Magnetic Resonance Imaging for Distinguishing Perianal Hidradenitis Suppurativa from Fistulizing Crohn Disease
Bibliographic record
Abstract
INTRODUCTION: Both hidradenitis suppurativa (HS) and Crohn disease (CD) are characterized by suppuration and granulomatous inflammation, which may result in formation of tunnels, sinus tracts, or fistulas. We sought to determine the differences in characteristics of fistulizing perianal CD vs. perianal HS on magnetic resonance imaging (MRI). METHODS: This retrospective cohort study included patients diagnosed with CD, HS, or both diseases who underwent medical examination at the Mayo Clinic between January 1, 1998, and July 31, 2021. We further selected patients who had documented perianal lesions and underwent pelvic MRI. Two abdominal radiologists blinded to clinical diagnosis reviewed the MR images for the presence or absence of 10 characteristics. For characteristics that differed significantly between patients with CD and HS, odds ratios were calculated. A radiomics-based MRI scoring system was constructed according to the odds ratios and was validated. RESULTS: On MRI, significantly more patients in the HS group (n = 49) than the CD group (n = 74) had subcutaneous tunnels (35% vs. 4%; p < 0.001), soft-tissue inflammation of subcutaneous tissue and skin (35% vs. 5%; p < 0.001), and inguinal lymphadenopathy (65% vs. 45%; p = 0.02). Significantly more patients in the CD group than the HS group had transsphincteric fistula (64% vs. 35%; p = 0.002), intersphincteric fistula (58% vs. 29%; p = 0.001), mesorectal lymphadenopathy (26% vs. 4%; p = 0.002), and rectal inflammation (32% vs. 12%; p = 0.01). The proposed MRI scoring system for distinguishing HS from CD had a sensitivity of 0.77, specificity of 0.80, and area under the curve of 0.84. CONCLUSION: MRI may be a valuable tool in distinguishing perianal draining tunnels in HS from fistulizing perianal CD. The MRI scoring system we created could help in the clinical decision-making process. Combining MRI with other investigational and clinical findings may improve diagnostic accuracy for these challenging perianal diseases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".