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

doi:10.4251/wjgo.115920

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Aug 15, 2026; 18(8): 115920
Published online Aug 15, 2026. doi: 10.4251/wjgo.115920

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

Luis Augusto Eijy Nagai, Hui-Heng Jeremy Lin

Luis Augusto Eijy Nagai, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, University of Rostock, Rostock 18057, Mecklenburg-Vorpommern, Germany

Hui-Heng Jeremy Lin, Department of Advanced Interdisciplinary Studies, University of Tokyo, Bunkyo-Ku 1130033, Tokyo, Japan

Co-corresponding authors: Luis Augusto Eijy Nagai and Hui-Heng Jeremy Lin.

Author contributions: Nagai LAE and Lin HHJ drafted the manuscript, performed the literature review, drafted and revised the manuscript as co-corresponding authors; all authors approved the final version.

Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.

Corresponding author: Luis Augusto Eijy Nagai, PhD, Associate Research Scientist, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, University of Rostock, Ernst-Heydemann-Strasse 8, Rostock 18057, Mecklenburg-Vorpommern, Germany. eijynagai@gmail.com

Received: October 29, 2025
Revised: December 23, 2025
Accepted: February 3, 2026
Published online: August 15, 2026
Processing time: 278 Days and 8.6 Hours

Core Tip

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.