Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) with uncertain etiology. CD can affect any area of the gastrointestinal tract from the mouth to the rectum and anus. The terminal ileum is the most vulnerable region in CD and ileocecal CD often leads to serious complications such as strictures and perforation. The incidence and prevalence of CD vary between geographical areas. In mainland China, data published in 2011 have shown an incidence of 1.21 cases per 100,000 persons with the prevalence estimated about 2.29 cases/100,000 people.
CD is characterized by high rates of recurrence. Currently, there are limited treatment options for CD. Patients with CD have a chronic disease that requires ongoing follow-up.
Endoscopy remains the reference standard. However, conventional endoscopy is invasive, shows limited evaluation of the small bowel, and requires sedation/anesthesia during the procedure. Clinical scores such as Crohn's disease activity index (CDAI) and Harvey–Bradshaw index (HBI) are easy and commonly used for CD evaluation. But the scores are based on subjective criteria. Due to the nature of its rapid response and short half-life, C-reactive protein (CRP) acts as a useful marker of inflammation especially in the management of CD. But the CRP level is dependent on the site and number of active CD lesions, and is therefore not specific.[6,7]
Recently, magnetic resonance imaging (MRI) has become the primary imaging tool for evaluation of disease activity in patients with CD because of the advantages of lack of ionizing radiation exposure and the ability to assess for both intestinal and extra-intestinal disease activity.[8–10] Compared with conventional imaging, newly emerging functional techniques such as dynamic contrast enhanced MRI (DCE-MRI) provide advantages in extraction of lesion mechanisms and offer more physiological information.
DCE-MRI is currently used to evaluate treatment responses for malignant tumors including glioblastoma, multiple myeloma, breast cancer, pancreatic cancer, and prostatic carcinoma. Meanwhile, studies have employed this technique to assess neovascularization in inflammatory diseases.[16,17] We have assessed the role of DCE-MRI in diagnosis and assessment for CD, such findings are similar to other researches.[19–21] In this paper, we present a preliminary study of CD by DCE-MRI and hypothesize that the parameters of DCE-MRI can be used to evaluate the therapeutic response.
2 Material and methods
This research was conducted from June 1, 2014, to January 31, 2016. All patients agreed to participate in a clinical trial of FMT for CD (NCT01793831) at The Second Affiliated Hospital of Nanjing Medical University. Twenty-two patients with CD (13 men and 9 women; age range 19–62 years, mean ± SD = 33.45 ± 11.39 years) were enrolled into the study. Inclusion criteria for this study were as follows: (1) first-visit subjects with complaints of digestive disorder; (2) adults aged 18 years or older; (3) underwent capsule enteroscopy and colonoscopy within the past 72 hours; (4) CD diagnosis confirmed by endoscopy and pathology; (5) only one lesion, and located at the terminal ileum; (6) ability to undergo MRI. This study was approved by the institutional review board. Informed consent was obtained from all patients.
2.1 Trial design
All patients with CD underwent MRI scanning twice. The first scan was prior to single fecal microbiota transplantation (FMT) treatment and the second scan was performed on day 90 after the initiation of the treatment. At the 2 time points, clinical data including laboratory examination, clinical scores and endoscopy were also collected. The changes in CRP and HBI were used as clinical indicators of disease progression. Serum samples were obtained from each subject for analysis of CRP prior to MRI scanning within the same day. The severity score for CD was defined and endoscopy was performed within 72 hours before MRI examination. DCE-MRI parameters were measured in the ileocecal region. Therapeutic response of each patient was assessed by correlating imaging parameters with clinical data.
2.2 MRI study protocol
MRI scan was performed using a 3.0 T clinical scanner (SignaHDxt, GE Healthcare) equipped with abdominal-pelvic coil (8 radio frequency channels). Subjects were scanned in supine position. No special preparation was required other than withholding intake of solid foods for 6 hours prior to MRI examination. All subjects were required to drink 300 mL of mannitol (2.5%) every 10 minutes until a total of 1.5 L has been consumed within 60 minutes. Immediately prior to the scan, all individuals were given of 20 mg of scopolamine-N-butyl bromide (Busco-pan; BoehringerIngelheim, Ingelheim, Germany) intravenously, to reduce bowel peristalsis motion artifacts.
Morphologic sequences and scan parameters were: (1) coronal T2 (single shot fast spin-echo, SSFSE) through the abdomen and pelvis with breath-holding (Tck = 5 mm, spacing = 1 mm; TR = 2800 ms, TE = 70 ms); (2) axial T2 fast spin-echo fat-suppressed images covering the abdomen and pelvis, free-breathing with navigator triggering (Tck = 4 mm, spacing = 2 mm; TR = 12000 ms, TE = 90 ms); (3) axial T1 LAVA-Flex Mask through the abdomen and pelvis with breath-holding (Tck = 4 mm, spacing = 0 mm; TR = 4.5 ms, TE = 1.7 ms).
