Magnetic Resonance Imaging-based ileal motility quantification predicts stricture response to biologic therapy in Crohn's disease.

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Valeria Peña-Trujillo, Sebastian Gallo-Bernal, Christopher J. Moran, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6306445/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Pediatric Radiology → Version 1 posted 9 You are reading this latest preprint version Abstract Objective Strictures are a common feature of Crohn’s disease (CD), impairing bowel function, complicating treatment, and often leading to surgery. Predicting which lesions are responsive to medical therapy and which will become obstructive represents an important clinical goal. We aim to characterize ileal strictures in patients with CD by exploring the bowel wall motility in patients receiving biologic agents and evaluate motility’s potential role in predicting therapeutic response compared with standard, transmural MRI biomarkers. Materials and Methods This retrospective study included subjects aged 10–28 with stricturing CD who underwent initiation or escalation of biologic therapy and clinically indicated MRE within a two-month window. Clinical response and disease activity were evaluated up to six months after therapy initiation or adjustment. Subjects were categorized as "non-responders" if they experienced worsening disease activity, required discontinuation of therapy, or needed surgical intervention. Simplified MR indexes of activity (sMaRIA) assessed the terminal ileum, and motility data were processed using GIQuant software. Sociodemographic and clinical variables were compared between responders and non-responders. Results A total of 40 subjects (52.5% female; mean age 19.77 ± 5.13) were included, with 15 (37.5%) classified as non-responders. No significant differences were observed in age, gender, stricture length, wall thickness, stricture volume, or T2-weighted signal intensity. However, mean motility (176.03 ± 128.63 vs. 67.83 ± 33.88; p = 0.006) and its standard deviation (72.06 ± 55.55 vs. 30.27 ± 25.79; p = 0.017) were significantly higher in responders. Motility outperformed sMaRIA (AUC 0.812 vs. 0.613; p = 0.07) in predicting response. Conclusion Quantitative MRI motility assessment was superior to traditional MRI biomarkers in predicting response to biologic therapy in adolescents and young adults with stricturing CD. Biomarkers Imaging Treatment response Crohn’s disease Stricture Figures Figure 1 Figure 2 Introduction One of the most common complications of Crohn’s disease (CD) is the development of intestinal strictures, defined by bowel wall thickening, luminal narrowing, and proximal dilation as seen with cross-sectional imaging. These strictures can initially be inflammatory and responsive to therapy; however, with time, fibrosis and muscular hypertrophy may predominate, contributing to bowel obstruction and an increased risk of penetrating complications [ 1 – 3 ]. Differentiating between inflammatory and fibrotic components of strictures is critical for treatment planning, particularly as the latter often requires surgical intervention [ 4 ]. Recent studies suggest that magnetic resonance (MR) imaging has the potential to detect not only these fibrotic changes but also muscular hypertrophy, a growing area of interest in stricturing CD and essential to future drug development. While biologic therapies, particularly anti-tumor necrosis factor (anti-TNF) agents, have revolutionized CD management by targeting specific inflammatory pathways, treatment outcomes remain highly variable and decrease over time [ 5 ]. According to Plumb et al., approximately 15–30% of CD patients require anti-TNF agents, although more recent data from Kandavel et al. demonstrated the usage may now approach 60%[ 6 ]. Concerningly, a significant proportion do not achieve a clinically meaningful response. About 10–30% fail to respond initially, and an additional 20–50% lose their response within the first year [ 7 ]. Traditional diagnostic approaches, including endoscopy and histology, provide critical insights into the structural and inflammatory status of the bowel but are invasive and often limited in accessing the stricture segment or extracting tissue from deeper bowel layers. Magnetic resonance enterography (MRE), a non-invasive imaging modality, offers a comprehensive assessment of the bowel and its complications, including strictures, and has emerged as a standard imaging tool in CD management [ 8 , 9 ]. Conventional MRI markers, such as wall thickness, contrast enhancement, and T2-weighted mural hyperintensity, are helpful in assessing disease severity through structural changes, but such measures often lack predictive power for treatment response, particularly in strictures [ 10 ]. Recent advances in MR techniques have introduced the ability to quantify intestinal bowel wall motion (hereafter referred to as motility), offering insights into bowel function that complement structural changes and validate as a marker for disease activity [ 7 , 9 , 11 ]. It has previously been shown that intestinal motility is reduced in strictures compared to morphologically normal bowel [ 12 ]. Beek et al. have demonstrated altered motility pre-stricture impacting symptoms with VanRein also advancing methodology for objective assessment [ 1 , 13 ]. However, no studies to date have performed a detailed analysis of strictures themselves, particularly in the context of motility and treatment response. In this study, we aim to comprehensively evaluate ileal strictures in children and young adults with CD undergoing biologic therapy, with a particular focus on motility. We hypothesize that quantitative motility assessment using MRE can serve as a non-invasive predictor of treatment response, with higher motility correlating with better outcomes. Materials and Methods Population: The cohort included all pediatric and young adult patients aged 10 to 28 years who had a diagnosis of CD complicated by imaging-confirmed ileal strictures during the study period of March 2017 to March 2022 and who were undergoing either initiation or dose escalation of anti-TNF agents (i.e., infliximab, adalimumab, or certolizumab). We then selected those patients who had changes in their medical regimen—either through medication changes (e.g., switching to another type of biologic medication) or dose adjustments (e.g., increasing the dose by altering the frequency or amount of the medication)—and who also had a clinically indicated MR enterography within a two-month timeframe from the medication change. Subjects with a history of intestinal surgery involving the removal of the terminal ileum were excluded due to the significant impact such surgeries could have on disease presentation and treatment outcomes. However, the use of concurrent anti-inflammatory treatments, like immunomodulators or corticosteroids, did not disqualify patients from participating in the study. This retrospective study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. It was approved by the (blinded) Human Research Committee. Human Ethics and Consent to Participate declarations: not applicable Clinical trial number: not applicable This study was conducted without any financial support. MRI protocol: All participants underwent clinically indicated MRI examinations of the small bowel using a 1.5-Tesla (Artist, General Electric