Endoscopic Ultrasound最新文献

Introduction: EUS-guided fine-needle organoid creation (EUS-FNO) from pancreatic cancer (PC) has been increasingly important for precision medicine. The cost for pancreatic organoid creation is substantial and close to 2000 USD/specimen in our institution, and the specimen has to be processed immediately after tissue acquisition so the more passes and specimens, the higher cost of organoid creation will incur. To date, no prospective comparison trial has answered how many needle passes of EUS-FNO needed for a successful organoid creation.

Methods: A prospective trial comparing the success rate of EUS-FNO between the first-pass (group A) versus combination of the first and the second-pass group (group B) was conducted at King Chulalongkorn Memorial Hospital, Thailand. Successful EUS-FNO in group B was defined as positive EUS-FNO from either 1 of 2 passes of EUS-FNO. Techniques for taking tissue from pancreatic cancer are the standard technique of EUS-guided fine needle biopsy (EUS-FNB) using a 20-gauge forward-bevel needle. Tissues from the first and second puncture were collected into separate test tubes that were frozen to control temperature and taken to a laboratory room for organoid culture. The success in pancreatic organoid creation is considered initial success when we could isolate organoids (P0). When organoids grow and are confluent in the Matrigel plate, we would pass the cell to grow in the other Matrigel plate and repeat the passing process until 5 passages of growth. Complete success is defined when we could establish pancreatic organoid lines for ≥5 passages of growth (P5). These processes were performed before standard EUS-FNB for histopathology. We then compared the success rate of pancreatic organoid establishment (P5) in cell culture between single versus two passages. McNemar's test was used for comparison between 2 groups.

Results: Fifty-two patients (33 females, 19 males) with PC underwent EUS-FNO during the period from September 15, 2020, to February 28, 2022, were recruited. Median age (range) was 64.0 (46-88) years. Median BMI (range) was 20.0 (14.6-30.8) kg/m². Tumors were located on the pancreatic head, neck, body, and tail of the pancreas at 57.7%, 7.7%, 25.0%, and 9.6%, respectively. Median size (range) of tumors was 41 (20-134) mm. Median CA19-9 level (range) was 187 units/mL (2.35-35,474). All initially generated pancreatic organoids (P0) could be successfully established (P5). The success rate of EUS-FNO from group A versus B was equally 78.8% (41 from 52 patients) versus 80.8% (42 from 52 patients) (P = 1.00).

Conclusion: Results from this current prospective trial showed that a single pass of EUS-FNO from a PC by using a 20-G forward-bevel needle provided a high success rate. Adding the second pass did not increase the success rate of EUS-FNO.

Background: EUS-guided fine-needle biopsy is the procedure of choice for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). Nevertheless, the samples obtained are small and require expertise in pathology, whereas the diagnosis is difficult in view of the scarcity of malignant cells and the important desmoplastic reaction of these tumors. With the help of artificial intelligence, the deep learning architectures produce a fast, accurate, and automated approach for PDAC image segmentation based on whole-slide imaging. Given the effectiveness of U-Net in semantic segmentation, numerous variants and improvements have emerged, specifically for whole-slide imaging segmentation.

Methods: In this study, a comparison of 7 U-Net architecture variants was performed on 2 different datasets of EUS-guided fine-needle biopsy samples from 2 medical centers (31 and 33 whole-slide images, respectively) with different parameters and acquisition tools. The U-Net architecture variants evaluated included some that had not been previously explored for PDAC whole-slide image segmentation. The evaluation of their performance involved calculating accuracy through the mean Dice coefficient and mean intersection over union (IoU).

Results: The highest segmentation accuracies were obtained using Inception U-Net architecture for both datasets. PDAC tissue was segmented with the overall average Dice coefficient of 97.82% and IoU of 0.87 for Dataset 1, respectively, overall average Dice coefficient of 95.70%, and IoU of 0.79 for Dataset 2. Also, we considered the external testing of the trained segmentation models by performing the cross evaluations between the 2 datasets. The Inception U-Net model trained on Train Dataset 1 performed with the overall average Dice coefficient of 93.12% and IoU of 0.74 on Test Dataset 2. The Inception U-Net model trained on Train Dataset 2 performed with the overall average Dice coefficient of 92.09% and IoU of 0.81 on Test Dataset 1.

Conclusions: The findings of this study demonstrated the feasibility of utilizing artificial intelligence for assessing PDAC segmentation in whole-slide imaging, supported by promising scores.

Background and objectives: EUS is recommended for guiding pancreatic tissue acquisition in suspected autoimmune pancreatitis (AIP) cases. However, there is a lack of comparative research on the effectiveness between EUS-guided fine-needle aspiration (EUS-FNA) and EUS-guided fine-needle biopsy (EUS-FNB) for diagnosing AIP in China. This study aimed to evaluate the diagnostic accuracy of EUS-guided tissue acquisition (EUS-TA) specifically for type 1 AIP.

Methods: Between 2010 and 2023, individuals with AIP who received EUS-TA at Changhai Hospital were included in the study.

Results: A total of 173 patients diagnosed with AIP who underwent EUS-TA were included in the final analysis. Of these, 104 patients (60.1%) received EUS-FNA, and 69 patients (39.9%) underwent EUS-FNB. Sufficient pancreatic tissue samples (>5 cells/high-power field) were obtained in 164 of 173 patients (94.8%), with success rates of 94.2% for EUS-FNA and 95.7% for EUS-FNB (P > 0.05). EUS-FNB exhibited higher rates of reliable level 1 histopathological findings (40.9% vs. 16.3%, P < 0.001) and reliable level 2 histopathological findings (33.3% vs. 12.2%, P < 0.001) compared with EUS-FNA. Furthermore, a higher occurrence of IgG4-positive plasma cell infiltration (>10 cells/high-power field) was observed with EUS-FNB compared with EUS-FNA (74.2% vs. 27.9%, P < 0.001). The multivariate logistic analysis also revealed that EUS-FNA was less effective in obtaining reliable evidence compared with EUS-FNB, as evident in both level 2 (P = 0.002; odds ratio, 0.21; 95% confidence interval, 0.08-0.56) and level 1 (P = 0.001; odds ratio, 0.19; 95% confidence interval, 0.08-0.49) histopathological evidence.