DCE-MRI protocol included 2 steps: (1) 5 different flip angles (4°, 6°, 8°, 10°, and 12°) T1-weighted 3D-LAVAsequences to determine the T1 relaxation time in the blood and tissue for T1 mapping; (2) DCE-MRI using 3D T1-weighted LAVA sequence with a flip angle of 12°. After 3 pre-contrast acquisitions, Gadodiamide (OmniScan, GE Healthcare, Ireland) was intravenously injected (0.2 mmol/kg) with a rate of 3.0 mL/s. Then, 15 mL of saline was flushed with the same rate. Scan parameters were following: axial images; TR = 2.9 ms, TE = 1.1 ms; matrix = 224 × 160; FOV = 42 cm; scan layers = 52. The temporal resolution was 7 seconds and the total scan time was 3 minutes 30 seconds including 30 phases. According to the inclusion criteria for this study, the terminal ileum was chosen as DCE-MRI scanning reference slice.
2.3 Interpretation of MRI measurements
DCE-MRI parameters were calculated using a noncommercial software (Omni-Kinetics, GE Healthcare). First, the individual artery input function (AIF) was obtained from a region of interest (ROI) drawn on the abdominal aorta located in close proximity to the terminal ileum. Second, extended Tofts liner model was chosen for fitting of the tissue response curves. The pharmacokinetic parameter as Ktrans and hemodynamic parameter as BV were generated as color maps (Fig. 1). ROI (30–50 mm2) for these DCE-MRI parameters (Ktrans, BV) was placed on the maximal enhancing region of the terminal ileum.
2.4 Quantitative measurement
Response to treatment was determined by the change in CRP and HBI. For each subject, percent changes in CRP and HBI between baseline and 90-day follow-up was calculated using the following equation:
ΔCRP (%) = [(CRP 90days – CRP baseline)/ CRP baseline] × 100
ΔHBI (%) = [(HBI 90days – HBI baseline) / HBI baseline] × 100
Percent changes in DCE-MRI parameters relative to baseline were calculated as follows:
ΔKtrans(%) = [(Ktrans90days – Ktransbaseline) / Ktransbaseline] × 100
ΔBV (%) = [(BV90days – BVbaseline) / BVbaseline] × 100
2.5 Reproducibility of DCE-MRI parameter measurements
All parameters in DCE-MRI were independently evaluated by 2 radiologists (with a combined 10 years of body MRI experience), who were blinded to the clinical and endoscopic examination. To further assess the reproducibility and the repeatability of the measurements, Bland–Altman plots were generated and intraclass correlation coefficient (ICC) was calculated. In the graphic method, the differences between the 2 radiologists are plotted against the averages of the 2 radiologists. Agreement was classified as excellent (ICC > 0.75), moderate (ICC = 0.50–0.75), or poor (ICC < 0.50). The mean of the 2 values (measured by different radiologists) was accepted as the final result for the quantitative analysis.
2.6 Statistical analysis
First, all data in both clinical (CRP, HBI) and DCE-MRI (Ktrans, BV) were tested with the Kolmogorov–Smirnov test for normal distribution before analysis. Normally distributed data were expressed as mean and standard deviations (SDs), whereas non-normally distributed data were expressed as median and interquartile ranges (IQRs). Second, intrameasurement reproducibility of the DCE-MRI was assessed through the calculation of ICC. Third, for normally distributed data, Student's 2-tailed, paired t-test was used to test statistical significance of change from pre- to post-treatment for each parameter (Levene's test for homogeneity was conducted first to test the assumption of equal variance). Pearson's correlation coefficients were used to measure the strength of the relationship between each percent change of DCE-MRI parameters and CRP, HBI in CD patients. For data not normally distributed, nonparametric tests were used such as the Mann–Whitney test for pre- and post-treatment comparisons and Spearman analysis for correlation. Lastly, a logistic regression model was used to identify the changes of DCE-MRI parameters related to the treatment outcomes. Receiver operating characteristic curves (ROCs) were generated to assess which DCE-MRI parameter showed the best accuracy for evaluation of therapeutic response. Optimal cut-off values of each DCE-MRI parameter and combination of parameters for identification of good or poor treatment outcome (based on the changes of CRP and HBI between pre- and post-treatment) were determined. Areas under the ROCs (AUC) were derived to estimate the probability of correctly assessment of therapeutic response.
All the computations were performed using SPSS (version 18.0; IBM SPSS Inc., Chicago, IL). Statistical significance was set at P < .05.
Twenty-two patients who received FMT for CD were analyzed in this study (Table 1). All parameters in both clinical and DCE-MRI were normally distributed, means and SDs were provided for continuous variable in this study (Table 2).
