Benefits and limitations of each type of endoscopic ultrasonography elastography technology for diagnosis of pancreatic diseases

Endoscopic Ultrasound (EUS) Guided Elastography

Endoscopic ultrasound (EUS) is an essential technique for the management of several diseases. Over the years, new technologies have been developed to improve and overcome certain limitations related to EUS-guided tissue acquisition. Among these new methods, EUS-guided elastography, which is a real-time method for the evaluation of tissue stiffness, has arisen as one of the most widely recognized and available. At present, there are available two different systems to perform an elastographic evaluation: strain elastography and shear wave elastography. Strain elastography is based on the knowledge that certain diseases lead to a change in tissue hardness while shear wave elastography monitored shear-wave propagation and measures its velocity. EUS-guided elastography has shown in several studies high accuracy in differentiating benign from malignant lesions from many different locations, mostly in the pancreas and lymph nodes. Therefore, nowadays, there are well-established indications for this technology, mainly for supporting the management of pancreatic diseases (diagnosis of chronic pancreatitis and differential diagnosis of solid pancreatic tumors) and characterization of different diseases. However, there are more data on new potential indications for the near future. In this review, we will present the theoretical bases of this technology and we will discuss the scientific evidence to support its use.

Diagnosing chronic pancreatitis by endoscopic ultrasound assessing the association between ultrasound and pathological findings: A narrative review

Endoscopic ultrasound (EUS) is widely recognized for its non‐invasiveness and for its usefulness in chronic pancreatitis (CP) diagnosis, including early CP. Although it is desirable to obtain a definitive diagnosis of CP by tissue sampling with EUS‐guided fine needle aspiration, histopathological changes in CP are heterogeneous in terms of the extent and the distribution of lesions. Therefore, histopathological diagnosis of appropriate tissue sampling by EUS‐fine needle aspiration is expected to be difficult. Furthermore, it is virtually impossible to match EUS images with pathological sections, making direct contrast between EUS findings and pathology difficult. This narrative review presents a discussion of the diagnosis of CP/early CP by EUS, particularly assessing the association between ultrasound and pathological findings. Recently, the histological corroboration and correlation of EUS findings related to CP have been clarified by surgical specimens, including those obtained from animal studies. Furthermore, remarkable advances have occurred in the objective and quantitative diagnosis of pancreatic fibrosis by EUS‐elastography. Future technological advances in EUS are expected to improve the accuracy of diagnosis of pancreatic fibrosis at earlier stages.

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.

Recent Advances in Endosonography-Elastography: Literature Review

Ultrasonographic elastography is a modality used to visualize the elastic properties of tissues. Technological advances in ultrasound equipment have supported the evaluation of elastography (EG) in endosonography (EUS). Currently, the usefulness of not only EUS-strain elastography (EUS-SE) but also EUS-shear wave elastography (EUS-SWE) has been reported. We reviewed the literature on the usefulness of EUS-EG for various diseases such as chronic pancreatitis, pancreatic solid lesion, autoimmune pancreatitis, lymph node, and gastrointestinal and subepithelial lesions. The importance of this new diagnostic parameter, "tissue elasticity" in clinical practice might be applied not only to the diagnosis of liver fibrosis but also to the elucidation of the pathogeneses of various gastrointestinal diseases, including pancreatic diseases, and to the evaluation of therapeutic effects. The most important feature of EUS-EG is that it is a non-invasive modality. This is an advantage not found in EUS-guided fine needle aspiration (EUS-FNA), which has made remarkable progress in the field of diagnostics in recent years. Further development of artificial intelligence (AI) is expected to improve the diagnostic performance of EUS-EG. Future research on EUS-EG is anticipated.

Automatic Segmentation of Pancreatic Tumors Using Deep Learning on a Video Image of Contrast-Enhanced Endoscopic Ultrasound

Background: Contrast-enhanced endoscopic ultrasound (CE-EUS) is useful for the differentiation of pancreatic tumors. Using deep learning for the segmentation and classification of pancreatic tumors might further improve the diagnostic capability of CE-EUS. Aims: The aim of this study was to evaluate the capability of deep learning for the automatic segmentation of pancreatic tumors on CE-EUS video images and possible factors affecting the automatic segmentation. Methods: This retrospective study included 100 patients who underwent CE-EUS for pancreatic tumors. The CE-EUS video images were converted from the originals to 90-s segments with six frames per second. Manual segmentation of pancreatic tumors from B-mode images was performed as ground truth. Automatic segmentation was performed using U-Net with 100 epochs and was evaluated with 4-fold cross-validation. The degree of respiratory movement (RM) and tumor boundary (TB) were divided into 3-degree intervals in each patient and evaluated as possible factors affecting the segmentation. The concordance rate was calculated using the intersection over union (IoU). Results: The median IoU of all cases was 0.77. The median IoUs in TB-1 (clear around), TB-2, and TB-3 (unclear more than half) were 0.80, 0.76, and 0.69, respectively. The IoU for TB-1 was significantly higher than that of TB-3 (p < 0.01). However, there was no significant difference between the degrees of RM. Conclusions: Automatic segmentation of pancreatic tumors using U-Net on CE-EUS video images showed a decent concordance rate. The concordance rate was lowered by an unclear TB but was not affected by RM.