CT Radiomics in Thoracic Oncology: Technique and Clinical Applications.
Geewon LeeSo Hyeon BakHo Yun LeePublished in: Nuclear medicine and molecular imaging (2017)
Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.
Keyphrases
- contrast enhanced
- lymph node metastasis
- high resolution
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- palliative care
- high throughput
- electronic health record
- dual energy
- image quality
- spinal cord
- healthcare
- big data
- deep learning
- machine learning
- artificial intelligence
- spinal cord injury
- cross sectional
- mass spectrometry
- free survival