Healthcare, Waukesha, WI) or 3.0-Tesla (Prisma, Siemens Healthineers, Malvern, PA) clinical MR scanner with multichannel phased array torso coil. These examinations were conducted with subjects awake and without sedation. The imaging protocol included standardized anatomical and quantitative acquisitions and dynamic cine imaging. This study focused on analyzing cinematic images obtained in the coronal plane using a 2D balanced steady-state free precession (SSFP) sequence. Additionally, anatomic images were acquired in both axial and coronal planes utilizing T2-weighted single-shot fast spin-echo (ssT2W) sequences without fat saturation. Oral contrast (Breeza, Beekley Medical, Bristol, CT or MiraLAX (polyethene glycol 3350)) was administered following our institutional weight-based protocol. Image processing and analyses: All functional and morphological analyses used as predictors in this study were conducted using FDA-cleared software GIQuant (Motilent, London, UK). The imaging-derived predictors included the Simplified MR Index of Activity (sMaRIA) score, an imaging-based metric designed to assess and quantify disease activity in CD by evaluating parameters such as bowel wall thickness, relative contrast enhancement, and presence of edema or ulceration[ 14 ]. Using different Motilent tools, we also measured the length of the inflamed bowel, stricture volume, wall thickness, average T2-weighted signal intensity, and mean motility of strictures [ 10 ]. All measures were made on the image analysis platform Entrolytics (Motilent, London, UK) Initially, T2-weighted coronal ssT2W images were analyzed by a fellowship-trained abdominal and pediatric radiologist with over 15 years of experience. The radiologist identified and localized the largest segment of ileal stricture and assessed the terminal ileum, defined as the distal 15-cm segment, to calculate the sMaRIA score. The length of the inflamed bowel segment was measured using a custom-built tool designed for precise linear measurements. This tool allowed the placement of multiple points along the affected intestinal lumen, which were then automatically interpolated to form a detailed centerline accurately representing the length of the inflamed segment. Subsequently, semi-automated volumetric segmentations of the inflamed ileum were performed using a specialized software tool GISeg, (Motilent, London, UK). This tool produced a three-dimensional bowel model from the 2D MRI slices, providing a volume score in cubic centimeters (cm³). The segmentation process started with the manually created centerline of the ileum. The software applied an unbiased k-means clustering technique to divide the image into contiguous segments. Each segment was characterized by a custom feature set and analyzed using a random forest regressor to estimate the likelihood of each segment being part of the inflamed ileal wall. To calculate the mean bowel wall thickness, Distance Transform morphological operations were applied to the bowel wall mask to measure thickness at various points along the mask. These measurements were averaged across all central pixels within the mask to provide a mean thickness for the bowel wall in the inflamed segment. For motility analysis, de-identified coronal cine images were reviewed by the primary operator. A polygon region of interest (ROI) was placed around the affected bowel segment to quantify intestinal motility at the stricture site. An FDA cleared algorithm (GIQuant) was used to produce quantitative motility maps using a previously described method based on image registration. The mean pixel value from these maps was reported as a numerical motility score in arbitrary units (AU). Scores below 150 AU indicated reduced motility (hypokinesis or akinesis), while scores above 300 AU reflected normal motility. These scores were visualized through heatmaps, providing a comprehensive view of bowel motion dynamics and facilitating a thorough assessment of intestinal motility. Clinical Outcomes: Responders vs. Non-Responders Systematic electronic medical record reviews were conducted to evaluate clinical responses and disease activity up to six months following the initiation or adjustment of biological therapy. Patients were categorized into two groups based on their treatment response, as documented in their electronic medical records. Non-response was defined as increased disease activity, discontinuation of anti-TNF therapy due to ineffectiveness, or the need for surgical interventions. Statistical Analysis Qualitative variables were reported as frequencies and percentages, while quantitative variables were expressed as means with standard deviations. Nonparametric tests were used for continuous variables to compare sociodemographic and clinical characteristics between responders and non-responders, and chi-square (χ²) tests were applied for categorical variables. The association between clinical and MRE-derived variables and treatment outcomes was evaluated using multivariable logistic regression analysis. This model was developed using a forward selection method to systematically identify significant predictors. We chose the forward selection method because, given our study's limited number of positive outcomes, it helps avoid overfitting by incrementally including only the most relevant predictors. In a post-hoc analysis, receiver operating characteristic (ROC) curve analyses were conducted to compare the predictive performance of motility and the sMaRIA score for treatment response. The area under the curve (AUC) was calculated to assess and compare the discriminative power of these two predictors. All tests were two-sided, and the significance level was set to p < 0.05. Correction for multiple comparisons was performed using the Bonferroni method. All statistical analyses were performed using R software version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria). Results General population A total of 40 subjects (52.5% female; mean age 19.77 ± 5.13 years) were included in the analysis, with 15 subjects (37.5%) classified as non-responders. Responders versus non-responders There were no statistically significant differences in age (responders: 20.12 ± 4.92 years vs. non-responders: 19.2 ± 5.59 years; p = 0.603) or gender (responders: 53.3% male vs. non-responders: 52% male; p = 0.935) between the two groups. There was no difference between groups in terms of sMaRIA score p = 0.355, stricture length (9.18 ± 5.73 vs. 9.04 ± 4.75 cm; p > 0.05), wall thickness (6.58 ± 2 vs. 6.24 ± 1.56 mm; p > 0.05), stricture volume (21.14 ± 12 vs. 30.13 ± 24.7; p > 0.05), and average T2-weighted signal intensity (363.12 ± 340.79 vs. 255.073 ± 229.8; p > 0.05). There were differences in mean stricture motility (176.03 ± 128.63 vs. 67.83 ± 33.88; p = 0.006) and its estimated standard deviation (72.06 ± 55.55 vs. 30.27 ± 25.79; p = 0.017) between responders and non-responders (Fig. 1 ). Multivariate analysis On multivariate analysis adjusted for all covariates, only the variables selected by the forward selection method were included in the final model. The analysis revealed that patients with higher mean motility showed higher odds of being responders, with an odds ratio of 1.020 (CI, 1.001–1.038; P = 0.039). No additional statistically significant associations were found. The results of the multivariate analysis are summarized in Table 1 . Table 1 Multivariate analysis Feature Odds