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
- convolutional neural network
- machine learning
- big data
- end stage renal disease
- long non coding rna
- contrast enhanced
- ejection fraction
- chronic kidney disease
- newly diagnosed
- magnetic resonance imaging
- high resolution
- loop mediated isothermal amplification
- real time pcr
- prognostic factors
- randomized controlled trial
- patient reported outcomes
- dna methylation
- low grade
- image quality
- magnetic resonance
- rna seq
- dual energy
- mass spectrometry
- sensitive detection
- quantum dots