Application of machine learning algorithms for multiparametric MRI-based evaluation of murine colitis

PLoS One. 2018 Oct 26;13(10):e0206576. doi: 10.1371/journal.pone.0206576. eCollection 2018.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Colitis / chemically induced
  • Colitis / pathology*
  • Colon / pathology
  • Dextran Sulfate / pharmacology
  • Female
  • Inflammatory Bowel Diseases / chemically induced
  • Inflammatory Bowel Diseases / pathology
  • Machine Learning
  • Magnetic Resonance Imaging / methods
  • Male
  • Mice
  • Pilot Projects
  • ROC Curve
  • Sensitivity and Specificity

Substances

  • Dextran Sulfate

Grants and funding

This work was in part funded by the Collaborative Research Center 1181 of the Deutsche Forschungsgemeinschaft DFG (CRC 1181; https://www.sfb1181.forschung.fau.de/), projects Z02 (T. Bäuerle) and B05 (K. Hildner), and DFG grants KFO257, FOR2438 (M. Stürzl), and BR5196/2-1 (N. Britzen-Laurent), by the W. Lutz Stiftung (M. Stürzl), and by the Interdisciplinary Center for Clinical Research (IZKF) of the Clinical Center Erlangen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.