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Validation of a realistic, simple, and inexpensive EUS-FNA training model using isolated porcine stomach.

ABSTRACT: Trainees are required to learn EUS-FNA using a model before working with a patient. The aim of the current study was to validate a new training model developed for EUS-FNA.Several fresh chicken tenderloins were embedded as target lesions in the submucosal layer of an isolated porcine stomach. The stomach was fixed to a plate with nails, and was placed in a tub filled with water. The primary endpoint was feasibility of the newly developed model for EUS-FNA training, evaluated as follows: 1) visualization of the target lesion with blinding for lesion location; 2) penetrability of the needle; 3) sampling rate of macroscopic specimen; and 4) ROSE capability. Secondary endpoints were its durability and utility for multiple EUS-FNA procedures during EUS-FNA training, and the ease and cost of preparing the model.Six endoscopists (1 expert, 5 trainees) attempted EUS-FNA procedures using this model. The target lesion could be identified clearly, and EUS-FNA could be performed with realistic resistance felt. In addition, rapid on-site evaluation could be easily achieved. Based on 10 needlings by each endoscopist, adequate specimens for histology could be macroscopically taken with an average 85 % success rate. Visibility and maneuverability were maintained throughout all needlings. Preparation time for this model was less than 30 minutes with a total cost of $ 22.An easy-to-use and inexpensive training model with a realistic feel of needling was created. This model can potentially enable beginners to practice safe and effective EUS-FNA procedures.

SUBMITTER: Hoshi K

PROVIDER: S-EPMC5025348 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

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Validation of a novel swine model for training in EUS-FNA (with videos).

Project description:Background and objectivesEUS-FNA is applied widely in clinical practice, but there remains a lack of authentic training models. The present study aimed to develop a novel swine training model and to perform a preliminary assessment of its feasibility and efficacy.Materials and methodsTo create an internal lesion-like target, empty shells of iodine-125 seeds were implanted into the caudate lobe of the liver in Bama minipigs. A training program involving 10 trainees was subsequently carried out, in which a total of 60 needlings were performed, composed of 6 for each trainee obtained during two training steps. Comparisons of procedure-related variables were conducted between the two. Trainees completed a questionnaire to assess their basic endoscopic experiences and reasonability of the model.ResultsA target region of 2.0 cm × 2.0 cm in diameter was successfully established on the caudate lobe in all implanted pigs. In the training program, the average procedure time decreased from the first to the second step and the average time for the total 30 needlings' obtainment was significantly shorter for the second training step (23.8 ± 4.5 min vs. 40.9 ± 9.0 min, P < 0.001). For the second step, there was also a significant improvement in total success rate (86.7% vs. 56.7%, P = 0.020) and accuracy rate (76.7% vs. 43.3%, P = 0.017). All trainees scored the effectiveness of the model highly and all reported improved confidence after the training.ConclusionThis novel swine training model could authentically mimic clinical EUS-FNA, providing an effective in vivo practice tool for novices before clinical practice.

| S-EPMC7529007 | biostudies-literature

Pancreatic hydatid cyst diagnosed on EUS-guided FNA.

Project description: Not available

| S-EPMC6669307 | biostudies-literature

Transoral EUS-guided FNA of prevertebral cervical metastasis.

Project description: Not available

| S-EPMC6732263 | biostudies-literature

Diagnosis of a remote neuroendocrine tumor thrombus by EUS-guided FNA.

Project description: Not available

| S-EPMC7003146 | biostudies-literature

Targeted transcriptomic analysis of pancreatic adenocarcinoma in EUS-FNA samples by NanoString technology

