Part of the methods are described in a supplementary file.
Patient and public involvement. Patients were not actively consulted for this study.
Ethics. The Institutional Review Board of Ghent University approved the animal studies (ECD/19–68 and ECD/20-67aanv). Mice were housed in the animal facility at Ghent University Hospital (Ghent, Belgium) according to the institutional animal healthcare guidelines. The study using patient samples was approved by the Ethics Committee of Ghent University Hospital (EC/2018/1493, Belgian study registration number B670201838339), and written informed consent was obtained from all participants.
Induction of gut fibrosis in mice. Seven-week-old male C57BL/6J mice (Janvier Labs, Le-Genest-Saint-Isle, France) were housed in open cages in a temperature-controlled room at 20°C with a dark-light cycle of 12h. All interventions occurred during the light cycle. Animals had free access to water and commercial chow (mouse maintenance chow, Carfil Labofood, Belgium) ad libitum, and no fasting periods were implemented. In addition, 2.5% dextran sulfate sodium (DSS, MW = 36,000 to 50,000; MP Biomedicals, Illkirch, France) was supplemented to the drinking water for seven consecutive days to induce intestinal fibrosis followed by a two-week recovery period during which mice received normal drinking water. This cycle was repeated twice. The induction of the intestinal inflammation caused by DSS was followed up using the Disease Activity Index (DAI), a combinational score of the weight evolution, stool consistency, and presence of blood in the stool.37
In the first experiment, 16 mice were subjected to DSS administration, and two control mice were included, receiving normal drinking water. The control mice underwent an MR examination at the baseline and endpoint (Weeks 0 and 9). Mice receiving DSS treatment were subsequently scanned after every DSS-induced inflammation peak (i.e., Weeks 1, 4, and 7) and after each two-week recovery period (Weeks 3, 6, and 9; Fig. 1A). Intermediate sampling occurred after scanning at Week 1 (n = 5 out of 16), Week 6 (n = 5 out of the remaining 11) and Week 9 (n = 6) for histological inflammation and fibrosis assessment. Control animals (n = 2) were sacrificed at Week 9. We first correlated the histopathological data with MRI parameters during recovery to assess the MRI parameters as a surrogate marker for fibrosis (Weeks 6 and 9). Next, we aimed to evaluate the correlating parameters for the longitudinal assessment of fibrosis development during chronic inflammation. To this end, scan parameters from all MRI scans were analyzed (Fig. 1A).
In the second experiment, we investigated the application of the best performing MRI parameter based on the results from the first experiment as a surrogate marker for antifibrotic therapy response. The mice were randomized into three groups for this experiment, each containing 12 animals (Fig. 1B). Control mice were scanned at the baseline and end of the experiment (Week 9). Intestinal fibrosis was induced in the remaining 24 mice, and these animals underwent MRI at the baseline and after each two-week recovery period (Weeks 3, 6, and 9). The baseline scan was included to allow a more accurate grouped comparison of the changes in MR parameters. The DSS-treated mice received REDX08397 daily (p.o. gavage, 10 mg/kg; Redx, Macclesfield, UK) suspended in an aqueous 0.5% (hydroxypropyl)methylcellulose solution (Sigma-Aldrich, MO, USA) or placebo (aqueous 0.5% (hydroxypropyl)methylcellulose solution).30 All mice were sacrificed after the final MR examination (Week 9).
Magnetic resonance imaging protocol. All details of the scanning protocol are available in Supplementary Table 1. In addition, MRI was performed on a 7 Tesla MRI scanner (PharmaScan, Bruker Biospin, Ettlingen, Germany). Axial T2-weighted images were obtained in each mouse. The MT imaging was acquired using two gradient-echo data sets with and without applying an off-resonance prepulse (frequency offset 2 kHz).
