Institute of Radiology, University Hospital Erlangen, Maximiliansplatz 1, Erlangen, Germany.
Division of Molecular and Experimental Surgery, Translational Research Center Erlangen, Department of Surgery, Erlangen, Germany.
PLoS One. 2018 Oct 26;13(10):e0206576. doi: 10.1371/journal.pone.0206576. eCollection 2018.
Magnetic resonance imaging (MRI) allows non-invasive evaluation of inflammatory bowel disease (IBD) by assessing pathologically altered gut. Besides morphological changes, relaxation times and diffusion capacity of involved bowel segments can be obtained by MRI. The aim of this study was to assess the use of multiparametric MRI in the diagnosis of experimentally induced colitis in mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity, specificity, positive- and negative-predictive values (PPV and NPV) of these single values in detecting DSS-treatment as a reference condition. A Model Averaged Neural Network (avNNet) was trained on the multiparametric combination of the measured values, and its predictive capacity was compared to those of the single parameters using exact binomial tests. Within the in vivo subgroup (n = 19), the avNNet featured a sensitivity of 91.3% (95% CI: 86.6-96.0%), specificity of 92.3% (95% CI: 85.1-99.6%), PPV of 96.9% (94.0-99.9%) and NPV of 80.0% (95% CI: 69.9-90.1%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.003) and performing significantly worse compared to none of the single values. Within the ex vivo subgroup (n = 30), the avNNet featured a sensitivity of 87.4% (95% CI: 82.6-92.2%), specificity of 82.9% (95% CI: 76.1-89.7%), PPV of 88.9% (84.3-93.5%) and NPV of 80.8% (95% CI: 73.8-87.9%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.015), exceeded by none of the single parameters. In experimental mouse colitis, multiparametric MRI and the combination of several single measured values to an avNNet can significantly increase diagnostic accuracy compared to the single parameters alone. This pilot study will provide new avenues for the development of an MR-derived colitis score for optimized diagnosis and surveillance of inflammatory bowel disease.
磁共振成像（MRI）可通过评估病理性改变的肠道，对炎症性肠病（IBD）进行非侵入性评估。除形态学改变外，还可以通过 MRI 获得受累肠段的弛豫时间和扩散能力。本研究旨在评估多参数 MRI 在诊断实验性结肠炎中的应用，并利用机器学习评估参数组合的诊断优势。该研究依赖于葡聚糖硫酸钠（DSS）诱导的结肠炎，并对体内和离体的小鼠结肠进行了研究。接收者操作特征用于计算这些单一值检测 DSS 治疗作为参考条件的敏感性、特异性、阳性和阴性预测值（PPV 和 NPV）。模型平均神经网络（avNNet）在测量值的多参数组合上进行训练，并使用精确二项式检验比较其预测能力与单一参数的预测能力。在体内亚组（n = 19）中，avNNet 的敏感性为 91.3%（95%CI：86.6-96.0%），特异性为 92.3%（95%CI：85.1-99.6%），PPV 为 96.9%（94.0-99.9%），NPV 为 80.0%（95%CI：69.9-90.1%），在至少 2 项准确性测量中明显优于所有单一参数（p <0.003），与任何单一参数相比，表现均明显更差。在离体亚组（n = 30）中，avNNet 的敏感性为 87.4%（95%CI：82.6-92.2%），特异性为 82.9%（95%CI：76.1-89.7%），PPV 为 88.9%（84.3-93.5%），NPV 为 80.8%（95%CI：73.8-87.9%），在至少 2 项准确性测量中明显优于所有单一参数（p <0.015），优于任何单一参数。在实验性小鼠结肠炎中，与单一参数相比，多参数 MRI 以及将多个单一测量值组合到 avNNet 中可显著提高诊断准确性。这项初步研究将为开发基于 MRI 的结肠炎评分以优化炎症性肠病的诊断和监测提供新途径。
