A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models
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ORIGINAL ARTICLE
A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models Ian Morilla1,2 Received: 16 October 2018 / Accepted: 5 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Fibrosis may be introduced as a severe complication of inflammatory bowel disease (IBD). This is a particular disorder causing luminal narrowing and stricture formation in the inflamed bowel wall of a patient denoting, possibly, need for surgery. Thus, the development of treatments reducing fibrosis is an urgent issue to be addressed in IBD. In this context, we require the finding and development of biomarkers of intestinal fibrosis. Potential candidates such as microRNAs, gene variants or fibrocytes have shown controversial results on heterogeneous sets of IBD patients. Magnetic resonance imaging (MRI) has been already successfully proven in the recognition of fibrosis. Nevertheless, while there are no numerical models capable of systematically reproducing experiments, the usage of MRI could not be considered a standard in the inflammatory domain. Hence, there is an importance of deploying new sequence combinations in MRI methods that enable learning reproducible models. In this work, we provide reproducible deep learning models of intestinal fibrosis severity scores based on MRI novel radiation-induced rat model of colitis that incorporates some unexplored sequences such as the flow-sensitive alternating inversion recovery or diffusion imaging. The results obtained return an 87:5% of success in the prediction of MRI scores with an associated mean-square error of 0.12. This approach offers practitioners a valuable tool to evaluate antifibrotic treatments under development and to extrapolate such noninvasive MRI scores model to patients with the aim of identifying early stages of fibrosis improving patients’ management. Keywords Severity scores of intestinal fibrosis MRI model Feature selection Deep learning models
1 Introduction In IBD the long-term evolution of a severe inflammation depends on the mucosa ability of healing the tissue damage. In certain cases, an excessive deposition of extracellular matrix components onto the bowel wall triggers the appearance of fibrosis causing the inflammation becomes chronic. Currently the standard IBD therapies are effective
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00521-020-04838-2) contains supplementary material, which is available to authorised users. & Ian Morilla [email protected] 1
LAGA, CNRS, UMR 7539, Laboratoire d’excellence Inflamex, Universite´ Sorbonne Paris Nord, 93430 Villetaneuse, France
2
Laboratoire d’excellence Inflamex, Research Centre of Inflammation, INSERM, BP 416, Paris, France
in their antiinflammatory aspects, reducing significantly inflammation; however, they do not show yet that successful in relation to the inflammation-associated stenosis or fibrosis, frequently requiring surgical
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