Clinical impact of strain histogram EUS elastography and contrast-enhanced EUS for the differential diagnosis of focal pancreatic masses: A prospective multicentric study

Comparison of standard vs auxiliary (contrast or elastography) endoscopic ultrasound-guided fine needle aspiration/biopsy in solid pancreatic lesions: A meta-analysis

BACKGROUND

Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) is the most common modality for tissue acquisition from pancreatic masses. Despite high specificity, sensitivity remains less than 90%. Auxiliary techniques like elastography and contrast-enhanced EUS may guide tissue acquisition from viable tumor tissue and improve the diagnostic outcomes theoretically. However, data regarding the same have shown conflicting results.

AIM

To compare the diagnostic outcomes of auxiliary-EUS-FNA/B to standard EUS-FNA/B for pancreatic lesions.

METHODS

The electronic databases of MEDLINE, EMBASE, and Scopus were searched from inception to February 2024 for all relevant studies comparing diagnostic outcomes of auxiliary-EUS-FNA/B to standard EUS-FNA/B for pancreatic lesions. A bivariate hierarchical model was used to perform the meta-analysis.

RESULTS

A total of 10 studies were identified. The pooled sensitivity, specificity, and area under the receiver-operated curve (AUROC) for standard EUS-FNA/B were 0.82 (95%CI: 0.79-0.85), 1.00 (95%CI: 0.96-1.00), and 0.97 (95%CI: 0.95-0.98), respectively. The pooled sensitivity, specificity, and AUROC for EUS-FNA/B with auxiliary techniques were 0.86 (95%CI: 0.83-0.89), 1.00 (95%CI: 0.94-1.00), and 0.96 (95%CI: 0.94-0.98), respectively. Comparing the two diagnostic modalities, sensitivity [Risk ratio (RR): 1.04, 95%CI: 0.99-1.09], specificity (RR: 1.00, 95%CI: 0.99-1.01), and diagnostic accuracy (RR: 1.03, 95%CI: 0.98-1.09) were comparable.

CONCLUSION

Analysis of the currently available literature did not show any additional advantage of EUS-FNA/B with auxiliary techniques for pancreatic solid lesions over standard EUS-FNA/B. Further randomized studies are required to demonstrate the benefit of auxiliary techniques before they can be recommended for routine practice.

EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

Objectives

This study aimed to develop and validate intratumoral, peritumoral, and combined radiomic models based on endoscopic ultrasonography (EUS) for retrospectively differentiating pancreatic neuroendocrine tumors (PNETs) from pancreatic cancer.

Methods

A total of 257 patients, including 151 with pancreatic cancer and 106 with PNETs, were retroactively enrolled after confirmation through pathological examination. These patients were randomized to either the training or test cohort in a ratio of 7:3. Radiomic features were extracted from the intratumoral and peritumoral regions from conventional EUS images. Following this, the radiomic features underwent dimensionality reduction through the utilization of the least absolute shrinkage and selection operator (LASSO) algorithm. Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. The optimum intratumoral radiomic model was identified and subsequently employed for further analysis. Furthermore, a combined radiomic model integrating both intratumoral and peritumoral radiomic features was established and assessed based on the same machine learning algorithm. Finally, a nomogram was constructed, integrating clinical signature and combined radiomics model.

Results

107 radiomic features were extracted from EUS and only those with nonzero coefficients were kept. Among the six radiomic models, the support vector machine (SVM) model had the highest performance with AUCs of 0.853 in the training cohort and 0.755 in the test cohort. A peritumoral radiomic model was developed and assessed, achieving an AUC of 0.841 in the training and 0.785 in the test cohorts. The amalgamated model, incorporating intratumoral and peritumoral radiomic features, exhibited superior predictive accuracy in both the training (AUC=0.861) and test (AUC=0.822) cohorts. These findings were validated using the Delong test. The calibration and decision curve analyses (DCA) of the combined radiomic model displayed exceptional accuracy and provided the greatest net benefit for clinical decision-making when compared to other models. Finally, the nomogram also achieved an excellent performance.