3.1 Comparison of changes in the clinical data after treatment
The mean value of CRP decreased significantly (P = .008) from 62.68 ± 31.86 mg/L (range 19–121 mg/L) to 43.55 ± 29.63 mg/L (range 5–135 mg/L) on day 90 after FMT treatment. Percent change in the CRP value was -22.44 ± 43.65%. In parallel, HBI showed an analogous decline (P = .012) from baseline (mean 7.18 ± 2.10, range 3–11) to day 90 (mean 5.73 ± 2.33, range 3–11), and the change in HBI was –15.86 ± 36.48%. Among all 22 subjects, 6 patients were considered to have poor treatment outcomes due to increased CRP and HBI values compared to pretreatment (Fig. 2).
3.2 Reproducibility of MRI parameter measurements
There were 88 MRI sets in total including 44 sets for Ktrans (each 22 sets in pre- and post-treatment) and 44 sets for BV (each 22 sets in pre- and post-treatment). Ktrans and BV showed ICC values of 0.990 (95% confidence interval [CI], 0.981– 0.994) and 0.999 (95% CI, 0.998 – 0.999), respectively. The Bland–Altman plots suggested that interobserver agreement was high (Fig. 3).
3.3 Comparison of changes in the DCE-MRI parameters after treatment
The following parameters were significantly different before and after treatment: Ktrans (1.86 ± 0.87vs 1.39 ± 0.83 min−1; P = .017), BV (61.02 ± 28.49 vs 41.96 ± 22.75 mL/100 g; P = .005). Relative percent changes in Ktrans and BV on day 90 after treatment were –24.94 ± 46.07% and –21.29 ± 45.69%. After treatment, increased values of Ktrans and BV were showed in 6 and 5 patients respectively (Fig. 4).
3.4 Correlation of DCE-MRI parameters with clinical indicators after treatment
ΔKtrans and ΔBV were correlated with ΔCRP (Pearson correlation coefficient: r = 0. 659, P = .001; r = 0.442, P = .039). There were also positive correlations between the change of MRI parameters and HBI, such as ΔKtrans to ΔHBI (r = 0.496, P = .019), and ΔBV to ΔHBI (r = 0.476, P = .025). ΔKtrans showed higher correlation with the percent changes of clinical indicators than ΔBV (Fig. 5).
3.5 Comparison of assessment efficacy among the DCE-MRI parameters
According to the changes of clinical indicators after treatment, 22 subjects were divided to good (16 subjects with decreased clinical indicators) and poor treatment outcomes (6 subjects with increased clinical indicators). Bivariate logistic regression model was used to estimate the interaction of DCE-MRI parameters and treatment outcomes. ΔKtrans [odds ratio (OR) 0.010, 95% CI 0.000–0.521, P = .023] and ΔBV (OR 0.023, 95% CI 0.001–0.920, P = .045) were associated with treatment outcomes. The sensitivity of predicting the therapeutic response was 83.3% when ΔKtrans was –2%, and the specificity was 93.7%, both of which were calculated with the values for ΔBV (cut-off = –24.46%; sensitivity of 100% and specificity of 75.0%). The AUC of 0.906 for ΔKtrans was slightly higher than the AUC for ΔBV (0.865). However, when ΔKtrans and ΔBV were combined, a considerably higher sensitivity (81.3%) and specificity (100%) with a significant discriminative accuracy (AUC = 0.948) was found (Fig. 6).
4.1 Current research status about DCE-MRI in the assessment of therapeutic response
By a serial acquisition of T1weighted images before, during, and after the injection of a paramagnetic contrast agent, meaningful physiological parameters related to vessel volume and permeability can be calculated. Thus, DCE-MRI has the potential to serve as an imaging biomarker of the clinical therapeutic response especially in tumors to antivascular therapies. De Bruyne S et al assessed the role of DCE-MRI for evaluation of response to chemotherapy and bevacizumab and for prediction of progression-free survival in 19 patients with colorectal liver metastases. They concluded a decrease in Ktrans (>40%) was a favorable prognostic factor. Pishko et al applied Ktrans and BV as biomakers to investigate the effect of treatment in a rat model of human lung cancer brain metastasis. Recently, DCE-MRI has been applied for the detection of inflammatory diseases and assessment of the therapeutic response to treatment of inflammatory diseases. Liu et al evaluated DCE-MRI for assessment of perfusion in 10 rheumatoid arthritis patients. They found that DCE-MRI parameters correlated significantly with treatment responses between baseline and follow-up. Floc’h et al reported the use of DCE-MRI to quantify changes in vascular permeability in a guinea-pig model of inner ear inflammation. These studies have demonstrated that DCE-MRI has the potential to become an accepted noninvasive indicator of vascularity and therefore ultimately, a biomarker of treatment response.