ratio 95% CI p-value Lower Upper Wall thickness 1.39 0.706 2.736 0.341 Motility mean 1.023 1.002 1.045 0.033 Motility standard deviation 0.957 0.884 1.035 0.271 sMaRIA 0.604 0.205 1.779 0.36 ROC Curve analysis ROC curves were fitted, and the area under the curve (AUC) of each test was estimated. Motility performed better than sMaRIA as a predictor of no response to treatment. The AUC for Motility was 0.812, indicating good predictive ability, while the AUC for sMaRIA was 0.613, showing lower performance in predicting treatment response (Fig. 2 ). There was a trend toward better performance with Motility when compared to the sMaRIA though this difference did not reach statistical significance (P = 0.07). Discussion This study aimed to characterize ileal strictures in patients with CD by examining bowel wall motion in those receiving biological agents and comparing its predictive value to several additional MRE-derived variables for determining treatment outcomes in stricturing CD. Our results demonstrate that higher mean motility scores within an intestinal stricture are significantly associated with positive treatment outcomes. This suggests that motility metrics, reflecting the functional status of the bowel, can serve as predictors of therapeutic success in pediatric and young adult patients with stricturing CD undergoing treatment with anti-TNF therapy. While traditional MRI biomarkers have been widely used, their predictive value in this context appears to be limited [ 15 ]. For example, traditional anatomical measures, such as stricture length, wall thickness, and the Simplified MR Index of Activity (sMaRIA) score, did not show significant predictive value in this study. The sMaRIA score, in particular, demonstrated a lower area under the ROC curve (AUC = 0.613) compared to motility (AUC = 0.812), indicating lower effectiveness in predicting treatment response. These findings underscore the potential of incorporating dynamic functional assessments like motility into the evaluation and management of CD. It is likely that the more traditional morphologic MR biomarkers, which are sensitive to transmural inflammatory changes, do not have sufficient ability to evaluate the reversibility of active inflammation before the onset of biologic therapy. Non-invasive imaging biomarkers such as these could significantly enhance the personalization of treatment strategies, allowing for more accurate prediction of patient outcomes and timely adjustments to therapy. One hypothesis for why patients with higher motility respond better to treatment is that increased motility suggests less fibrosis and less smooth muscle hypertrophy in the affected bowel segments [ 16 – 18 ]. Fibrosis can lead to rigid and non-compliant bowel segments, reducing their ability to respond dynamically to inflammation and medical treatment [ 17 – 19 ]. Additionally, severe and chronic inflammation can cause irreversible neuroendocrine damage, impairing the bowel's motility and ability to function normally [ 20 ]. Patients with preserved or higher motility likely have less structural and functional damage, making their bowel more responsive to the anti-inflammatory effects of anti-TNF therapy [ 21 ]. This preserved motility may indicate a less advanced disease state where the bowel's normal function is better maintained, thus responding more effectively to therapeutic interventions to reduce inflammation and prevent disease progression. The traditional sMaRIA biomarkers, which are focused on detecting transmural inflammatory changes, do not appear to discriminate the degree of bowel function in the setting of inflammatory strictures. Magnetic resonance imaging-based ileal motility quantification appears to be a promising tool for monitoring response to biologic therapy in CD. Studies have demonstrated that MRI-derived intestinal motility quantification (as a surrogate of intestinal peristalsis) improves over time in response to anti-inflammatory therapy. Dillman et al. demonstrated that intestinal motility is dynamic and shows early increases in response to biologic therapy in children and young adults with newly diagnosed ileal CD [ 9 ]. Similarly, Plumb et al. found that responders to anti-TNF therapy exhibited significantly more motility improvements than no responders, with a sensitivity of 93.1% and a specificity of 76.5% for detecting therapeutic response against a combined clinical endpoint [ 7 ]. These findings suggest that MRI-based ileal motility quantification can serve as a sensitive marker for monitoring the effectiveness of biologic therapy in managing CD strictures, potentially aiding in the early identification of nonresponse and allowing for personalized treatment adjustments. The clinical implications of our findings are significant. First, integrating quantitative motility assessment into routine MRE protocols could enhance the predictive accuracy of therapeutic responses, enabling clinicians to tailor treatments more effectively and potentially improve patient outcomes and reduce healthcare costs. Additionally, our results support the development of standardized motility assessment protocols and advanced imaging software and postprocessing techniques. These tools can provide objective and reproducible measures of bowel motility, facilitating their adoption in clinical practice and research. Importantly, bowel motility assessment does not require intravenous contrast or time-consuming diffusion-weighted imaging sequences, and the tool used here is clinically available and may be a helpful tool for patient identification in trials examining therapeutics in fibrostenosis disease. Despite the promising results, our study has several limitations. The generalizability of our findings may be limited due to the retrospective design and the relatively small sample size. More extensive prospective studies are necessary to validate these findings and establish standardized motility thresholds to predict treatment response. Furthermore, while we focused on pediatric and young adult populations, future research should explore whether these findings apply to older adults with CD who may have more advanced intestinal fibrosis. Investigating the interplay between motility and other emerging biomarkers, such as fecal calprotectin or serum cytokine profiles, could also provide a more comprehensive understanding of treatment response mechanisms. Conclusion In conclusion, our study demonstrates that quantitative assessment of ileal stricture motility using MRE is a superior predictor of therapeutic response compared to traditional MRI biomarkers in adolescents and young adults with stricturing CD. This novel imaging biomarker holds promise for enhancing the precision of CD management, paving the way for more targeted and effective therapeutic strategies. Declarations Conflicts of interests The authors have nothing to disclose. Author Contribution V.P.T: Conceptualization, methodology development, validation, statistical analysis, investigation, resources, data curation, writing – original draft, writing – review and editing. S.G.B: Conceptualization, methodology development, validation, statistical analysis, investigation, resources, writing – original draft, writing – review and editing. N.S.C.A: Investigation, writing – original draft, writing – review and editing. C.J.M: Conceptualization, methodology development, validation, resources, writing – review & editing, and study supervision. A.M: Validation, visualization, resources, writing – review & editing, and study supervision. M.S.G: Conceptualization, methodology development, resources, validation, writing – original draft, writing – review & editing, overall study supervision, and project administration. All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. 