Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.
Cristian AnghelMugur Cristian GrasuDenisa Andreea AnghelGina-Ionela Rusu-MunteanuRadu Lucian DumitruIoana Gabriela LupescuPublished in: Diagnostics (Basel, Switzerland) (2024)
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
Keyphrases
- artificial intelligence
- deep learning
- poor prognosis
- machine learning
- convolutional neural network
- big data
- long non coding rna
- end stage renal disease
- contrast enhanced
- newly diagnosed
- loop mediated isothermal amplification
- computed tomography
- label free
- peritoneal dialysis
- magnetic resonance imaging
- prognostic factors
- dna methylation
- gene expression
- low grade
- systematic review
- magnetic resonance
- rna seq
- dual energy
- fluorescence imaging
- sensitive detection
- patient reported
- mass spectrometry