Conclusions

An efficient and accurate EUS-based radiomic model incorporating intratumoral and peritumoral radiomic features was proposed and validated to accurately distinguish PNETs from pancreatic cancer. This research has the potential to offer novel perspectives on enhancing the clinical utility of EUS in the prediction of PNETs.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors

OBJECTIVES

The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).

METHODS

Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.

RESULTS

One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.

CONCLUSIONS

The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.

TRIAL REGISTRATION

ChiCTR2400091906.

Endoscopic Ultrasound-Guided Pancreatic Tissue Sampling: Lesion Assessment, Needles, and Techniques

Endoscopic ultrasound (EUS)-guided tissue sampling includes the techniques of fine needle aspiration (FNA) and fine needle biopsy (FNB), and both procedures have revolutionized specimen collection from the gastrointestinal tract, especially from remote/inaccessible organs. EUS-FNB has replaced FNA as the procedure of choice for tissue acquisition in solid pancreatic lesions (SPLs) across various society guidelines. FNB specimens provide a larger histological tissue core (preserving tissue architecture) with fewer needle passes, and this is extremely relevant in today's era of precision and personalized molecular medicine. Innovations in needle tip design are constantly under development to maximize diagnostic accuracy by enhancing histological sampling capabilities. But, apart from the basic framework of the needle, various other factors play a role that influence diagnostic outcomes, namely, sampling techniques (fanning, aspiration or suction, and number of passes), collection methods, on-site evaluation (rapid, macroscopic, or visual), and specimen processing. The choice taken depends strongly on the endoscopist's preference, available resources at the disposal, and procedure objectives. Hence, in this review, we explicate in detail the concepts and available literature at our disposal on the topic of EUS-guided pancreatic tissue sampling to best guide any practicing gastroenterologist/endoscopist in a not-to-ideal set-up, which EUS-guided tissue acquisition technique is the "best" for their case to augment their diagnostic outcomes.

Combining Potential Strain Elastography and Radiomics for Diagnosing Breast Lesions in BI-RADS 4: Construction and Validation a Predictive Nomogram

RATIONALE AND OBJECTIVES

To develop a nomogram by integrating B-mode ultrasound (US), strain ratio (SR), and radiomics signature (RS) effectively differentiating between benign and malignant lesions in the Breast Imaging Reporting and Data System (BI-RADS) 4.

MATERIALS AND METHODS

We retrospectively recruited 709 consecutive patients who were assigned a BI-RADS 4 and underwent curative resection or biopsy between 2017 and 2022. US images were collected before surgery. A RS was developed through a multistep feature selection and construction process. Histology findings served as the gold standard. Univariate and multivariate regression analysis were employed to analyze the clinical and US characteristics and identify variables for developing a nomogram. The calibration and discrimination of the nomogram were conducted to evaluate its performance.

RESULTS

The study included a total of 709 patients, with 497 in the training set and 212 in the validation set. In the training set, the B-mode US had an AUC of 0.84 (95% confidence interval [CI], 0.80, 0.87). The SR demonstrated an AUC of 0.78 (95% CI, 0.74, 0.82), while the RS showed an AUC of 0.85 (95% CI, 0.81, 0.88). Notably, the nomogram exhibited superior performance compared to the conventional US, SR, and RS (AUC=0.93, both p < 0.05, as per the Delong test). The clinical usefulness of the nomogram was favorable.

CONCLUSION

The calibrated nomogram can be specifically designed to predict the malignancy of breast lesions in the BI-RADS 4 category.

A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumors

OBJECTIVES

This research aimed to retrospectively construct and authenticate ultrasomics models using endoscopic ultrasonography (EUS) images for forecasting the pathological grading of pancreatic neuroendocrine tumors (PNETs).

METHODS

After confirmation through pathological examination, a retrospective analysis of 79 patients was conducted, including 49 with grade 1 PNETs and 30 with grade 2/3 PNETs. These patients were randomized to the training or test cohort in a 6:4 proportion. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimensionality of ultrasomics features derived from standard EUS images. These nonzero coefficient features were retained and applied to construct prediction models via eight machine-learning algorithms. The optimum ulstrasomics model was determined, followed by creating and evaluating a nomogram.

