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Enregistrement W2028523859 · doi:10.4103/2303-9027.127118

Endoscopic ultrasound-guided fine-needle aspiration: Getting to the point

2014· article· en· W2028523859 sur OpenAlexaff
AnandV Sahai

Notice bibliographique

RevueEndoscopic Ultrasound · 2014
Typearticle
Langueen
DomaineMedicine
ThématiquePancreatic and Hepatic Oncology Research
Établissements canadiensHôpital Saint-Luc
Organismes subventionnairesnon disponible
Mots-clésMedicineMalignancyFine-needle aspirationRadiologyEndoscopic ultrasoundRest (music)SuctionBiopsyPathologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Endoscopic ultrasound (EUS)-guided fine-needle aspiration (FNA) is arguably the cornerstone of the on-going popularity of EUS. The ability to safely and reliably obtain cytological or histological proof of malignancy; or to reliably exclude malignancy in indeterminate lesions is clinically extremely useful — particularly when lesions are otherwise inaccessible. In expert hands, the sensitivity of EUS-FNA for malignancy is over 90% (higher for nodes, lower for pancreatic malignancies).[1] Can we improve on these results? Can less experienced endosonographers be as effective? Finding the best and simplest technique could answer both questions. Most studies on EUS-FNA have focused on comparing variables such as needle sizes, needle type, suction, stylet use, on site cytological analysis, number of passes, etc. These studies seem to ignore the fact that the primary goal of the EUS-FNA technique is to effectively position the FNA needle into the target lesion and then move the needle to collect tumor cells. After all, if the needle does not come in contact with the tumor, it does not matter what other variable you change, there can be no diagnosis of cancer. In other words, these studies ignore what, in practice, is likely the most important variable in EUS-FNA — how and where the EUS-FNA needle is positioned and moved within the target lesion. It is well-known that what appears as a “mass” or malignant node may contain only a small focus of tumor. The rest may be inflammation or necrosis. The only study that compared wide tissue sampling (“fanning”) to regular sampling showed a clear advantage to fanning and a yield after the first pass that was comparable to the results in most other studies after multiple passes.[2] All this is to say that maybe we should be focusing on what is happening at the point of the needle, rather than at the other end! Is it possible that proper technique can overcome all other variables? The current literature does not allow us to answer this question because great majority of papers never describe the needle path (whether the entire lesion traversed, what part of the lesion was sampled) or if fanning was used or not. Every “expert” believes that his or her technique is best and they are very resistant to change. In our experience, we are able to obtain a sensitivity of 90% with only 2 passes, with no stylet, no suction and no cytologist.[3] How is this possible? We believe that it is because we use an aggressive multi-pass fanning technique. Our simplified technique requires less nursing support and is faster and safer (due to no risk of needle stick injury during re-insertion of the stylet). If this basic technique does not work, we may try to use a “salvage” maneuver by adding suction or perhaps trying a different needle type (such a needle with a side hole). Anecdotally, we find this is rarely helpful. All this is to say that there may be many different variables that need to be taken into account maximize the results of EUS-FNA, or maybe just one variable: The endosonographer — because that is the one variable that controls how the needle is used. If this is the case, could training be more important that hardware? In this issue of EUS, we hope to offer a balanced and detailed assessment of as many issues related to maximizing the yield of EUS-FNA; as well maximizing its effectiveness. I would like to sincerely thank our international panel of experts for their thoughtful contributions.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,148
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,327
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2014
Routes d'admission1
Résumé présentoui

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