Ponction sous endosonographie des lésions digestives et péri-digestives : étude de son rendement diagnostique à l’ère de nouvelles aiguilles permettant des prélèvements biopsiques
Bibliographic record
Abstract
Introduction: The adequate management of a disease necessarily requires a precise diagnosis for which certain paraclinical examinations have brought great diagnostic yield. Thus, endoscopic ultrasound (EUS) and the puncture by EUS (EUS-FNA), used in clinical practice for a several decades, made it possible to obtain better diagnostic yield in the event of digestive and peridigestive lesions. Currently, different forms of needles, whether fine aspiration needles (FNA) or other more newly developed ones, the so-called biopsy-puncture needles (FNB), are used for the acquisition of cytological or even histological material with variable diagnostic yields. Objective: To assess the contribution of fine needle aspiration under endoscopy using FNB and FNA needles for the diagnosis of digestive masses and malign lesions of the pancreas in particular. Method: Our methodological approach was carried out in three steps. As a first step, we performed a literature review accompanied by a meta-analysis according to the PRISMA guideline to compare the diagnostic yield of EUS-FNA and EUS-FNB for malign pancreatic masses. Secondly, a cross-sectional study was carried out to analyze the diagnostic yield and performance of fine needle aspiration (FNA) in the diagnosis of pelvic lesions and its determinants with samples taken in patients between January 2001 and March 2005 in the Centre Hospitalier de l’Université de Montréal (CHUM). Finally, a randomized crossover non-inferiority, double blind clinical trial was performed at the CHUM comparing the diagnostic yield of guided fine needle aspiration with standard 25 Gauge FNA needle (25S) in comparison with the 25 Gauge Procore FNB needle (25P) for the diagnosis of pancreatic cancer. Results: The results of the meta-analysis showed that EUS-FNB provides better diagnostic yield than EUS-FNA in the detection of malignant pancreatic lesions with a combined OR of 1.87 (95% CI: 1.33-2.63). When we pooled all the available data on the 2 needle types (cutting versus scratching needles), we found a non-statistically significant higher diagnostic yield for the cutting needles compared to scraping needles, with a combined OR of 1.47 (95 % CI: 0.67-3.22). The cross-sectional study showed that PEUS-FNA was positive for malignancy in 45% of cases, atypical or suspected in 4.7% of cases and negative for malignancy in 50.3% of cases. The diagnostic yield was 82.7%. PEUS-FNA performance indices were estimated in the patient subgroup for which the gold standard was available. It showed a sensitivity of 89.3%, a specificity of 100%, a diagnostic accuracy of 93.2%, a positive predictive value of 100% and a negative predictive value of 84.2% in the event of pelvic lesions. No analyzed variable was associated with the performance indices. Finally, the randomized clinical trial showed that the 25G standard (25S) FNA needle was noninferior to the 25P FNB needle for diagnosing pancreatic cancer, cellularity or the presence of blood. Conclusion: We can say based on the meta-analysis that EUS-FNB generally provides better diagnostic yield for cancerous pancreatic lesions compared to EUS-FNA. On the other hand, this advantage of FNB could not be observed for small gauge needles. Moreover, the diagnostic yield of the 25S needle (FNA) was not inferior to the 25P needle (FNB) for the diagnosis of cancerous pancreatic lesions in our clinical trial. With regard to pelvic lesions, PEUS-FNA seems to be a safe and effective means of investigation.
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".