose-painting approaches and require clinical validation.
Keyphrases
- machine learning
- contrast enhanced
- computed tomography
- high resolution
- positron emission tomography
- pet ct
- diffusion weighted imaging
- diffusion weighted
- cell therapy
- magnetic resonance imaging
- radiation induced
- pet imaging
- genome wide
- artificial intelligence
- gene expression
- mesenchymal stem cells
- stem cells
- deep learning
- radiation therapy
- dna methylation
- climate change