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PLoS One 13:e0190999. https://doi.org/10.1371/journal.pone.0190999 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2025 Read the published version in Pediatric Radiology → Version 1 posted Editorial decision: Revision requested 15 May, 2025 Reviews received at journal 08 May, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6306445","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442926714,"identity":"24c9c236-c6cf-4d54-b565-c692f263974c","order_by":0,"name":"Valeria Peña-Trujillo","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Peña-Trujillo","suffix":""},{"id":442926715,"identity":"09fc7667-7bfc-4dc9-816a-f340db08f7f4","order_by":1,"name":"Sebastian Gallo-Bernal","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Gallo-Bernal","suffix":""},{"id":442926716,"identity":"f6eb044b-7096-4a0e-afbf-203686f6d2ee","order_by":2,"name":"Christopher J. Moran","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"J.","lastName":"Moran","suffix":""},{"id":442926717,"identity":"f192cec4-b0b8-4149-a814-114368eecacd","order_by":3,"name":"Natalia Sofia Cortes Albornoz","email":"","orcid":"","institution":"Universidad del Rosario","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"Sofia Cortes","lastName":"Albornoz","suffix":""},{"id":442926718,"identity":"5e0c40c6-4e88-4d94-bc99-c3ce76c14a4a","order_by":4,"name":"Alex Menys","email":"","orcid":"","institution":"Motilent Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Menys","suffix":""},{"id":442926719,"identity":"0f31717e-b76c-494c-a474-41d490dca028","order_by":5,"name":"Michael S. Gee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYNCCCgYGAwYeKOcAA4MEYS1nSNbC2EaKFt3+4xcffJy3zW47A+/Bz5Vtdvl8B5gP3ubBo8XsRk6x4cxtt5N3NvAlS55tS7aceYAt2Rq/Fp40aV6gFoMDPAaSDWeYDYAMM2m8Ws6fSf/9dw5Yi/HPhjP1QC383/BrOZB+jJmx4bYdyHDJhorDIFvY8Gu5kcMs2XPsdoLBYR4zy4aK4waSh9mMLefgddjxhx9+1Ny2NzjeY3yzwaDagO9488Mbb/BoYWDgMQCRiQ3MMAFm3GqhgP0BiLQnqG4UjIJRMApGLgAApvZRqFPjhVAAAAAASUVORK5CYII=","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"S.","lastName":"Gee","suffix":""}],"badges":[],"createdAt":"2025-03-25 19:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6306445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6306445/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00247-025-06406-z","type":"published","date":"2025-11-12T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81023953,"identity":"7da15b50-1d89-4e69-ac71-06e4c118c9c4","added_by":"auto","created_at":"2025-04-21 10:09:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":861631,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Motility Scores in Responders and Non-Responders to Treatment: Representative MRI and GIQuant Analysis\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6306445/v1/2d80da005dcc699ecfcadf75.jpeg"},{"id":81023950,"identity":"90fee76b-712a-4de7-a9cb-4a5017cce297","added_by":"auto","created_at":"2025-04-21 10:09:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96199,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC Curve Performance of Motility vs. sMaRIA in Predicting Non-Response to Treatment\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6306445/v1/27cf6c88b1a4050f5cedd104.jpeg"},{"id":96105314,"identity":"c992d031-10fd-4a65-931a-d0c9a8ec0cc8","added_by":"auto","created_at":"2025-11-17 16:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6306445/v1/2079f48e-6764-4965-9a17-f566cc51dbd6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnetic Resonance Imaging-based ileal motility quantification predicts stricture response to biologic therapy in Crohn's disease.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOne of the most common complications of Crohn\u0026rsquo;s disease (CD) is the development of intestinal strictures, defined by bowel wall thickening, luminal narrowing, and proximal dilation as seen with cross-sectional imaging. These strictures can initially be inflammatory and responsive to therapy; however, with time, fibrosis and muscular hypertrophy may predominate, contributing to bowel obstruction and an increased risk of penetrating complications [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Differentiating between inflammatory and fibrotic components of strictures is critical for treatment planning, particularly as the latter often requires surgical intervention [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent studies suggest that magnetic resonance (MR) imaging has the potential to detect not only these fibrotic changes but also muscular hypertrophy, a growing area of interest in stricturing CD and essential to future drug development.\u003c/p\u003e \u003cp\u003eWhile biologic therapies, particularly anti-tumor necrosis factor (anti-TNF) agents, have revolutionized CD management by targeting specific inflammatory pathways, treatment outcomes remain highly variable and decrease over time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to Plumb et al., approximately 15\u0026ndash;30% of CD patients require anti-TNF agents, although more recent data from Kandavel et al. demonstrated the usage may now approach 60%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Concerningly, a significant proportion do not achieve a clinically meaningful response. About 10\u0026ndash;30% fail to respond initially, and an additional 20\u0026ndash;50% lose their response within the first year [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditional diagnostic approaches, including endoscopy and histology, provide critical insights into the structural and inflammatory status of the bowel but are invasive and often limited in accessing the stricture segment or extracting tissue from deeper bowel layers. Magnetic resonance enterography (MRE), a non-invasive imaging modality, offers a comprehensive assessment of the bowel and its complications, including strictures, and has emerged as a standard imaging tool in CD management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conventional MRI markers, such as wall thickness, contrast enhancement, and T2-weighted mural hyperintensity, are helpful in assessing disease severity through structural changes, but such measures often lack predictive power for treatment response, particularly in strictures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent advances in MR techniques have introduced the ability to quantify intestinal bowel wall motion (hereafter referred to as motility), offering insights into bowel function that complement structural changes and validate as a marker for disease activity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It has previously been shown that intestinal motility is reduced in strictures compared to morphologically normal bowel [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Beek et al. have demonstrated altered motility pre-stricture impacting symptoms with VanRein also advancing methodology for objective assessment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, no studies to date have performed a detailed analysis of strictures themselves, particularly in the context of motility and treatment response.