4.2 Why to choose CRPand HBI as reference index in this study
In most experimental and clinical studies, histological tissue sections are set as gold-standard to validate the reliability of DCE-MRI parameters in predicting outcomes related to therapy. For example, Chen et al evaluated the correlation between parameters of DCE-MRI and microvessel density (MVD) measurements in rabbit VX2 liver tumor models. Jia et al used pathological complete response and major histological response as references to identify the capability of DCE-MRI in predicting treatment response among 48 breast cancer patients. In the current study, choosing CRP and HBI as reference was based on following reasons: (1) surgery is not the preferred treatment for CD, so it is difficult to obtain pathological samples; (2) MVD reports only a morphologic index of vasculature and cannot differentiate between functional vessels, whereas DCE-MRI parameters reflect those vessels with active perfusion; (3) CRP and clinical scores have been set as gold-standard in previous DCE-MRI researches[16,18]; (4) CRP and HBI are important monitoring tools in the clinical management of CD.[29,30]
4.3 The value of DCE-MRI parameters in this study
In CD pathogenesis, angiogenesis activated primarily by hypoxia is clearly related to inflammation. Some researchers[32,33] even emphasize that angiogenesis could play a key role as a cause of CD tissue injury and driving force of inflammation. Several alternate processes including the cell-to-extracellular matrix interaction, vessel wall maturation, and basal lamina modifications are implicated in new vessel development. Multiple new blood vessels gather together to build vascular nets. The process of new blood vessels forming is known as angiogenesis. Based on the above analysis, it is well established that in CD, angiogenesis represents microvascular remodeling which leads to increased vascular wall permeability, this in turn increases exudation from vessels into the extravascular extracellular space (EES); vascular density changes whereby blood flow increases. By changing the fecal dysbiosis, FMT as a safe, feasible, and efficient therapy that could help reduce intestinal inflammation and inhibit angiogenesis.[35,36]
The volume transfer constant of contrast agent from a plasma space to an EES, as defined Ktrans, has been used to characterize this microvascular permeability quantitatively. Another significant parameter: BV has been applied to calculate blood density and blood flow. In this study, Ktrans and BV decreased significantly after treatment and good correlation was shown between ΔKtrans, ΔBV, and changes of clinical indicators. In our prior study, we have found the parameters of DCE-MRI including Ktrans had significant correlation with CRP. Sinha et al concluded that the contrast agent rapidly passed from the vascular space into the EES, resulting in mural enhancement in CD patients. According to the above conclusions, we hypothesized that Ktrans as a pharmacokinetic parameter can reflect the change of microvascular permeability and may add valuable information about disease severity in CD. The current study also demonstrated the mean value of BV differed significantly between pre- and post-treatment. Similar results can be found in other research. For example, Nylund et al made a comparison of BV between CD patients with inflammation or fibrosis using contrast-enhanced ultrasound. They reported the fibrosis group had lower BV compared with the inflammation group (P = .001). These findings suggested BV as a perfusion parameter can assess the bowel wall vascularization and monitor therapeutic responses to treatment.
Furthermore, we demonstrated that when comparing Ktrans with BV, ΔKtrans performed better in assessment of therapeutic response than ΔBV. Similar results were also observed in a previously reported study. Keeping in mind that Ktrans reflects vascular permeability, whereas BV is influenced more by the number and density of microvessels, a few possible explanations should be considered. First, anatomic vascular changes in response to treatment are found to occur at a later point than changes in vascular permeability. Second, based on the pharmacokinetic model, Ktrans is less affected than BV by the molecular weight of the contrast agent and is therefore more accurate.
As described above, DCE-MRI is a method for characterization of angiogenic activity in CD. DCE-MRI parameters (Ktrans and BV) can provide quantitative information about the volume and permeability of these new vessels. By the combination of both parameters, a logistic regression equation was developed to generate a combination predicting factor for assessment of therapeutic responses. The AUC of the combined Ktrans and BV was higher than its individual components, indicating that both parameters together reflect comprehensive treatment response-related information.
Several limitations were present in this study: First, the study included data from a small number of patients. Second, we cannot obtain pathological specimens by FMT treatment, so the comparative analysis between pathology and DCE-MRI parameters was lacking. Our third limitation was due to the technique: currently, we cannot employ DCE-MRI to assess CD patients with multiple lesions, so the patients included were restricted to only 1 lesion and located only at the terminal ileum.
The DCE-MRI quantitative parameters (Ktrans and BV) could be used to precisely evaluate the therapeutic response of CD lesions after FMT therapy. As a promising inflammation quantification tool in clinical research of CD, DCE-MRI may contribute unique insights into the response of the lesion microenvironment to therapy. Therefore, we believe that DCE-MRI quantitative analysis technology might have broad applications in the field of precision medicine.
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Keywords:Copyright © 2017 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.
blood volume; Crohn's disease; dynamic contrast enhanced MRI; therapeutic responses; vascular transfer constant