Advanced imaging and Crohn's disease: An overview of clinical application and the added value of artificial intelligence. Issue 157 (December 2022)

• Record Type: Journal Article
• Authors: Grassi, Giovanni; Laino, Maria Elena; Fantini, Massimo Claudio; Argiolas, Giovanni Maria; Cherchi, Maria Valeria; Nicola, Refky; Gerosa, Clara; Cerrone, Giulia; Mannelli, Lorenzo; Balestrieri, Antonella; Suri, Jasjit S.; Carriero, Alessandro; Saba, Luca
• Is Part Of: European journal of radiology. Issue 157(2022)
• Publication Date: 2022-12
• Subjects: Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110551
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Advanced imaging offers important biomarkers for CD diagnosis, staging and prognosis. Automation could reduce inter-reader variability and stage CD lesions more objectively. AI can help towards a personalized medicine approach to CD affected patients. Abstract: Purpose: The purpose of this narrative review is to describe the clinical applications of advanced computed tomography (CT) and magnetic resonance (MRI) techniques in patients affected by Crohn's disease (CD), giving insights about the added value of artificial intelligence (AI) in this field. Methods: We performed a literature search comparing standardized and advanced imaging techniques for CD diagnosis. Cross-sectional imaging is essential for the identification of lesions, the assessment of active or relapsing disease and the evaluation of complications. Results: The studies reviewed show that new advanced imaging techniques and new MRI sequences could be integrated into standard protocols, to achieve a reliable quantification of CD activity, improve the lesions' characterization and the evaluation of therapy response. These promising tools are: dual-energy CT (DECT) post-processing techniques, diffusion-weighted MRI (DWI-MRI), dynamic contrast-enhanced MRI (DCE-MRI), Magnetization Transfer MRI (MT-MRI) and CINE-MRI. Furthermore, AI solutions show a potential when applied to radiological techniques in these patients. Machine learning (ML) algorithms and radiomic features prove to be useful in improving the diagnostic accuracy of clinicians and in attempting a personalized medicine approach, stratifying patients by predicting their prognosis. Conclusions: Advanced imaging is crucial in the diagnosis, lesions' characterisation and in the estimation of the abdominal involvement in CD. New AI developments are promising tools that could support doctors in the management of CD affected patients.