\u003c/p\u003e \u003cp\u003eIn this study, we aim to comprehensively evaluate ileal strictures in children and young adults with CD undergoing biologic therapy, with a particular focus on motility. We hypothesize that quantitative motility assessment using MRE can serve as a non-invasive predictor of treatment response, with higher motility correlating with better outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation:\u003c/h2\u003e \u003cp\u003eThe cohort included all pediatric and young adult patients aged 10 to 28 years who had a diagnosis of CD complicated by imaging-confirmed ileal strictures during the study period of March 2017 to March 2022 and who were undergoing either initiation or dose escalation of anti-TNF agents (i.e., infliximab, adalimumab, or certolizumab). We then selected those patients who had changes in their medical regimen\u0026mdash;either through medication changes (e.g., switching to another type of biologic medication) or dose adjustments (e.g., increasing the dose by altering the frequency or amount of the medication)\u0026mdash;and who also had a clinically indicated MR enterography within a two-month timeframe from the medication change. Subjects with a history of intestinal surgery involving the removal of the terminal ileum were excluded due to the significant impact such surgeries could have on disease presentation and treatment outcomes. However, the use of concurrent anti-inflammatory treatments, like immunomodulators or corticosteroids, did not disqualify patients from participating in the study. This retrospective study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. It was approved by the (blinded) Human Research Committee.\u003c/p\u003e \u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable\u003c/p\u003e \u003cp\u003eThis study was conducted without any financial support.\u003c/p\u003e \u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003eMRI protocol:\u003c/div\u003e \u003cp\u003eAll participants underwent clinically indicated MRI examinations of the small bowel using a 1.5-Tesla (Artist, General Electric Healthcare, Waukesha, WI) or 3.0-Tesla (Prisma, Siemens Healthineers, Malvern, PA) clinical MR scanner with multichannel phased array torso coil. These examinations were conducted with subjects awake and without sedation. The imaging protocol included standardized anatomical and quantitative acquisitions and dynamic cine imaging. This study focused on analyzing cinematic images obtained in the coronal plane using a 2D balanced steady-state free precession (SSFP) sequence. Additionally, anatomic images were acquired in both axial and coronal planes utilizing T2-weighted single-shot fast spin-echo (ssT2W) sequences without fat saturation. Oral contrast (Breeza, Beekley Medical, Bristol, CT or MiraLAX (polyethene glycol 3350)) was administered following our institutional weight-based protocol.\u003c/p\u003e\n\u003ch3\u003eImage processing and analyses:\u003c/h3\u003e\n\u003cp\u003eAll functional and morphological analyses used as predictors in this study were conducted using FDA-cleared software GIQuant (Motilent, London, UK). The imaging-derived predictors included the Simplified MR Index of Activity (sMaRIA) score, an imaging-based metric designed to assess and quantify disease activity in CD by evaluating parameters such as bowel wall thickness, relative contrast enhancement, and presence of edema or ulceration[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Using different Motilent tools, we also measured the length of the inflamed bowel, stricture volume, wall thickness, average T2-weighted signal intensity, and mean motility of strictures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All measures were made on the image analysis platform Entrolytics (Motilent, London, UK)\u003c/p\u003e \u003cp\u003eInitially, T2-weighted coronal ssT2W images were analyzed by a fellowship-trained abdominal and pediatric radiologist with over 15 years of experience. The radiologist identified and localized the largest segment of ileal stricture and assessed the terminal ileum, defined as the distal 15-cm segment, to calculate the sMaRIA score. The length of the inflamed bowel segment was measured using a custom-built tool designed for precise linear measurements. This tool allowed the placement of multiple points along the affected intestinal lumen, which were then automatically interpolated to form a detailed centerline accurately representing the length of the inflamed segment.\u003c/p\u003e \u003cp\u003eSubsequently, semi-automated volumetric segmentations of the inflamed ileum were performed using a specialized software tool GISeg, (Motilent, London, UK). This tool produced a three-dimensional bowel model from the 2D MRI slices, providing a volume score in cubic centimeters (cm\u0026sup3;). The segmentation process started with the manually created centerline of the ileum. The software applied an unbiased k-means clustering technique to divide the image into contiguous segments. Each segment was characterized by a custom feature set and analyzed using a random forest regressor to estimate the likelihood of each segment being part of the inflamed ileal wall. To calculate the mean bowel wall thickness, Distance Transform morphological operations were applied to the bowel wall mask to measure thickness at various points along the mask. These measurements were averaged across all central pixels within the mask to provide a mean thickness for the bowel wall in the inflamed segment.\u003c/p\u003e \u003cp\u003eFor motility analysis, de-identified coronal cine images were reviewed by the primary operator. A polygon region of interest (ROI) was placed around the affected bowel segment to quantify intestinal motility at the stricture site. An FDA cleared algorithm (GIQuant) was used to produce quantitative motility maps using a previously described method based on image registration. The mean pixel value from these maps was reported as a numerical motility score in arbitrary units (AU). Scores below 150 AU indicated reduced motility (hypokinesis or akinesis), while scores above 300 AU reflected normal motility. These scores were visualized through heatmaps, providing a comprehensive view of bowel motion dynamics and facilitating a thorough assessment of intestinal motility.\u003c/p\u003e\n\u003ch3\u003eClinical Outcomes: Responders vs. Non-Responders\u003c/h3\u003e\n\u003cp\u003eSystematic electronic medical record reviews were conducted to evaluate clinical responses and disease activity up to six months following the initiation or adjustment of biological therapy. Patients were categorized into two groups based on their treatment response, as documented in their electronic medical records. Non-response was defined as increased disease activity, discontinuation of anti-TNF therapy due to ineffectiveness, or the need for surgical interventions.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eQualitative variables were reported as frequencies and percentages, while quantitative variables were expressed as means with standard deviations. Nonparametric tests were used for continuous variables to compare sociodemographic and clinical characteristics between responders and non-responders, and chi-square (χ\u0026sup2;) tests were applied for categorical variables. The association between clinical and MRE-derived variables and treatment outcomes was evaluated using multivariable logistic regression analysis. This model was developed using a forward selection method to systematically identify significant predictors. We chose the forward selection method because, given our study's limited number of positive outcomes, it helps avoid overfitting by incrementally including only the most relevant predictors.