Letter to the Editor: Explainable artificial intelligence helps early cancer diagnosis via extracellular vesicle long RNA

Abstract

Pancreatic ductal adenocarcinoma is a highly aggressive malignancy characterized by poor prognosis and a lack of effective early diagnostic methods. While liquid biopsy has emerged as a promising non-invasive strategy, many existing models rely on “black-box” machine learning algorithms that lack biological transparency. This work discussed and reviewed a methodological advancement by Liu and Zhang, who developed an interpretable two-layer machine learning framework utilizing plasma extracellular vesicle long RNA as a biomarker source. While limitations and challenges still exist, by combining the stability of extracellular vesicle long RNA with an interpretable computational approach, Liu and Zhang’s work, published in the recent issue of World Journal of Gastrointestinal Oncology, represents a significant step forward in developing transparent and highly accurate tools for the early detection and differential diagnosis of pancreatic ductal adenocarcinoma.

Keywords: Pancreatic ductal adenocarcinoma; Liquid biopsy; Extracellular vesicles; Long RNA; Explainable artificial intelligence; Machine learning

Core Tip: Interpretable machine learning applied to extracellular vesicle long ribonucleic acid profiles is a promising direction for earlier pancreatic cancer detection and for differentiating pancreatic ductal adenocarcinoma from chronic pancreatitis. To accelerate clinical readiness, reported discrimination should be benchmarked against standard diagnostic pathways, validated across multiple centers with standardized pre-analytical processing, and accompanied by robustness checks that confirm explanation stability under realistic perturbations.