Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms

doi:10.4251/wjgo.v17.i12.113988

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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

Letter to the Editor

World J Gastrointest Oncol. Dec 15, 2025; 17(12): 113988
Published online Dec 15, 2025. doi: 10.4251/wjgo.v17.i12.113988

Beyond the blank page: Frequentist and Bayesian perspectives on risk prediction algorithms

Francisco Tustumi, Felipe Antonio Boff Maegawa, Pedro Luiz Serrano Uson Junior

Francisco Tustumi, Pedro Luiz Serrano Uson Junior, Center for Personalized Medicine, Hospital Israelita Albert Einstein, São Paulo 05652900, Brazil

Francisco Tustumi, Gastroenterology, Universidade de Sao Paulo, Sao Paulo 05403-000, Brazil

Felipe Antonio Boff Maegawa, Surgery, Emory University, Atlanta, GA 30322, United States

Author contributions: Tustumi F conceived the study, designed the research question, and drafted the manuscript; Maegawa FAB contributed to the conceptual framework and critical revision of the manuscript; Uson Junior PLS contributed to the literature review and manuscript editing. All authors approved the final version of the manuscript.

Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript.

Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Francisco Tustumi, MD, PhD, Center for Personalized Medicine, Hospital Israelita Albert Einstein, Avenue Albert Einstein, 627/701-Morumbi, São Paulo 05652900, Brazil. franciscotustumi@gmail.com

Received: September 9, 2025
Revised: October 15, 2025
Accepted: November 3, 2025
Published online: December 15, 2025
Processing time: 93 Days and 23.6 Hours

Core Tip

Core Tip: Risk prediction in surgical oncology is evolving beyond traditional scoring systems. Frequentist methods, widely used in machine learning, offer transparency and reproducibility but rely solely on observed data. Bayesian reasoning, by contrast, integrates prior clinical knowledge with new information, mirroring real-world decision-making. The integration of both frameworks promises more reliable, interpretable, and personalized prediction tools, advancing the future of surgical care.