\u003c/p\u003e \u003cp\u003eIn a post-hoc analysis, receiver operating characteristic (ROC) curve analyses were conducted to compare the predictive performance of motility and the sMaRIA score for treatment response. The area under the curve (AUC) was calculated to assess and compare the discriminative power of these two predictors. All tests were two-sided, and the significance level was set to p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Correction for multiple comparisons was performed using the Bonferroni method. All statistical analyses were performed using R software version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGeneral population\u003c/h2\u003e \u003cp\u003eA total of 40 subjects (52.5% female; mean age 19.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13 years) were included in the analysis, with 15 subjects (37.5%) classified as non-responders.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResponders versus non-responders\u003c/h3\u003e\n\u003cp\u003eThere were no statistically significant differences in age (responders: 20.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92 years vs. non-responders: 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59 years; p\u0026thinsp;=\u0026thinsp;0.603) or gender (responders: 53.3% male vs. non-responders: 52% male; p\u0026thinsp;=\u0026thinsp;0.935) between the two groups. There was no difference between groups in terms of sMaRIA score p\u0026thinsp;=\u0026thinsp;0.355, stricture length (9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73 vs. 9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75 cm; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), wall thickness (6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2 vs. 6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56 mm; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), stricture volume (21.14\u0026thinsp;\u0026plusmn;\u0026thinsp;12 vs. 30.13\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and average T2-weighted signal intensity (363.12\u0026thinsp;\u0026plusmn;\u0026thinsp;340.79 vs. 255.073\u0026thinsp;\u0026plusmn;\u0026thinsp;229.8; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). There were differences in mean stricture motility (176.03\u0026thinsp;\u0026plusmn;\u0026thinsp;128.63 vs. 67.83\u0026thinsp;\u0026plusmn;\u0026thinsp;33.88; p\u0026thinsp;=\u0026thinsp;0.006) and its estimated standard deviation (72.06\u0026thinsp;\u0026plusmn;\u0026thinsp;55.55 vs. 30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;25.79; p\u0026thinsp;=\u0026thinsp;0.017) between responders and non-responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analysis\u003c/h2\u003e \u003cp\u003eOn multivariate analysis adjusted for all covariates, only the variables selected by the forward selection method were included in the final model. The analysis revealed that patients with higher mean motility showed higher odds of being responders, with an odds ratio of 1.020 (CI, 1.001\u0026ndash;1.038; P\u0026thinsp;=\u0026thinsp;0.039). No additional statistically significant associations were found. The results of the multivariate analysis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWall thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotility mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotility standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esMaRIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eROC Curve analysis\u003c/h2\u003e \u003cp\u003eROC curves were fitted, and the area under the curve (AUC) of each test was estimated. Motility performed better than sMaRIA as a predictor of no response to treatment. The AUC for Motility was 0.812, indicating good predictive ability, while the AUC for sMaRIA was 0.613, showing lower performance in predicting treatment response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was a trend toward better performance with Motility when compared to the sMaRIA though this difference did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to characterize ileal strictures in patients with CD by examining bowel wall motion in those receiving biological agents and comparing its predictive value to several additional MRE-derived variables for determining treatment outcomes in stricturing CD. Our results demonstrate that higher mean motility scores within an intestinal stricture are significantly associated with positive treatment outcomes. This suggests that motility metrics, reflecting the functional status of the bowel, can serve as predictors of therapeutic success in pediatric and young adult patients with stricturing CD undergoing treatment with anti-TNF therapy.\u003c/p\u003e \u003cp\u003eWhile traditional MRI biomarkers have been widely used, their predictive value in this context appears to be limited [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, traditional anatomical measures, such as stricture length, wall thickness, and the Simplified MR Index of Activity (sMaRIA) score, did not show significant predictive value in this study. The sMaRIA score, in particular, demonstrated a lower area under the ROC curve (AUC\u0026thinsp;=\u0026thinsp;0.613) compared to motility (AUC\u0026thinsp;=\u0026thinsp;0.812), indicating lower effectiveness in predicting treatment response. These findings underscore the potential of incorporating dynamic functional assessments like motility into the evaluation and management of CD. It is likely that the more traditional morphologic MR biomarkers, which are sensitive to transmural inflammatory changes, do not have sufficient ability to evaluate the reversibility of active inflammation before the onset of biologic therapy. Non-invasive imaging biomarkers such as these could significantly enhance the personalization of treatment strategies, allowing for more accurate prediction of patient outcomes and timely adjustments to therapy.\u003c/p\u003e \u003cp\u003eOne hypothesis for why patients with higher motility respond better to treatment is that increased motility suggests less fibrosis and less smooth muscle hypertrophy in the affected bowel segments [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Fibrosis can lead to rigid and non-compliant bowel segments, reducing their ability to respond dynamically to inflammation and medical treatment [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, severe and chronic inflammation can cause irreversible neuroendocrine damage, impairing the bowel's motility and ability to function normally [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Patients with preserved or higher motility likely have less structural and functional damage, making their bowel more responsive to the anti-inflammatory effects of anti-TNF therapy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This preserved motility may indicate a less advanced disease state where the bowel's normal function is better maintained, thus responding more effectively to therapeutic interventions to reduce inflammation and prevent disease progression. The traditional sMaRIA biomarkers, which are focused on detecting transmural inflammatory changes, do not appear to discriminate the degree of bowel function in the setting of inflammatory strictures.