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Record W4400389640 · doi:10.1002/ueg2.12619

Seeing the whole picture: Inflammatory bowel disease complications and extraintestinal manifestations on cross‐sectional imaging

2024· editorial· en· W4400389640 on OpenAlexaboutno aff
María Manuela Estevinho, Nurulamin M Noor

Bibliographic record

VenueUnited European Gastroenterology Journal · 2024
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
FundersNIHR Cambridge Biomedical Research CentreDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMedicineInflammatory bowel diseasePrimary sclerosing cholangitisFistulaMagnetic resonance imagingRadiologyCrohn's diseaseRetrospective cohort studyDiseaseCohortGastroenterologyUltrasoundUlcerative colitisInternal medicine

Abstract

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Computed tomography (CT) and magnetic resonance (MR) enterography techniques are widely used for detection and monitoring of intestinal complications in inflammatory bowel disease (IBD). According to international recommendations, patients newly diagnosed with Crohn's disease (CD) and those with symptomatic small bowel disease should undergo small bowel assessment ideally using MR enterography, capsule endoscopy, or intestinal ultrasound (IUS).1 While the diagnostic yield of the three techniques is similar, cross-sectional tools such as CT imaging (involving ionizing radiation) should be reserved for those with a suspicion of more urgent pathology such as obstructive or fistulizing disease.2 Although commonly used for luminal indications in IBD, the use of cross-sectional imaging specifically for identifying extraintestinal manifestations (EIMs) has been less explored.3, 4 In a real-world retrospective study, Vuyyuru et al. analyzed the prevalence of transmural complications (stricture/fistula) and the incidental finding of EIMs in patients with IBD who underwent CT or MR enterography over 9 years at two Canadian centers.5 The study included over 550 IBD patients, 91% of whom had CD, with a median disease duration of 11 years. In this cohort, transmural imaging identified a B2 (stricturing) or B3 (fistulizing) phenotype in more than 40% of individuals. The overall prevalence of EIMs was 25%, with one-third in patients previously undiagnosed at the time of the enterography. Among those newly diagnosed with EIMs (n = 41), the most common were cholelithiasis (63%), followed by sacroiliitis (24%) and primary sclerosing cholangitis (5%). These results corroborate current international guidance, highlighting the importance of cross-sectional imaging to identify transmural complications. Additionally, this study emphasizes the need for vigilance and of the possibility to use cross-sectional imaging tools to help identify EIMs. It is important to note that for some patients the presence of EIMs may be earlier in development or asymptomatic. This may explain why there was a relatively large proportion of patients with newly diagnosed EIMs, despite a median disease duration of 11 years. In this regard, the authors demonstrate promise for cross-sectional imaging to detect even subclinical EIMs. More timely detection and diagnosis of EIMs may be crucial for some diseases such as primary sclerosing cholangitis due to its association with malignancy and need for surveillance. Moreover, earlier detection may also enable better understanding of the burden of EIMs and help guide treatment selection.6 For example, earlier introduction of biologic therapy might be considered in a patient with only mild luminal IBD but who has concomitant sacroiliitis. Although this study provides novel and important insights in a real-world setting, there remain some unanswered questions. First, selection of the most “appropriate or optimal” cross-sectional imaging modality remains a challenge. With increasing availability of IUS, it may be more difficult to justify use of CT or MR for first-line cross-sectional imaging. There are multiple potential benefits of IUS including being:less expensive, faster to perform, well tolerated for patients, with no ionizing radiation risk as well as more recently being validated to identify B2 and B3 phenotypes. However, this should be balanced with an awareness that MR in particular has been reported to have a higher specificity than IUS for small bowel disease extent, to detect deep-seated or pelvic fistulas, as well as abscesses.7, 8 Moreover, it is unknown whether IUS could also help identify EIMs with the same level as demonstrated by CT and MR imaging in this study. Second, the inter-observer agreement for identifying and grading EIMs remains unclear, with a possibility for more incidental or non-diagnostic findings if all cross-sectional imaging were to routinely report on presence or absence of EIMs. Third, it is not clear if the additional time and acquisition costs for specific sequences dedicated for EIMs should be routinely included for all patients with IBD or just those who have specific symptoms, signs, or investigation results to warrant additional assessment. Fourth and linked to the previous point, the prognostic impact of subclinical EIMs identified on cross-sectional imaging remains unknown. For example, while it is plausible that active joint inflammation would associate with IBD and later outcomes, the associations with other EIMs such as those of the biliary tract may not be so clear.9 It is important to note that prior studies have demonstrated high levels of complicated disease on cross-sectional imaging even at diagnosis.10 In line with these previous findings, use of cross-sectional imaging earlier in the disease course may provide crucial information on both IBD phenotype and the presence of EIMs. Performing such a detailed initial assessment could help better guide management decisions, potentially reducing future disability and improving quality of life for patients. Maria Manuela Estevinho wrote the initial manuscript draft. Nurulamin M. Noor provided critical input. Maria Manuela Estevinho and Nurulamin M. Noor approved the final version of the manuscript. NMN is supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. NMN has received personal fees from BMS, Galapagos, Janssen, Lilly, SBK Healthcare, Takeda outside the submitted work and grants from Celltrion, Dr Falk, Pfizer, Pharmacosmos, Tillotts Pharma outside the submitted work. NIHR Cambridge Biomedical Research Centre, Grant/Award Number: NIHR203312 Data sharing is not applicable to this article as no new data were created or analyzed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.248
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes1
Has abstractyes

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