RESULTS

Ultrasomics features of 107 were extracted, and only those with coefficients greater than zero were retained. The XGboost ultrasomics model performed exceptionally well, achieving AUCs of 0.987 and 0.781 in the training and test cohorts, respectively. Furthermore, an effective nomogram was developed and visually represented. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curve (CIC) displayed in the ulstrasomics model and nomogram demonstrated high accuracy. They provided significant net benefits for clinical decision-making.

CONCLUSIONS

A novel ulstrasomics model and nomogram were created and certified to predict the pathological grading of PNETs using EUS images. This study has the potential to provide valuable insights that improve the clinical applicability and efficacy of EUS in predicting the grading of PNETs.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors

OBJECTIVES

To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).

METHODS

A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.

RESULTS

A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.

CONCLUSION

A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer

OBJECTIVES

To develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer.

METHODS

A total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed.

RESULTS

A total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models.

CONCLUSIONS

A novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.

Diagnostic Endoscopic Ultrasound in Pancreatology: Focus on Normal Variants and Pancreatic Masses

Background

Endoscopic ultrasound (EUS) is a main tool in gastroenterology for both diagnosis and exclusion of pancreatic pathology. It allows minimally invasive assessment of various diseases or anatomic variations affecting the pancreas also with the help of new Doppler technologies, elastography, contrast-enhanced imaging including post hoc image processing with quantification analyses, three-dimensional reconstruction, and artificial intelligence. EUS also allows interventional direct access to the pancreatic parenchyma and the retroperitoneal space, to the pancreatic duct, pancreatic masses, cysts, and vascular structures.

Summary

This review aimed to summarize new developments of EUS in the field of pancreatology. We highlight the role of EUS in evaluating pancreatic pathology by describing normal anatomic variants like pancreas divisum, pancreatic lipomatosis, pancreatic fibrosis in the elderly and characterizing pancreatic masses, both in the context of chronic pancreatitis and within healthy pancreatic parenchyma. EUS is considered the optimal imaging modality for pancreatic masses of uncertain dignity and allows both cytological diagnosis and histology, which is essential not only for neoplastic conditions but also for tailoring therapy for benign inflammatory conditions.

Key Messages

EUS plays an indispensable role in pancreatology and the development of new diagnostic and interventional approaches to the retroperitoneal space and the pancreas exponentially increased over the last years. The development of computer-aided diagnosis and artificial intelligence algorithms hold the potential to overcome the obstacles associated with interobserver variability and will most likely support decision-making in the management of pancreatic disease.

Ultrasonic Elastography-guided Pleural Biopsy for the Diagnosis of Pleural Effusion: A Multicenter Prospective Study of Diagnostic Test Performance

Rationale: The diagnostic yield of traditional ultrasound-guided pleural biopsy remains unsatisfactory, particularly when the pleural thickness is ⩽5 mm and/or no pleural nodules are detected. Pleural ultrasound elastography (UE) has a better diagnostic yield than traditional ultrasound for malignant pleural effusion (MPE). However, studies on UE-guided pleural biopsies are lacking. Objectives: To evaluate the feasibility and safety of UE-guided pleural biopsy. Methods: In this multicenter prospective single-arm trial, patients with pleural effusion whose pleural thickness was ⩽5 mm with no pleural nodules were enrolled between July 2019 and August 2021. The diagnostic yield of UE-guided pleural biopsy for pleural effusion and its sensitivity for detecting MPE were evaluated. Results: Ninety-eight patients (mean age, 62.4 ± 13.2 yr; 65 men) were prospectively enrolled. The diagnostic yield of UE-guided pleural biopsy for making any diagnosis was 92.9% (91/98), and its sensitivity for MPE was 88.7% (55/62). In addition, its sensitivity for pleural tuberculosis was 69.6% (16/23). The rate of postoperative chest pain was acceptable, and there was no pneumothorax. Conclusions: UE-guided pleural biopsy is a novel technique for diagnosing MPE with good diagnostic yield and sensitivity. Clinical trial registered with https://www.chictr.org.cn (ChiCTR2000033572).

