Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
Keyphrases
- pet imaging
- artificial intelligence
- positron emission tomography
- deep learning
- computed tomography
- end stage renal disease
- big data
- machine learning
- high resolution
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- image quality
- pet ct
- poor prognosis
- convolutional neural network
- optical coherence tomography
- magnetic resonance
- oxidative stress
- photodynamic therapy
- fluorescence imaging
- lymph node metastasis
- robot assisted
- rectal cancer
- health information
- contrast enhanced
- anti inflammatory
- radical prostatectomy