S1044 Correlation and Assessment of Small Bowel Lesions Using Cross-Sectional Imaging Techniques vs Small Intestinal Contrast UltraSonography in Known Crohn’s Disease (CACTUS-CD Trial): A Paired, Validating Study (NCT06125678)
Notice bibliographique
Résumé
Introduction: Magnetic resonance/computed tomography enterography (MRE/CTE) are established for assessing small bowel (SB) Crohn’s disease (CD) with superior accuracy compared to intestinal ultrasound (IUS). However, the role of small intestinal contrast ultrasound (SICUS) in monitoring CD activity needs further exploration. Methods: This study evaluated SICUS in comparison to CTE/MRE for monitoring known SB CD activity. Patients (age 18-75) with established SB CD underwent SICUS prior to CTE (n=75)/MRE (n=39). Accuracy of SICUS for detecting SB disease presence, extent, maximum bowel wall thickness (BWT), length of involvement, and complications (strictures, fistulas), and its impact on management were assessed against MRE/CTE. Results: A total of 111 patients (median age 35 years, 56% male) were included. SICUS showed sensitivity/specificity of 95.2%/100%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 58.3%, and accuracy of 95.5% for detecting SB disease. For disease extent, SICUS had sensitivity/specificity of 87.4%/87.5%, PPV 98.9%, NPV 35%, and accuracy 87.4%. Sensitivity/specificity for detecting strictures were 76.5% (58.8% with IUS alone)/98.3%, PPV 97.5%, NPV 83.1%, and accuracy 88.3%; for fistulas, 80%/99%, PPV 80%, NPV 99%, and accuracy 98.2%. SICUS correlated strongly with cross-sectional imaging for BWT (Spearman’s R=0.723, P < 0.001) and length of involvement (R=0.834, P < 0.001). Missed lesions were primarily in proximal and mid SB. Overall, management changed in 17% (n=19) after CTE/MRE. Conclusion: SICUS accurately identifies SB CD activity, extent, and complications, with limited impact on management decisions compared to cross-sectional imaging. It is particularly beneficial for detecting SB strictures. Cross-sectional imaging remains valuable for proximal and mid SB involvement. (Clinicaltrials.gov: NCT06125678) (see Figure 1, Table 1).Figure 1.: Correlation of cross sectional imaging (CTE-computed tomography enterography/MRE: magnetic resonance enterography) with intestinal ultrasound : maximum bowel wall thickness measurement (A) and correlation (B); length of involvement (C) and correlation (D). Table 1. - Baseline characteristics and key results of the study Baseline characteristics Total number of patients n=111 Male 62 (56%) Age (median/range, years) 35 (18-70) Disease location (Montreal classification), n(%) Terminal ileal disease (L1) 8 (7.2%) Ileo-colonic disease (L3) 20 (18.1%) Isolated SB disease (L4b) 38 (34.2%) Multiple segments involved 45 (40.5%) Disease behaviour (Montreal classification), n (%) B1 42 (37.8%) B2 59 (53.2%) B3 5 (4.5%) B2+B3 5 (4.5%) Perianal disease modifier 10 (9%) Previous small bowel resection, n (%) 14 (12.6%) Therapy, n (%) Azathioprine 73 (65.8%) Methotrexate 1 (0.9%) Steroid (prednisolone/budesonide) 39 (35.1%) 5 amino-salicylic acid (5-ASA) 12 (10.8%) Infliximab 12 (10.8%) Adalimumab 9 (8.1%) Vedolizumab 1 (0.9%) Ustekinumab 3 (2.7%) Results Change in management after cross-sectional imaging over IUS, n (%) 19 (17%) IUS guided management correlated with management after cross-sectional imaging 92 (83%) Accuracy of SICUS to identify active disease, n (%) Sensitivity: 95.2% Specificity: 100% PPV: 100% NPV: 58.3% Accuracy: 95.5% SICUS correctly identified extent of SB disease, n (%) Sensitivity: 87.4% Specificity: 87.5% PPV: 98.9% NPV: 35% Accuracy: 87.4% SICUS correlated with CTE/MRE for absence/presence of strictures, n (%) Sensitivity: 76.5% (58.8% IUS)Specificity: 98.3%PPV: 97.3%NPV: 79.7%Accuracy: 85.6% SICUS correctly identified extent of SB strictures, n (%) Sensitivity: 70.6% (51% IUS) Specificity: 98.3% PPV: 97.5% NPV: 83.1% Accuracy: 88.3% SICUS correlated with CTE/MRE for absence/presence of intra-abdominal fistula, n (%) Sensitivity: 80% Specificity: 99% PPV:80% NPV: 99% Accuracy: 98.2%
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».