Conclusions: EUS-FNB demonstrates higher rates of level 1 and level 2 histopathological findings, as well as more abundant IgG4-positive plasma cell infiltration, compared with EUS-FNA.

Background and objectives: A majority of pancreatic malignancies are unresectable at the time of presentation and require EUS-guided fine-needle aspiration or fine-needle biopsy (EUS-FNA/FNB) for diagnosis. With the advent of precision therapy, there is an increasing need to use EUS-FNA/FNB sample for genetic analysis. Next-generation sequencing (NGS) is a preferred technology to detect genetic mutations with high sensitivity in small specimens. We performed a meta-analysis to evaluate the adequacy of EUS-FNA/FNB for NGS in pancreatic malignancies.

Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched from database inception to November 11, 2023. The primary outcome was the proportion of sufficient sample acquired by EUS-FNA/FNB in pancreatic malignancies for NGS. Secondary outcomes were the proportion of sufficient sample for NGS in pancreatic ductal adenocarcinoma (PDAC) and the detection rates of mutations in KRAS, TP53, CDKN2A, and SMAD4 and actionable mutations in PDAC. The pooled proportions were calculated using a random-effects model. Potential sources of heterogeneity were investigated with subgroup analyses and meta-regression.

Results: Twenty studies with 881 samples were included. The pooled adequacy of EUS-FNA/FNB sample for NGS was 89.9% (95% CI, 80.8%-96.7%) in pancreatic malignancies and 92.0% (95% CI, 81.3%-98.8%) in PDAC. Screening sample suitability before NGS testing was associated with lower adequacy in subgroup analysis (79.7% vs. 98.4%, P = 0.001). The pooled prevalences of mutations in KRAS, TP53, CDKN2A, and SMAD4 in PDAC were 87.4% (95% CI, 83.2%-91.2%), 62.6% (95% CI, 53.2%-71.7%), 20.6% (95% CI, 11.9%-30.8%), and 19.4% (95% CI, 11.2%-29.1%), respectively. The pooled prevalence of potentially actionable mutations in PDAC was 14.5% (95% CI, 8.2%-22.0%).

Conclusions: In the majority of cases, EUS-FNA/FNB can acquire adequate sample for NGS and identify tumor-specific mutations in patients with pancreatic malignancies. Strict pre-analysis screening criteria may negatively impact the sample adequacy and the success rate for NGS.

Background and objectives: An accurate diagnosis is crucial for the clinical management of pancreatic cystic neoplasm (PCN). EUS-guided through-the-needle biopsy (EUS-TTNB) is a novel technique for improving the accuracy of PCN diagnosis. There is insufficient evidence about the efficacy of EUS-TTNB. This study aims to evaluate the feasibility and diagnostic performance of EUS-TTNB for PCN.

Methods: Between June 2015 and July 2023, we prospectively enrolled 454 patients with a clinical concern for PCN in our database. After excluding those diagnosed with pancreatic cancer, pseudocysts, or other no-neoplasms, we assessed 326 patients with 329 cysts undergoing EUS-guided fine-needle-aspiration (EUS-FNA) or EUS-TTNB for evaluation. The primary indicators were tissue acquisition yield and diagnostic yield. The cyst characteristics (size, location, the presence of septation, mural nodule, and solid mass) and the number of biopsy samples were chosen for the analysis of factors associated with diagnostic performance.

Results: There were 220 (67.5%) females and 106 (32.5%) males, and the median patient age was 50 years (range, 18-88). There were 329 cysts sampled by FNA and 143 by TTNB. The median cyst size was 31.5 mm (range, 6.9-114.0). The diagnostic yields of FNA and TTNB were 35.7% (112/314) and 57.5% (73/127), respectively (P < 0.001). Special cyst types were diagnosed by TTNB in 58 (45.7%, 58/127) cysts, 19 of which had surgical pathology. Fifteen of 19 TTNB diagnoses were concordant with the surgical pathology.

Conclusion: EUS-TTNB is an option to improve the diagnosis of PCN. Standardized procedures and appropriate indications for TTNB need to be studied.

Objective 

To prepare a set of practice guidelines to standardize the entire process, from diagnosis to treatment and follow-up, for pancreatic pseudocysts and walled-off necrosis.

Methods 

Thirty-six experts in the fields of digestive endoscopy, pancreatic surgery, interventional radiology, and others presented their opinions via discussions in online conferences by referring to the patient, intervention, comparison, and outcomes principles and then reviewed the evidence and statements using the Delphi method to reach a consensus. The consensus of >80% was finally achieved for the items.

Results 

The experts discussed and reached a consensus on 29 statements including 10 categories: (1) definition and classification, (2) imaging and endoscopic diagnosis, (3) therapeutic implications, (4) surgical therapy, (5) percutaneous catheter drainage, (6) endoscopic retrograde cholangiopancreatography, (7) EUS-guided drainage, (8) stent selection for EUS-guided drainage, (9) complication related to stents for cyst drainage, and (10) drug treatment and follow-up.

Conclusion 

This consensus based on the clinical experience of experts in various fields and international evidence-based medicine further standardizes the multidisciplinary diagnosis and treatment processes for pancreatic pseudocysts and walled-off necrosis.