The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.
Dalia Monir FahmyAhmed AlksasAhmed ElnakibAli MahmoudHeba KandilAshraf KhalilMohammed GhazalEric van BogaertSohail ContractorAyman S El-BazPublished in: Cancers (2022)
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
Keyphrases
- artificial intelligence
- deep learning
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- big data
- machine learning
- convolutional neural network
- loop mediated isothermal amplification
- lymph node metastasis
- magnetic resonance
- dual energy
- label free
- high resolution
- real time pcr
- diffusion weighted imaging
- image quality
- positron emission tomography
- low grade
- risk assessment
- climate change
- high grade
- photodynamic therapy
- pet ct
- quantum dots