A review of contrast-enhanced harmonic endoscopic ultrasonography for pancreatic solid tumors

Endoscopic ultrasonography (EUS) is superior to other imaging modalities in the detection of pancreatic masses, although differentiating the types of pancreatic masses detected on EUS remains challenging. However, the value of contrast-enhanced harmonic EUS (CH-EUS) using ultrasound contrast agents for this differentiation has been reported. CH-EUS plays a pivotal role in analysis of small lesions that can only be detected with EUS. Recently, CH-EUS was used for staging and/or determining the resectability of pancreatic cancer in several clinical trials. In addition, it is used to estimate the response of pancreatic cancer to chemotherapy and to determine the prognosis in cases of pancreatic cancer and pancreatic neuroendocrine neoplasms. It is also postulated that CH-EUS improves the diagnostic performance of endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNAB) through complementary diagnoses using CH-EUS and EUS-FNAB, or CH-EUS-guided EUS-FNAB. Thus, CH-EUS has been employed for various qualitative diagnoses, including differentiation of pancreatic masses. Second-generation contrast agents such as Sonazoid are used clinically for ultrasound diagnostic imaging of liver and breast disease. The positioning of CH-EUS with Sonazoid as a test for the diagnosis of solid pancreatic tumors is an issue for further studies.

The Latest Advancements in Diagnostic Role of Endosonography of Pancreatic Lesions

Endosonography, a minimally invasive imaging technique, has revolutionized the diagnosis and management of pancreatic diseases. This comprehensive review highlights the latest advancements in endosonography of the pancreas, focusing on key technological developments, procedural techniques, clinical applications and additional techniques, which include real-time elastography endoscopic ultrasound, contrast-enhanced-EUS, EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. EUS is well established for T-staging and N-staging of pancreaticobiliary malignancies, for pancreatic cyst discovery, for identifying subepithelial lesions (SEL), for differentiation of benign pancreaticobiliary disorders or for acquisition of tissue by EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. This review briefly describes principles and application of EUS and its related techniques.

The efficiency of contrast-enhanced endoscopic ultrasound (EUS) combined with EUS elastography for pancreatic cancer diagnosis: a systematic review and meta-analysis

PURPOSE

This study aimed to evaluate the performance characteristics of endoscopic ultrasound (EUS) elastography combined with contrast-enhanced EUS (CE-EUS) for the diagnosis of pancreatic malignancy among solid pancreatic lesions (SPLs).

METHODS

A comprehensive literature search using MEDLINE, Embase, and the Cochrane Library was performed from January 1991 through December 2020. The pooled performance characteristics, including sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (OR), were calculated using Meta-DiSc 1.4.

RESULTS

In total, 430 patients with 282 pancreatic malignancies were included in the metaanalysis. The pooled sensitivity, specificity, LR+, LR-, and diagnostic OR were 84% (95% confidence interval [CI], 80% to 88%), 85% (95% CI, 78% to 90%), 5.31 (95% CI, 2.57 to 10.97), 0.15 (95% CI, 0.07 to 0.34), and 67.72 (95% CI, 12.84 to 357.26) for the combination of CE-EUS and EUS elastography, respectively. In these same studies, the corresponding performance characteristics for EUS elastography were 87% (95% CI, 82% to 90%), 56% (95% CI, 48% to 64%), 2.28 (95% CI, 1.43 to 3.63), 0.12 (95% CI, 0.03 to 0.42), and 22.60 (95% CI, 5.81 to 87.92), respectively. In these same studies, the respective performance characteristics for CE-EUS were 84% (95% CI, 80% to 88%), 78% (95% CI, 70% to 84%), 3.80 (95% CI, 1.92 to 7.53), 0.13 (95% CI, 0.04 to 0.41), and 31.29 (95% CI, 6.12 to 159.87).

CONCLUSION

CE-EUS and EUS elastography are reliable supplemental techniques for the characterization of SPLs and diagnosis of pancreatic malignancies. However, more studies assessing the combined utilization of both procedures are needed.