Project description:Background: Integration of transcriptomic testing into EUS-FNA samples is a growing need for precision oncology in pancreatic ductal adenocarcinoma (PDAC). The NanoString platform is suitable for transcriptome profiling in low yield RNA samples. Methods: Inclusion of patients that underwent EUS-FNA cytological diagnosis of pancreatic ductal adenocarcinoma using 19G and/or 22G needles and subsequent surgical resection. Formalin fixed paraffin embedded (FFPE) cytological and surgical samples underwent RNA extraction and transcriptomic analysis using a custom 52-gene NanoString panel of stromal PDAC features. Cell type abundance was quantified in FFPE specimens and correlated. Results: 18 PDAC patients were included. Mean EUS-FNA passes was 2 + 0.7. All FFPE passed the RNA quality control for genomic analysis. Hierarchical clustering on the global gene expression data showed that genes were differentially expressed between EUS and surgical samples. A more enriched cancerassociated fibroblasts and epithelial-mesenchymal transition transcriptomic profile was observed across surgical specimens whereas immunological biomarkers were more represented in EUS-FNA samples. Cytological examination confirmed a scanty representation of CAF and more immunological cell abundance in cytological samples in comparison to surgical specimens. Conclusion: Targeted transcriptomic NanoString profiling of PDAC samples obtained by EUS-FNA is a feasible approach for pre-surgical molecular analysis although stromal CAF/EMT mRNA biomarkers are underrepresented.

2023-05-04 | GSE225953 | GEO

Three-dimensional Assessment of the Breast: Validation of a Novel, Simple and Inexpensive Scanning Process.

Project description:BACKGROUND/AIM:Methods to assess three-dimensionally the breast surface are increasingly used in plastic and reconstructive surgery. The aim of this study was to validate the use of the Structure Sensor 3D scanner (Occipital, Inc., Boulder, CO, USA) connected to an iPad Pro (Apple, Inc., Cupertino, CA, USA) as a novel, inexpensive and handheld three-dimensional scanning process. MATERIALS AND METHODS:Surface images of a medical human female anatomy torso model of rigid plastic were repeatedly acquired with Structure Sensor 3D scanner and compared with those obtained using two clinically established 3D imaging systems. Digital measurements of vector and surface breast distances were analyzed using Mimics® Innovation Suite 20 medical imaging software (Materialise, Leuven, Belgium). RESULTS:The analysis of variance (ANOVA) revealed no statistically significant difference among measurements obtained using different scanning processes for all the variables examined (p>0.05). CONCLUSION:The study demonstrates analogous practicability and reliability for surface image acquisition using the newly introduced Structure Sensor 3D scanner and other clinically established scanners.

| S-EPMC6559897 | biostudies-literature

Diagnostic performance and factors influencing the accuracy of EUS-FNA of pancreatic neuroendocrine neoplasms.

Project description:BackgroundMultiple studies have investigated sampling adequacy of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) for pancreatic neuroendocrine neoplasms (pNENs). However, none have described the diagnostic performance of EUS-FNA for pNENs, or the influencing factors. The aim of this study was to evaluate the diagnostic accuracy of EUS-FNA, with post-operative pathological diagnosis as the gold standard, and factors predictive of inadequate EUS sampling.MethodsFrom 1998 to 2014, a total of 698 patients underwent pancreatic resection and 1455 patients underwent EUS-FNA sampling for pancreatic lesions. A total of 410 cases underwent both surgical resection and preceding EUS-FNA. Of these, 60 cases (49 true pNEN, nine non-diagnostic, two misdiagnoses) were included. We studied diagnostic performance of EUS-FNA and factors that were associated with failed diagnosis.ResultsOf the 60 cases, EUS-FNA yield was 49 true-positive cases, two misdiagnoses, and nine non-diagnostic cases (including six suggestive cases). Sensitivity, specificity, and accuracy were 84.5, 99.4, and 97.3 %, respectively; including the six suggestive cases, diagnostic values were 94.8 % sensitivity (55/58), 99.4 % specificity (350/352), and 98.7 % accuracy (405/410). In multivariate analysis, sampling adequacy rates were significantly lower when lesions were located in the pancreatic head [odds ratio (OR) = 10.0] and in tumor-rich stromal fibrosis (OR = 10.45). Tumor size, needle type, tumor grading, presence of cystic component, and time period were not significant factors.ConclusionsEUS-FNA offers high accuracy for pNEN. However, location of the tumor in the pancreatic head and presence of rich stromal fibrosis negatively impacts sampling adequacy.

| S-EPMC4990623 | biostudies-literature

Diagnostic yield of EUS-guided through-the-needle microforceps biopsy versus EUS-FNA of pancreatic cystic lesions: a systematic review and meta-analysis.

