Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer.
Yu-Hung ChenKun-Han LueChih-Bin LinKuang-Chi ChenSheng-Chieh ChanSung-Chao ChuBee-Song ChangYen-Chang ChenPublished in: International journal of molecular sciences (2023)
Radiogenomic heterogeneity features in 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = -0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages ( p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.
Keyphrases
- positron emission tomography
- computed tomography
- pet ct
- single cell
- pet imaging
- deep learning
- small cell lung cancer
- copy number
- magnetic resonance imaging
- advanced cancer
- end stage renal disease
- magnetic resonance
- prognostic factors
- contrast enhanced
- dna methylation
- electronic health record
- brain metastases
- diffusion weighted imaging
- data analysis