Clinical-stage Approaches for Imaging Chronic Inflammation and Fibrosis in Crohn’s Disease
Bibliographic record
Abstract
The number of imaging-based indices developed for inflammatory bowel disease as research tools, objectively measuring ileocolonic and perianal activity and treatment response, has expanded in the past 2 decades. Created primarily to assess Crohn's disease (CD), there is increasing adoption of these indices into the clinical realm to guide patient care. This translation has been facilitated by validation in adult and pediatric populations, prompted by simplification of score calculations needed for practical application outside the research environment. The majority of these indices utilize magnetic resonance imaging (MRI), specifically MR enterography (MRE) and pelvic MRI, and more recently ultrasound. This review explores validated indices by modality, anatomic site and indication, including for documentation of the presence and extent of CD, disease progression, complications, and treatment response, highlighting those in clinical use or with the potential to be. As well, it details index imaging features used to quantify chronic inflammatory activity, severity, and to lesser extent fibrosis, in addition to their reference standards and any modifications. Validation in the pediatric population of indices primarily developed in adult cohorts such as the Magnetic Resonance Index of Activity (MaRIA), the Simplified Magnetic Resonance Index of Activity (MARIAs), and the MRE global score (MEGS), together with newly developed pediatric-specific indices, are discussed. Indices that may be predictive of disease course and investigational techniques with the potential to provide future imaging biomarkers, such as multiparametric MRI, are also briefly considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".