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging-based ileal motility quantification appears to be a promising tool for monitoring response to biologic therapy in CD. Studies have demonstrated that MRI-derived intestinal motility quantification (as a surrogate of intestinal peristalsis) improves over time in response to anti-inflammatory therapy. Dillman et al. demonstrated that intestinal motility is dynamic and shows early increases in response to biologic therapy in children and young adults with newly diagnosed ileal CD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, Plumb et al. found that responders to anti-TNF therapy exhibited significantly more motility improvements than no responders, with a sensitivity of 93.1% and a specificity of 76.5% for detecting therapeutic response against a combined clinical endpoint [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings suggest that MRI-based ileal motility quantification can serve as a sensitive marker for monitoring the effectiveness of biologic therapy in managing CD strictures, potentially aiding in the early identification of nonresponse and allowing for personalized treatment adjustments.\u003c/p\u003e \u003cp\u003eThe clinical implications of our findings are significant. First, integrating quantitative motility assessment into routine MRE protocols could enhance the predictive accuracy of therapeutic responses, enabling clinicians to tailor treatments more effectively and potentially improve patient outcomes and reduce healthcare costs. Additionally, our results support the development of standardized motility assessment protocols and advanced imaging software and postprocessing techniques. These tools can provide objective and reproducible measures of bowel motility, facilitating their adoption in clinical practice and research. Importantly, bowel motility assessment does not require intravenous contrast or time-consuming diffusion-weighted imaging sequences, and the tool used here is clinically available and may be a helpful tool for patient identification in trials examining therapeutics in fibrostenosis disease.\u003c/p\u003e \u003cp\u003eDespite the promising results, our study has several limitations. The generalizability of our findings may be limited due to the retrospective design and the relatively small sample size. More extensive prospective studies are necessary to validate these findings and establish standardized motility thresholds to predict treatment response. Furthermore, while we focused on pediatric and young adult populations, future research should explore whether these findings apply to older adults with CD who may have more advanced intestinal fibrosis. Investigating the interplay between motility and other emerging biomarkers, such as fecal calprotectin or serum cytokine profiles, could also provide a more comprehensive understanding of treatment response mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates that quantitative assessment of ileal stricture motility using MRE is a superior predictor of therapeutic response compared to traditional MRI biomarkers in adolescents and young adults with stricturing CD. This novel imaging biomarker holds promise for enhancing the precision of CD management, paving the way for more targeted and effective therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interests\u003c/h2\u003e \u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eV.P.T: Conceptualization, methodology development, validation, statistical analysis, investigation, resources, data curation, writing \u0026ndash; original draft, writing \u0026ndash; review and editing. S.G.B: Conceptualization, methodology development, validation, statistical analysis, investigation, resources, writing \u0026ndash; original draft, writing \u0026ndash; review and editing. N.S.C.A: Investigation, writing \u0026ndash; original draft, writing \u0026ndash; review and editing. C.J.M: Conceptualization, methodology development, validation, resources, writing \u0026ndash; review \u0026amp; editing, and study supervision. A.M: Validation, visualization, resources, writing \u0026ndash; review \u0026amp; editing, and study supervision. M.S.G: Conceptualization, methodology development, resources, validation, writing \u0026ndash; original draft, writing \u0026ndash; review \u0026amp; editing, overall study supervision, and project administration. All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRimola J, Beek KJ, Ord\u0026aacute;s I, et al (2024) Contemporary Imaging Assessment of Strictures and Fibrosis in Crohn Disease, With Focus on Quantitative Biomarkers: From the \u003cem\u003eAJR\u003c/em\u003e Special Series on Imaging of Fibrosis. American Journal of Roentgenology 222. https://doi.org/10.2214/AJR.23.29693\u003c/li\u003e\n\u003cli\u003eGauci J, Sammut L, Sciberras M, et al (2018) Small bowel imaging in Crohn\u0026rsquo;s disease patients. Ann Gastroenterol. https://doi.org/10.20524/aog.2018.0268\u003c/li\u003e\n\u003cli\u003eRieder F, Mukherjee PK, Massey WJ, et al (2024) Fibrosis in IBD: from pathogenesis to therapeutic targets. Gut 73:854\u0026ndash;866. https://doi.org/10.1136/gutjnl-2023-329963\u003c/li\u003e\n\u003cli\u003eRieder F, Fiocchi C, Rogler G (2017) Mechanisms, Management, and Treatment of Fibrosis in Patients With Inflammatory Bowel Diseases. Gastroenterology 152:340-350.e6. https://doi.org/10.1053/j.gastro.2016.09.047\u003c/li\u003e\n\u003cli\u003eDretzke J, Edlin R, Round J, et al (2011) A systematic review and economic evaluation of the use of tumour necrosis factor-alpha (TNF-\u0026alpha;) inhibitors, adalimumab and infliximab, for Crohn\u0026rsquo;s disease. Health Technol Assess (Rockv) 15:. https://doi.org/10.3310/hta15060\u003c/li\u003e\n\u003cli\u003eKandavel P, Eder SJ, Adler J (2021) Reduced Systemic Corticosteroid Use among Pediatric Patients With Inflammatory Bowel Disease in a Large Learning Health System. J Pediatr Gastroenterol Nutr 73:345\u0026ndash;351. https://doi.org/10.1097/MPG.0000000000003182\u003c/li\u003e\n\u003cli\u003ePlumb AA, Menys A, Russo E, et al (2015) Magnetic resonance imaging‐quantified small bowel motility is a sensitive marker of response to medical therapy in Crohn\u0026rsquo;s disease. Aliment Pharmacol Ther 42:343\u0026ndash;355. https://doi.org/10.1111/apt.13275\u003c/li\u003e\n\u003cli\u003ePanes J, Bouhnik Y, Reinisch W, et al (2013) Imaging techniques for assessment of inflammatory bowel disease: Joint ECCO and ESGAR evidence-based consensus guidelines. J Crohns Colitis 7:556\u0026ndash;585. https://doi.org/10.1016/j.crohns.2013.02.020\u003c/li\u003e\n\u003cli\u003eDillman JR, Tkach JA, Imbus R, et al (2022) MRI-Based Characterization of Intestinal Motility in Children and Young Adults With Newly Diagnosed Ileal Crohn Disease Treated by Biologic Therapy: A Controlled Prospective Study. American Journal of Roentgenology 219:655\u0026ndash;664. https://doi.org/10.2214/AJR.22.27792\u003c/li\u003e\n\u003cli\u003eVan Assche G, Herrmann KA, Louis E, et al (2013) Effects of infliximab therapy on transmural lesions as assessed by magnetic resonance enteroclysis in patients with ileal Crohn\u0026rsquo;s disease. J Crohns Colitis 