Radiomics, radiogenomics and artificial intelligence in the study of liver and pancreatic tumors

Abstract

Two branches on which precision medicine is based are radiomics and genomics, in particular the latter analyzes the different molecules. The study of the molecules is the basis of the response to treatment and therefore of the choice of the different therapeutic strategies. Currently, radiomic data are typically not incorporated as part of this data stream; however, this is changing with the adoption of structured radiology reporting. The challenge going forward will be to capture radiomic data as part of the structured report. Based on multiple studies about liver and pancreas neoplasms it is clearly visible what radiomics has brought in terms of preoperative prognostic factors related to survival and prognostic stratification, based on degree of aggressiveness of the lesion, as well as the evaluation of factors associated with presence of metastases or presence of vascular microinvasion. Several studies broadly describe genomic approaches to solve different problems in the context of liver and pancreatic imaging. In particular segmentation, quantification, characterization and improvement of image quality. Artificial intelligence will not be able to replace man, who covers a fundamental role; for example, the radiologist’s experience in manual tumor segmentation. Surely the prospect is to bring help in terms of time consumption.