Diagnostic performance of endoscopic ultrasound elastography for differential diagnosis of solid pancreatic lesions: A propensity score-matched analysis

BACKGROUND

Endoscopic ultrasound-elastography (EUS-EG) is a non-invasive complementary diagnostic method for differential diagnosis of solid pancreatic lesions (SPL). However, the optimal strain ratio (SR) value and diagnostic performance of EUS-EG have not yet been determined in pancreatic neuroendocrine neoplasm (PNEN), mass-forming pancreatitis (MFP), and pancreatic ductal adenocarcinoma (PDAC). We aimed to determine the optimal SR value in EUS-EG for differential diagnosis of SPLs.

METHODS

Patients who underwent EUS-EG for SPL evaluation between July 2016 and June 2019 were retrospectively investigated. Patients were divided into three groups based on the final diagnosis (PNEN, MFP, or PDAC). Patient demographics, characteristics of SPL, and EUS-EG were compared.

RESULTS

The mean (± standard deviation) SR value for each group were 11.85 ± 7.56 (PNEN, n = 10), 11.45 ± 5.97 (MFP, n = 37), and 22.50 ± 13.19 (PDAC, n = 87). Multinomial logistic regression analysis revealed that an increase of SR value was significantly associated with PDAC (PNEN versus PDAC, p = 0.0216; MFP versus PDAC, p = 0.0006). The optimal cut-off value for differential diagnosis was confirmed as 17.14 after propensity score matching.

CONCLUSIONS

We provided the optimal cut-off SR values for differential diagnosis between MFP and PDAC. EUS-EG can be used as a supplementary diagnostic method in the diagnosis of SPLs. (Clinical trial registration number: https://cris.nih.go.kr/cris: KCT0002082).

Endoscopic ultrasonography: Enhancing diagnostic accuracy

Endoscopic ultrasound (EUS) is an essential technique for the management of several diseases. Over the years, new technologies have been developed because to improve and overcome certain limitations related to EUS guided tissue acquisition. Among these new methods, EUS guided elastography and contrast enhanced EUS has arisen as the most widely recognized and available. We will review in this manuscript the different techniques of elastography and contrast enhancement. Nowadays, there are well establish indications for advance imaging, mainly for supporting the management of pancreatic diseases (diagnosis of chronic pancreatitis and differential diagnosis of solid and cystic pancreatic tumors) and characterization of lymph nodes. However, there are more data on new potential indications for the near future.

The expanding role of endoscopic ultrasound elastography

Endoscopic ultrasound (EUS) is an invaluable tool for assessing various GI diseases. However, using just the conventional B-mode EUS imaging may not be sufficient to accurately delineate the lesion's character. Using the principle of stress-induced tissue strain, EUS elastography (EUS-E) can help in the real-time sonographic assessment of the level of tissue stiffness or hardness of any organ of interest during a routine EUS procedure. Thus, EUS-E can better characterize the lesion's nature and highlight the more suspicious areas within an individual lesion. The most commonly studied lesions with EUS-E are the pancreatic lesions, namely, chronic pancreatitis, pancreatic cancer, and lymph nodes. However, EUS-E is gradually expanding its use for lesion characterization of the liver, bile duct, adrenals, gastrointestinal tract, and even therapy response. Moreover, the use of EUS-E along with other image enhancement techniques such as harmonic EUS and contrast-enhanced EUS can improve the accuracy of the diagnosis. However, several technical aspects need to be standardized before EUS-E can be truly used as a tool for "virtual biopsy". This review focuses on the various technical aspects of the use of EUS-E, it is established and expanding indications and an extensive outline of the various studies on EUS-E. We also discuss the current pitfalls and future trends in EUS-E.

Early detection of pancreatic tumors by advanced EUS imaging

The early detection of pancreatic ductal adenocarcinoma (PDAC) dramatically improves outcome. All available state-of-the-art imaging methods allow early detection with EUS being the best technique for exclusion of PDAC and detection of very early PDAC. Etiological differentiation of small SPL is important to guide individually tailored patients' management including radical surgery in resectable PDAC, medical (neoadjuvant or palliative intended) treatment in patients with non-resectable malignancy, pancreatic parenchyma saving strategies in some non-PDAC, and follow-up in particular in low-grade PanNEN or other small benign lesions. Multimodality EUS imaging including B-Mode assessment, elastography, contrast-enhancement and EUS-guided sampling is the most appropriate technique for diagnosis and risk assessment of small SPL. We present a review discussing modern (endoscopic) ultrasound imaging techniques including contrast enhanced ultrasound and elastography for the early detection and characterization of solid pancreatic lesions.

