Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) and fine-needle biopsy (EUS-FNB) are the gold standard methods for obtaining tissue samples from solid pancreatic lesions for diagnosis. The technique uses a thin needle guided by ultrasound imaging through an endoscope to reach the pancreas.
Over recent years, significant advances have been made in needle design, sampling techniques, and specimen evaluation — all aimed at improving diagnostic accuracy while reducing the number of needle passes required and improving patient safety.
Third-generation needles — the Franseen and fork-tip designs — now outperform older reverse-bevel needles for tissue quality. Studies show Franseen needles provide more than twice the tissue core length per pass and better diagnostic accuracy compared to older designs.
The optimal number of needle passes remains an active area of study. One key finding is that fewer passes (as few as three) can match the diagnostic sensitivity of twelve passes, while producing less blood contamination in the specimen — making pathologist review easier.
Several sampling techniques have been developed to maximize tissue yield. The door-knocking technique involves rapid to-and-fro needle movements to collect more cells. The fanning technique spreads the needle across different angles within the lesion during a single pass.
A comparison of suction methods showed that wet suction (pre-filling the needle with saline) produces better tissue integrity scores, though with more blood contamination. The slow-pull method produces cleaner specimens with fewer blood clots. Neither is universally superior, and clinical context guides the choice.
Rapid on-site evaluation (ROSE) by a cytologist during EUS-FNA significantly improves diagnostic accuracy, reducing inadequate samples from 12.6% to just 1%. However, cytologists are not always available, creating a gap that AI is beginning to fill.
AI-powered ROSE models using deep convolutional neural networks have demonstrated performance equal to or better than macroscopic on-site evaluation (MOSE) in assessing specimen quality. One AI system trained on over 5,300 cytology slide images from 194 patients matched cytologist performance in identifying cancer cell clusters.
As precision oncology advances, getting enough tissue not just for diagnosis but for comprehensive genomic profiling (CGP) has become critical. CGP identifies specific genetic mutations that can guide targeted treatment decisions.
Currently, standard EUS-FNB procedures often yield insufficient tissue area for CGP tests, which require approximately 25 square millimeters of core tissue. New needle designs and optimized techniques are being investigated specifically to meet this higher bar, enabling more patients to benefit from precision medicine.
EUS-guided tissue acquisition has undergone rapid evolution in needle technology, sampling technique, and specimen processing. Third-generation needles, MOSE, and AI-assisted evaluation represent the current state of the art.
Ongoing innovation in AI-assisted diagnosis and next-generation needle design is expected to further improve diagnostic accuracy and enable comprehensive genomic profiling in more patients, closing the gap between tissue acquisition and precision treatment.