Accuracy of EUS-FNA in Solid Pancreatic Lesions: Sometimes Size Does Matter

Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy

OBJECTIVE

This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA).

METHODS

498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients.

RESULTS

The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution.

CONCLUSION

The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA.

ADVANCES IN KNOWLEDGE

This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.

Pitfalls in interventional EUS procedures and coping strategies for endoscopy assistants (with video)

In recent years, the application of EUS in the diagnosis and treatment has become increasingly popular due to the rapid technological advancements in this field. With the application of new technologies, EUS assistants encounter various problems or “pitfalls” during clinical operations, which may pose challenges to the successful completion of relevant procedures. For example, a needle tip may not be visualized by ultrasonography during EUS-FNA; a stiff fine needle may not be introduced through the working channel of the endoscope in the duodenum, and withdrawal of a guidewire in a needle may be associated with tearing and peeling of the guidewire in EUS-guided biliary drainage. In view of these commonly encountered problems, this article summarizes the countermeasures that EUS assistants can take for EUS-FNA and EUS-guided drainage to improve the efficiency of the procedures and reduce the occurrence of adverse events.