What is the best modality for diagnosing pancreatic cancer?

This Editorial refers to the article by S. Omoto et al., p 198‐206 of this issue DEN 34:1.

Applications of endoscopic ultrasound elastography in pancreatic diseases: From literature to real life

Elastography is a non-invasive method widely used to measure the stiffness of the tissues, and it is available in most endoscopic ultrasound machines, using either qualitative or quantitative techniques. Endoscopic ultrasound elastography is a tool that should be applied to obtain a complementary evaluation of pancreatic diseases, together with other imaging tests and clinical data. Elastography can be informative, especially when studying pancreatic masses and help the clinician in the differential diagnosis between benign or malignant lesions. However, further studies are necessary to standardize the method, increase the reproducibility and establish definitive cut-offs to distinguish between benign and malignant pancreatic masses. Moreover, even if promising, elastography still provides little information in the evaluation of benign conditions.

Direct Comparison of Elastography Endoscopic Ultrasound Fine-Needle Aspiration and B-Mode Endoscopic Ultrasound Fine-Needle Aspiration in Diagnosing Solid Pancreatic Lesions

Elastography endoscopic ultrasound (E-EUS) has been proved to be a valuable supplement to endoscopic ultrasound fine-needle aspiration (EUS-FNA) in differentiating solid pancreatic lesions, but the improvement of EUS-FNA guided during E-EUS has not been proven. Our study aimed to evaluate whether E-EUS fine-needle aspiration (E-EUS-FNA) was superior to B-mode EUS-FNA for the diagnosis of solid pancreatic masses and whether the diagnostic rate was affected by specific factors. Our prospective study was conducted between 2019-2020 by recruiting patients with solid pancreatic masses. E-EUS examination was followed by one pass of E-EUS-FNA towards the blue part of the lesion and a second pass of EUS-FNA. The final diagnosis was based on surgery, E-EUS-FNA or EUS-FNA results, or a 12-month follow-up. Sixty patients with solid pancreatic lesions were evaluated. The sensitivity, specificity, and accuracy for diagnosing malignancy using E-EUS-FNA and EUS-FNA were 89.5%, 100%, 90%, 93%, 100%, and 93.3%, respectively, but the differences were not significant. Neither mass location nor the lesion size influenced the results. The lengths of the core obtained during E-EUS-FNA and EUS-FNA were similar. E-EUS-FNA in solid pancreatic lesions was not superior to B-mode EUS-FNA.

Scoparone inhibits pancreatic cancer through PI3K/Akt signaling pathway

BACKGROUND

Pancreatic cancer is a highly malignant tumor of the gastrointestinal system whose emerging resistance to chemotherapy has necessitated the development of novel antitumor treatments. Scoparone, a traditional Chinese medicine monomer with a wide range of pharmacological properties, has attracted considerable attention for its antitumor activity.

AIM

To explore the potential antitumor effect of scoparone on pancreatic cancer and the possible molecular mechanism of action.

METHODS

The target genes of scoparone were determined using both the bioinformatics and multiplatform analyses. The effect of scoparone on pancreatic cancer cell proliferation, migration, invasion, cell cycle, and apoptosis was detected in vitro. The expression of hub genes was tested using quantitative reverse transcription polymerase chain reaction (qRT-PCR), and the molecular mechanism was analyzed using Western blot. The in vivo effect of scoparone on pancreatic cancer cell proliferation was detected using a xenograft tumor model in nude mice as well as immunohistochemistry.

