Preliminary data from a multicenter Italian study: use of EUS-Elastography and Contrast in the differential diagnosis of SELs
Notice bibliographique
Résumé
Aims Distinguishing gastrointestinal subepithelial lesions (GI-SELs) poses a clinical challenge because Endoscopic Ultrasound (EUS) is adept at detecting them but may fall short in providing effective differentiation. New methodologies have been introduced which provide further details and potential prognostic information. Elastography (EUS-E) allows to carry out a qualitative and semi-quantitative assessment of tissue stiffness, but for now only a few studies have examined its role in the diagnosis of SELs. Recent findings indicate that contrast agents enhance the diagnostic accuracy of EUS (CE-EUS) for SELs. The purpose of the study is to examine the performance of the EUS-E and CE-EUS in differentiating GI-SELs, and in particular gastrointestinal stromal tumors (GISTs). Methods From March 2021 to June 2023, all patients referred to 4 Italian Institutions for EUS-guided fine needle biopsy (FNB) of GI-SELs who agreed to participate in the study were prospectively enrolled. Endosonographic patterns were compared with the analysis of the histological samples taken through FNB or subsequent endoscopic/surgical resection. Sensitivity (Se), specificity (Sp) and accuracy (Ac) of an increasing number of variables in the diagnosis of GIST were then evaluated. Results We present the data of the first 56 enlisted patients. The cohort had a balanced M:F ratio of 1, with a median age of 65 years (range: 26-85). Lesions’ median size was 29 mm (12-90), 38 were detected in the stomach, while 9 in the duodenum and 9 in the esophagus. All lesions were solid and 51 of them originated from the muscolaris propria, 52 were hypoechoic. Qualitative elastography (based on color) revealed homogeneous blue (stiff) lesion in 40 cases, 1 green (soft) lesion and 15 mixed lesions. During contrast infusion, 40 lesions showed hyper-enhancement, 9 hypo-enhancement and 7 iso-enhancement; contrast distribution was evaluated as inhomogeneous in 32 cases. Final diagnosis showed 38 GISTs, 10 leiomyomas and 8 among lipomas, NETs and others. The basic characteristics of EUS (origin 4 th layer and hypo-echogenicity) have a Se, Sp and Ac of 94,7%, 22,2% and 71,5% respectively; qualitative elastography showed Se, Sp and Ac of 71,1%, 44,4% and 62,5% respectively; elastography plus CE-hyperenhancement corresponded to Se, Sp and Ac of 61,1%, 66,7% and 63,0%; and if contrast diffusion was also evaluated as inhomogeneous Se, Sp and Ac were 41,7%, 83,3% and 55,5% respectively. [ 1 ] [ 2 ] [ 3 ] [ 4 ] Conclusions Our findings indicate that EUS alone is highly sensitive and reasonably accurate in detecting GISTs, albeit lacking specificity. However, when dealing with a lesion that raises suspicion for GIST, employing certain enhancing EUS techniques can significantly enhance specificity. This can be of help in the decision-making process, especially where a biopsy is not technically easy to obtain, but, in the concrete suspicion of a GIST, it is of fundamental importance to obtain it. Publication History Article published online: 15 April 2024 © 2024. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0,008 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».