What Are Computer-Assisted Methods Achieving in Fine-Needle Aspiration Cytology of the Pancreas? A Systematic Review...

Pancreatic malignancies present major diagnostic challenges. The gold standard for diagnosis is endoscopic ultrasound-guided fine-needle aspiration (FNA) with cytopathological assessment. Workforce shortages have driven interest in computer-assisted diagnosis (CAD) to improve efficiency. However, its overall diagnostic impact in pancreatic cytology remains unclear. We evaluated the accuracy of CAD in this setting.

We conducted a systematic review and meta-analysis. Five databases were searched (January 2010-May 2025) for studies applying CAD to pancreatic FNA cytology. Two reviewers independently screened studies, with risk of bias assessed using QUADAS-2. Random-effects bivariate models generated pooled sensitivity, specificity, and summary receiver-operating-characteristic (SROC) curves. Studies were analysed according to the unit of analysis: image-level studies generated predictions from individual cytopathological images, whereas patient/case-level studies integrated multiple images per case to produce a single diagnostic output.

Ten studies met eligibility criteria and provided quantitative data. At the image-level, pooled sensitivity and specificity were 91% [95% confidence interval: 86-94] and 92% [87-96], respectively, with a SROC area under the curve (AUC) of 0.962. At the patient/case-level, pooled sensitivity and specificity were 85% [69-93] and 91% [73-97], respectively, with an SROC AUC of 0.919. Heterogeneity was high (I² = 65%-93%), driven by retrospective designs and variable reference standards.

CAD tools achieve near-expert accuracy for definitive diagnoses, reducing routine screening burdens and accelerating surgical planning. However, selection bias, single-centre training and inconsistent thresholds limit generalisability. Prospective multi-centre validation using whole-slide workflows is warranted, and telepathology integration could expand low-resource access.

This systematic review and meta-analysis shows that computer-assisted diagnosis for pancreatic EUS-FNA cytology achieves high accuracy in distinguishing benign from malignant lesions, with pooled sensitivity up to 91% and specificity up to 92%. Current evidence is dominated by retrospective and curated image-level studies, highlighting the need for prospective multicentre validation using whole-slide workflows before routine clinical adoption.

Computer-assisted diagnosis for pancreatic EUS-FNA cytology shows promising accuracy in this systematic review and meta-analysis, but current evidence is still limited by retrospective, curated datasets. Considerable prospective multicentre validation in real-world whole-slide workflows is needed before clinical adoption.