RESULTS

The hub genes involved in the suppression of pancreatic cancer by scoparone were obtained by network bioinformatics analyses using publicly available databases and platforms, including SwissTargetPrediction, STITCH, GeneCards, CTD, STRING, WebGestalt, Cytoscape, and Gepia; AKT1 was confirmed using qRT-PCR to be the hub gene. Cell Counting Kit-8 assay revealed that the viability of Capan-2 and SW1990 cells was significantly reduced by scoparone treatment exhibiting IC50 values of 225.2 μmol/L and 209.1 μmol/L, respectively. Wound healing and transwell assays showed that scoparone inhibited the migration and invasion of pancreatic cancer cells. Additionally, flow cytometry confirmed that scoparone caused cell cycle arrest and induced apoptosis. Scoparone also increased the expression levels of Bax and cleaved caspase-3, decreased the levels of MMP9 and Bcl-2, and suppressed the phosphorylation of Akt without affecting total PI3K and Akt. Moreover, compared with the control group, xenograft tumors, in the 200 μmol/L scoparone treatment group, were smaller in volume and lighter in weight, and the percentages of Ki65- and PCNA-positive cells were decreased.

CONCLUSION

Our findings indicate that scoparone inhibits pancreatic cancer cell proliferation in vitro and in vivo, inhibits migration and invasion, and induces cycle arrest and apoptosis in vitro through the PI3K/Akt signaling pathway.

Diagnostic accuracy of EUS-FNA in the evaluation of pancreatic neuroendocrine neoplasms grading: Possible clinical impact of misclassification

BACKGROUND AND OBJECTIVES

Prognosis of pancreatic neuroendocrine neoplasms (PanNENs) mostly depend on tumor stage and grade, determined by Ki-67 labeling index. EUS-FNA is considered the gold-standard technique to obtain it. The aims of our study were to establish diagnostic accuracy of preoperative EUS-FNA Ki-67 evaluation considering final pathological assessment on surgical specimen as gold standard and to investigate the possible impact on prognosis of misclassification.

METHODS

This is a retrospective study from a prospectively collected database. EUS-FNA grading (eG) and surgical one (sG) measured according to Ki-67 proliferative index values, according to 2017 WHO classification, were compared. eG-sG correlation was evaluated by Spearman index. Logistic regression investigated factors associated with misclassification. Prognostic difference in terms of progression-free survival was evaluated by Kaplan Meier method.

RESULTS

One hundred and twelve PanNENs patients enrolled. In 13.4% of patients (15/112) EUS-FNA "undergraded" patients (eG1 vs. sG2), while in 12.5% (n = 14) it "overgraded" PanNENs (eG2 to sG1). No misclassifications in G3 patients. In patients with tumors <20 mm (n = 44), 2 (4.5%) eG1 and 10 (22.7%) eG2 were finally classified respectively as G2 and G1 at surgical histology. No factors, as i.e. the lesions' size or their morphological aspect, were associated with misclassification. In overgraded PanNENs, no progression occurred, while in patients correctly classified/undergraded the progression rate was 14.3%.

CONCLUSIONS

This is the largest cohort of surgical PanNENs with preoperative EUS-FNA grading evaluation. Despite an acceptable eG-sG correlation, about 25% of patients are misclassified. Clinical impact of misclassification should be carefully considered especially in small tumors undergoing observation.

What does liver elastography measure? Technical aspects and methodology

Elastography can be thought as an extension of the ancient technique of palpation. After giving a short introduction to the history of elastography, the different technologies that are nowadays available and the physics behind them, the article focuses on the assessment of liver stiffness in patients with diffuse liver disease using shear wave elastography (SWE). Practical advices on how to perform the SWE techniques and on the factors that should be considered for a correct interpretation of the results are given. This paper aimed to provide a practical guide for beginners and advanced clinical users to better understand technical aspects, methodologies and terminology.

Diagnostic performance of endoscopic ultrasonography-guided elastography for solid pancreatic lesions: Shear-wave measurements versus strain elastography with histogram analysis

BACKGROUND AND AIMS

Endoscopic ultrasound (EUS) elastography (EUS-EG) is a minimally invasive diagnostic method for evaluating tissue elasticity. The aim of this study was to evaluate the feasibility of newly developed EUS shear-wave measurement (EUS-SWM) and to compare diagnostic performance between EUS-SWM and the conventional strain elastography (SE) for the measurement of elasticity of solid pancreatic lesions (SPLs).

