A review on advances in 18 F-FDG PET/CT radiomics standardisation and application in lung disease management.
Noushin AnanRafidah ZainonMahbubunnabi TamalPublished in: Insights into imaging (2022)
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18 F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18 F-FDG PET/CT in the field of oncology is well established. Clinical application of 18 F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18 F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18 F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18 F-FDG PET/CT in lung disease management.
Keyphrases
- positron emission tomography
- computed tomography
- contrast enhanced
- lymph node metastasis
- papillary thyroid
- high throughput
- oxidative stress
- magnetic resonance imaging
- healthcare
- deep learning
- squamous cell carcinoma
- pet ct
- palliative care
- single cell
- stem cells
- pet imaging
- magnetic resonance
- cell therapy
- social media
- mesenchymal stem cells
- convolutional neural network
- young adults
- gene expression
- anti inflammatory