Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

Abstract

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

“…The extracted data can then be combined with clinical features to generate a diagnostic or prognostic model for cancer or, even by means of adding artificial intelligence or machine learning allow for early detection of cancer. 89 - 93 However, there is a need to harmonize data towards a common standard. 94 …”

“…The extracted data can then be combined with clinical features to generate a diagnostic or prognostic model for cancer or, even by means of adding artificial intelligence or machine learning allow for early detection of cancer. [89][90][91][92][93] However, there is a need to harmonize data towards a common standard. 94 Current studies on quantitative imaging biomarkers in pancreatic cancer are hampered by small sample sizes, together with a lack of standardization in image pre-processing and acquisition protocols, external validation, and the substantial heterogeneity in features analyzed, making it hard to compare data sets.…”

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

“…There are a range of ways in which AI/ML/DL can support more accurate and reliable diagnosis of conditions that can severely impair patients’ quality of life. Since big data are mostly unstructured, natural language processing of texts [ 36 ], as well as medical image analysis of CAT scans, magnetic resonance images or ultrasound images [ 37 ], can be useful. AI-based diagnostic approaches could complement physicians’ efforts, creating macro efficiencies in the healthcare system and significant quality-of-life benefits for individual patients.…”