METHODS

From December 2017 until August 2019, we retrospectively reviewed 64 consecutive cases with SPLs who underwent both EUS-SWM and SE. EUS-SWM was used to measure the shear-wave velocity, Vs (m/s), and the unique measurement reliability index, VsN (%), in the target lesion. SE images were assessed by strain histogram (SH) analysis, and the mean strain value of the elasticity index was measured. We evaluated the diagnostic performance of EUS-SWM and SE with SH to characterize the SPLs.

RESULTS

The Vs (m/s) values of SPLs were 2.19 for pancreatic cancer (PC), 1.31 for pancreatic neuroendocrine neoplasm (PanNEN), 2.56 for mass-forming pancreatitis (MFP) and 1.58 for metastatic tumors. Vs showed no significant difference based on the disease. The mean strain values were 45.5 for PC, 47.3 for PanNEN, and 74.5 for MFP. In the comparison of tissue elasticity between PC and MFP, Vs showed no significant difference (P = 0.5687); however, the mean strain value was significantly lower in PC cases (45.4 vs 74.5: P = 0.0007).

CONCLUSION

Endoscopic ultrasound SWM tended to be unstable for the measurement of elasticity of SPLs, and conventional SE with SH was superior for their characterization.

Revising the European Society of Gastrointestinal Endoscopy (ESGE) research priorities: a research progress update

BACKGROUND

One of the aims of the European Society of Gastrointestinal Endoscopy (ESGE) is to encourage high quality endoscopic research at a European level. In 2016, the ESGE research committee published a set of research priorities. As endoscopic research is flourishing, we aimed to review the literature and determine whether endoscopic research over the last 4 years had managed to address any of our previously published priorities.

METHODS

As the previously published priorities were grouped under seven different domains, a working party with at least two European experts was created for each domain to review all the priorities under that domain. A structured review form was developed to standardize the review process. The group conducted an extensive literature search relevant to each of the priorities and then graded the priorities into three categories: (1) no longer a priority (well-designed trial, incorporated in national/international guidelines or adopted in routine clinical practice); (2) remains a priority (i. e. the above criterion was not met); (3) redefine the existing priority (i. e. the priority was too vague with the research question not clearly defined).

RESULTS

The previous ESGE research priorities document published in 2016 had 26 research priorities under seven domains. Our review of these priorities has resulted in seven priorities being removed from the list, one priority being partially removed, another seven being redefined to make them more precise, with eleven priorities remaining unchanged. This is a reflection of a rapid surge in endoscopic research, resulting in 27 % of research questions having already been answered and another 27 % requiring redefinition.

CONCLUSIONS

Our extensive review process has led to the removal of seven research priorities from the previous (2016) list, leaving 19 research priorities that have been redefined to make them more precise and relevant for researchers and funding bodies to target.

Role of Endoscopic Ultrasonography and Endoscopic Retrograde Cholangiopancreatography in the Diagnosis of Pancreatic Cancer

Pancreatic cancer has the poorest prognosis among all cancers, and early diagnosis is essential for improving the prognosis. Along with radiologic modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), endoscopic modalities play an important role in the diagnosis of pancreatic cancer. This review evaluates the roles of two of those modalities, endoscopic ultrasonography (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), in the diagnosis of pancreatic cancer. EUS can detect pancreatic cancer with higher sensitivity and has excellent sensitivity for the diagnosis of small pancreatic cancer that cannot be detected by other imaging modalities. EUS may be useful for the surveillance of pancreatic cancer in high-risk individuals. Contrast-enhanced EUS and EUS elastography are also useful for differentiating solid pancreatic tumors. In addition, EUS-guided fine needle aspiration shows excellent sensitivity and specificity, even for small pancreatic cancer, and is an essential examination method for the definitive pathological diagnosis and treatment decision strategy. On the other hand, ERCP is invasive and performed less frequently for the purpose of diagnosing pancreatic cancer. However, ERCP is essential in cases that require evaluation of pancreatic duct stricture that may be early pancreatic cancer or those that require differentiation from focal autoimmune pancreatitis.