Magnetic resonance imaging analysis. Axial T2-weighted images were reviewed, and the most distal part of the distal colon was identified in each mouse. Two readers manually drew a region of interest (ROI) in consensus (a radiologist with 11 years of experience (IDK) and a research fellow with 3 years of experience (SB)) in the distal colon in an area where the bowel wall was well defined. The full thickness and entire circumference of the bowel wall was included within the ROI. Care was taken to exclude any intraluminal or mesenteric tissue. Once defined on the T2-weighted image, the ROI was copied and applied to the corresponding MT-image map (Fig. 2). If necessary, to account for bowel motion, minor adjustments for the position or size of the ROI were made when the ROI was copied.
The MT value was calculated using the formula \((MT0-MTsat)/MT0\), where MTsat and MT0 refer to the signal intensities acquired with and without the off-resonance prepulse saturation, respectively. The MT-image maps were generated using open-source image processing software (ImageJ, US National Institutes of Health, Bethesda, Maryland, USA). The MT of the bowel wall was divided by the MT of the paraspinal muscle at the same slice in each mouse to obtain a normalized MT ratio (MTR) to minimize individual variation.
Axial T2-weighted MR images were uploaded into commercially available research software (TexRAD, Feedback Plc, Cambridge, UK). The TA with a filtration-histogram technique was performed within the selected ROI using a previously published methodology.27 Filtration extracted and enhanced image features of different sizes (radii from 0 to 6 mm) within the ROI before the subsequent histogram quantification. A spatial scale filter of 2 mm was chosen for this study, highlighting the fine textural features. The heterogeneity within the ROI was quantified, and three textural parameters were obtained: kurtosis (pointiness of the pixel distribution), skewness (asymmetry of the pixel distribution), and entropy (inhomogeneity of the pixel distribution). These heterogeneity features have been previously described.27
Regarding the entropy, we calculated the incremental entropy (IncrEn) as a relative measurement to allow a more appropriate comparison between different experiments. This parameter represents the increase in entropy in the pathologic bowel over the normal bowel. This value was calculated in Experiment 1 by dividing the entropy in each mouse by the mean entropy of the two control animals at the baseline. In Experiment 2, IncrEn was obtained by dividing the entropy in each mouse by its entropy value at the baseline.
Proof-of-concept translation of magnetic resonance/texture analysis findings to Crohn’s disease in humans. The mean interval between MRI and surgery of the patients was 35 days. Region-by-region correlations between surgical specimens and MRE were conducted by an experienced radiologist (IDK) who was blinded to the clinical and histopathological data. Matching locations between the resected bowel segments and MRE images were identified using anatomic landmarks (surgically resected margins and ileocecal valve) or gross lesions (bowel fistula). The most stenotic areas were selected for histopathological assessment and MRE correlation, and one specimen per patient was obtained. Routine clinical assessments require only a conventional MRE; thus, no MT images were available, and only TA was feasible. The TA was performed by placing an ROI on the pathologic small bowel wall on the axial T2-weighted images in each patient (n = 5) and in a normal-appearing small bowel (n = 5) to calculate the normalized entropy (NormEn = entropy pathologic bowel/entropy normal bowel).
Statistical analysis. This study was codesigned and analyzed by an expert statistician (Biostatistics Unit, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium). The statistical analyses were performed in SPSS (v. 27; IBM, Armonk, NY) and all tests were two-tailed. A linear mixed model assessed differences in body weight and DAI progression. The Pearson correlation test was applied to explore the relationship between MRI parameters and pathophysiological data. A one-way analysis of variance was used when comparing the normally distributed data between groups, applying Dunnett’s multiple comparison correction (F values and adjusted p-values reported). Linear regression and a mixed model analysis were used to evaluate the MRI parameters. The Akaike information criterion was consulted to select the best covariance structure to perform a mixed model analysis when comparing parameters between different scan timepoints. The p-values from the mixed model analysis were corrected for multiple comparisons using the more stringent 99% confidence interval.
No data, methods or study materials will be made available to other researchers.