Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.
Margarita KirienkoMartina SolliniMarinella CorbettaEmanuele VoulazNoemi GozziMatteo InterlenghiFrancesca GallivanoneIsabella CastiglioniRosanna AsseltaStefano DugaGiulia SoldàArturo ChitiPublished in: European journal of nuclear medicine and molecular imaging (2021)
Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.
Keyphrases
- gene expression
- end stage renal disease
- small cell lung cancer
- ejection fraction
- newly diagnosed
- dna methylation
- papillary thyroid
- peritoneal dialysis
- genome wide
- prognostic factors
- genome wide identification
- electronic health record
- case report
- big data
- machine learning
- copy number
- young adults
- healthcare
- magnetic resonance
- advanced non small cell lung cancer
- risk assessment
- human health
- artificial intelligence
- patient reported outcomes
- combination therapy
- contrast enhanced
- childhood cancer