7:950\u0026ndash;957. https://doi.org/10.1016/j.crohns.2013.01.011\u003c/li\u003e\n\u003cli\u003eMenys A, Puylaert C, Tutein Nolthenius CE, et al (2018) Quantified Terminal Ileal Motility during MR Enterography as a Biomarker of Crohn Disease Activity: Prospective Multi-Institution Study. Radiology 289:428\u0026ndash;435. https://doi.org/10.1148/radiol.2018180100\u003c/li\u003e\n\u003cli\u003eMenys A, Helbren E, Makanyanga J, et al (2013) Small bowel strictures in Crohn\u0026rsquo;s disease: a quantitative investigation of intestinal motility using MR enterography. Neurogastroenterology \u0026amp; Motility 25:967. https://doi.org/10.1111/nmo.12229\u003c/li\u003e\n\u003cli\u003eGollifer RM, Menys A, Makanyanga J, et al (2018) Relationship between MRI quantified small bowel motility and abdominal symptoms in Crohn\u0026rsquo;s disease patients\u0026mdash;a validation study. Br J Radiol 20170914. https://doi.org/10.1259/bjr.20170914\u003c/li\u003e\n\u003cli\u003eRoseira J, Ventosa AR, de Sousa HT, Brito J (2020) The new simplified MARIA score applies beyond clinical trials: A suitable clinical practice tool for Crohn\u0026rsquo;s disease that parallels a simple endoscopic index and fecal calprotectin. United European Gastroenterol J 8:1208\u0026ndash;1216. https://doi.org/10.1177/2050640620943089\u003c/li\u003e\n\u003cli\u003eMignini I, Maresca R, Ainora ME, et al (2023) Predicting Treatment Response in Inflammatory Bowel Diseases: Cross-Sectional Imaging Markers. J Clin Med 12:5933. https://doi.org/10.3390/jcm12185933\u003c/li\u003e\n\u003cli\u003eSeveri C, Sferra R, Scirocco A, et al (2014) Contribution of intestinal smooth muscle to Crohn\u0026rsquo;s disease fibrogenesis. European Journal of Histochemistry. https://doi.org/10.4081/ejh.2014.2457\u003c/li\u003e\n\u003cli\u003eAlfredsson J, Wick MJ (2020) Mechanism of fibrosis and stricture formation in Crohn\u0026rsquo;s disease. Scand J Immunol 92:. https://doi.org/10.1111/sji.12990\u003c/li\u003e\n\u003cli\u003eChen W, Lu C, Hirota C, et al (2017) Smooth Muscle Hyperplasia/Hypertrophy is the Most Prominent Histological Change in Crohn\u0026rsquo;s Fibrostenosing Bowel Strictures: A Semiquantitative Analysis by Using a Novel Histological Grading Scheme. J Crohns Colitis 11:92\u0026ndash;104. https://doi.org/10.1093/ecco-jcc/jjw126\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Alessio S, Ungaro F, Noviello D, et al (2022) Revisiting fibrosis in inflammatory bowel disease: the gut thickens. Nat Rev Gastroenterol Hepatol 19:169\u0026ndash;184. https://doi.org/10.1038/s41575-021-00543-0\u003c/li\u003e\n\u003cli\u003eEngel T, Ben-Horin S, Beer-Gabel M (2015) Autonomic Dysfunction Correlates with Clinical and Inflammatory Activity in Patients with Crohnʼs Disease. Inflamm Bowel Dis 1. https://doi.org/10.1097/MIB.0000000000000508\u003c/li\u003e\n\u003cli\u003ede Bruyn JR, Becker MA, Steenkamer J, et al (2018) Intestinal fibrosis is associated with lack of response to Infliximab therapy in Crohn\u0026rsquo;s disease. PLoS One 13:e0190999. https://doi.org/10.1371/journal.pone.0190999\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biomarkers, Imaging, Treatment response, Crohn’s disease, Stricture","lastPublishedDoi":"10.21203/rs.3.rs-6306445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6306445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eStrictures are a common feature of Crohn\u0026rsquo;s disease (CD), impairing bowel function, complicating treatment, and often leading to surgery. Predicting which lesions are responsive to medical therapy and which will become obstructive represents an important clinical goal. We aim to characterize ileal strictures in patients with CD by exploring the bowel wall motility in patients receiving biologic agents and evaluate motility\u0026rsquo;s potential role in predicting therapeutic response compared with standard, transmural MRI biomarkers.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis retrospective study included subjects aged 10\u0026ndash;28 with stricturing CD who underwent initiation or escalation of biologic therapy and clinically indicated MRE within a two-month window. Clinical response and disease activity were evaluated up to six months after therapy initiation or adjustment. Subjects were categorized as \"non-responders\" if they experienced worsening disease activity, required discontinuation of therapy, or needed surgical intervention. Simplified MR indexes of activity (sMaRIA) assessed the terminal ileum, and motility data were processed using GIQuant software. Sociodemographic and clinical variables were compared between responders and non-responders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 40 subjects (52.5% female; mean age 19.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13) were included, with 15 (37.5%) classified as non-responders. No significant differences were observed in age, gender, stricture length, wall thickness, stricture volume, or T2-weighted signal intensity. However, mean motility (176.03\u0026thinsp;\u0026plusmn;\u0026thinsp;128.63 vs. 67.83\u0026thinsp;\u0026plusmn;\u0026thinsp;33.88; p\u0026thinsp;=\u0026thinsp;0.006) and its standard deviation (72.06\u0026thinsp;\u0026plusmn;\u0026thinsp;55.55 vs. 30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;25.79; p\u0026thinsp;=\u0026thinsp;0.017) were significantly higher in responders. Motility outperformed sMaRIA (AUC 0.812 vs. 0.613; p\u0026thinsp;=\u0026thinsp;0.07) in predicting response.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eQuantitative MRI motility assessment was superior to traditional MRI biomarkers in predicting response to biologic therapy in adolescents and young adults with stricturing CD.\u003c/p\u003e","manuscriptTitle":"Magnetic Resonance Imaging-based ileal motility quantification predicts stricture response to biologic therapy in Crohn's disease.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 10:09:51","doi":"10.21203/rs.3.rs-6306445/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-15T20:07:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T01:11:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T15:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221738907856806329246729130289172292973","date":"2025-04-14T21:58:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157979632669163585064177381712889015805","date":"2025-04-01T18:07:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T13:34:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T02:39:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T02:38:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Radiology","date":"2025-03-25T19:14:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7eb810f9-2e4d-4f9e-8f50-cbc3b930dd13","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:06:39+00:00","versionOfRecord":{"articleIdentity":"rs-6306445","link":"https://doi.org/10.1007/s00247-025-06406-z","journal":{"identity":"pediatric-radiology","isVorOnly":false,"title":"Pediatric Radiology"},"publishedOn":"2025-11-12 15:58:39","publishedOnDateReadable":"November 12th, 2025"},"versionCreatedAt":"2025-04-21 10:09:51","video":"","vorDoi":"10.1007/s00247-025-06406-z","vorDoiUrl":"https://doi.org/10.1007/s00247-025-06406-z","workflowStages":[]},"version":"v1","identity":"rs-6306445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6306445","identity":"rs-6306445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}