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.

中文翻译：

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在磁共振成像模型中评估肠道纤维化的深度学习方法

摘要

纤维化可能是炎症性肠病（IBD）的严重并发症。这是引起患者发炎的肠壁中管腔狭窄和狭窄形成的特殊病症，表明可能需要手术。因此，开发减少纤维化的疗法是IBD中亟待解决的问题。在这种情况下，我们需要发现和发展肠道纤维化的生物标志物。潜在的候选物，如microRNA，基因变体或纤维细胞，对IBD患者的异质性组显示出有争议的结果。磁共振成像（MRI）在纤维化识别中已被成功证明。然而，尽管没有能够系统地再现实验的数值模型，但是在炎性领域，MRI的使用不能被视为标准。因此，在能够学习可再现模型的MRI方法中部署新的序列组合非常重要。在这项工作中，我们基于MRI新型放射诱发的结肠炎大鼠模型提供了可重现的肠道纤维化严重程度评分的深度学习模型，该模型结合了一些未探索的序列，例如流量敏感的交替倒置恢复或扩散成像。获得的结果返回MRI得分预测的成功率为（87.5％），相关的均方误差为0.12。这种方法为从业人员提供了宝贵的工具，可用于评估正在开发的抗纤维化治疗方法，并将这种非侵入性MRI评分模型外推给患者，以期确定纤维化的早期阶段，从而改善患者的管理。