Project description:Background and study aims Accurate diagnosis and risk stratification of pancreatic cysts (PCs) is challenging. The aim of this study was to perform a systematic review and meta-analysis to assess the feasibility, safety, and diagnostic yield of endoscopic ultrasound-guided through-the-needle biopsy (TTNB) versus fine-needle aspiration (FNA) in PCs. Methods Comprehensive search of databases (PubMed, EMBASE, Cochrane, Web of Science) for relevant studies on TTNB of PCs (from inception to June 2019). The primary outcome was to compare the pooled diagnostic yield and concordance rate with surgical pathology of TTNB histology and FNA cytology of PCs. The secondary outcome was to estimate the safety profile of TTNB. Results: Eight studies (426 patients) were included. The diagnostic yield was significantly higher with TTNB over FNA for a specific cyst type (OR: 9.4; 95 % CI: [5.7-15.4]; I 2 = 48) or a mucinous cyst (MC) (OR: 3.9; 95 % CI: [2.0-7.4], I 2 = 72 %). The concordance rate with surgical pathology was significantly higher with TTNB over FNA for a specific cyst type (OR: 13.5; 95 % CI: [3.5-52.3]; I 2 = 48), for a MC (OR: 8.9; 95 % [CI: 1.9-40.8]; I 2 = 29), and for MC histologic severity (OR: 10.4; 95 % CI: [2.9-36.9]; I 2 = 0). The pooled sensitivity and specificity of TTNB for MCs were 90.1 % (95 % CI: [78.4-97.6]; I 2 = 36.5 %) and 94 % (95 % CI: [81.5-99.7]; I 2 = 0), respectively. The pooled adverse event rate was 7.0 % (95 % CI: [2.3-14.1]; I 2 = 82.9). Conclusions TTNB is safe, has a high sensitivity and specificity for MCs and may be superior to FNA cytology in risk-stratifying MCs and providing a specific cyst diagnosis.

| S-EPMC7164999 | biostudies-literature

Molecular Characterization and Xenotransplantation of Pancreatic Cancer Using Endoscopic Ultrasound-Guided Fine Needle Aspiration (EUS-FNA).

Project description:Pancreatic cancer has one of the worst prognoses among all malignancies and few available treatment options. Patient-derived xenografts can be used to develop personalized therapy for pancreatic cancer. Endoscopic ultrasound fine-needle aspiration (EUS-FNA) may provide a powerful alternative to surgery for obtaining sufficient tissue for the establishment of patient-derived xenografts. In this study, EUS-FNA samples were obtained for 30 patients referred to the Ottawa Hospital, Ottawa, Ontario, Canada. These samples were used for xenotransplantation in NOD-SCID mice and for genetic analyses. The gene expression of pancreatic-cancer-relevant genes in xenograft tumors was examined by immunohistochemistry. Targeted sequencing of both the patient-derived tumors and xenograft tumors was performed. The xenografts' susceptibility to oncolytic virus infection was studied by infecting xenograft-derived cells with VSV∆51-GFP. The xenograft take rate was found to be 75.9% for passage 1 and 100% for passage 2. Eighty percent of patient tumor samples were successfully sequenced to a high depth for 42 cancer genes. Xenograft histological characteristics and marker expression were maintained between passages. All tested xenograft samples were susceptible to oncoviral infection. We found that EUS-FNA is an accessible, minimally invasive technique that can be used to acquire adequate pancreatic cancer tissue for the generation of patient-derived xenografts and for genetic sequencing.

| S-EPMC11311391 | biostudies-literature

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

Project description:Background and aimsEndoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.MethodsHSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.ResultsA total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.ConclusionsAn HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

| S-EPMC10501295 | biostudies-literature

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