KEYWORDS: Feature extraction, Magnetic resonance imaging, Visualization, Inflammation, Surgery, Proteins, Feature selection, Control systems, Performance modeling, In vivo imaging
Pediatric Crohn’s disease (pCD) is a chronic relapsing-remitting inflammatory disease of the gastrointestinal tract, where there is a significant need for non-invasive comprehensive markers to accurately target clinical interventions. While Magnetic Resonance Enterography (MRE) is often used to localize pCD in vivo, it is still limited in predicting treatment response and capturing pCD phenotypes. The goal of this study was to identify radiomic features on baseline MRE associated with disease activity and treatment outcomes in pCD, as well as investigate potential associations of radiomics with serum-based pCD subtypes. Baseline MRE scans were acquired from 45 pediatric subjects (including healthy controls and pCD patients) where the latter was further sub-grouped into responders (stable after treatment) and non-responders (required surgery or had active disease 2+ years after treatment initiation). Radiomic features were extracted from the terminal ileum on a per-voxel basis from MRE and evaluated via a multi-stage feature selection scheme for identifying disease presence and patient outcomes separately. A Random Forest (RF) classifier achieved an area under the ROC curve (AUC) of 0.83 in distinguishing diseased patients from healthy subjects and an AUC of 0.85 in distinguishing nonresponders from responders; in leave-one-out cross-validation. Top-ranked Gabor and Laws radiomic features were found to be significantly correlated with serum pCD phenotypes including anemia, inflammation risk, vitamin deficiency, and immune activity. Radiomic features may therefore offer the ability to better characterize pCD phenotypes and predict patient outcomes, which could then be effectively treated via targeted interventions.
Crohn’s Disease is a relapsing and remitting disease involving chronic intestinal inflammation that is often characterized by hypertrophy of visceral adipose tissue (VAT). While an increased ratio of VAT to subcutaneous fat (SQF) has previously been identified as a predictor of worse outcomes in Crohn’s Disease, bowel-proximal fat regions have also been hypothesized to play a role in inflammatory response. However, there has been no detailed study of VAT and SQF regions on MRI to determine their potential utility in assessing Crohn’s Disease severity or guiding therapy. In this paper we present a fully-automated algorithm to segment and quantitatively characterize VAT and SQF via routinely acquired diagnostic bowel MRIs. Our automated segmentation scheme for VAT and SQF regions involved a combination of morphological processing and connected component analysis, and demonstrated DICE overlap scores of 0.86±0.05 and 0.91±0.04 respectively, when compared against expert annotations. Additionally, VAT regions proximal to the bowel wall (on diagnostic bowel MRIs) demonstrated a statistically significantly, higher expression of four unique radiomic features in pediatric patients with moderately active Crohn’s Disease. These features were also able to accurately cluster patients who required aggressive biologic therapy within a year of diagnosis from those who did not, with 87.5% accuracy. Our findings indicate that quantitative radiomic characterization of visceral fat regions on bowel MRIs may be highly relevant for guiding therapeutic interventions in Crohn’s Disease.
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