Negoi I. Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up. World J Gastroenterol 2025; 31(18): 106670 [DOI: 10.3748/wjg.v31.i18.106670]
Corresponding Author of This Article
Ionut Negoi, MD, PhD, Associate Professor, Department of General Surgery, Carol Davila University of Medicine and Pharmacy Bucharest, Clinical Emergency Hospital of Bucharest, No. 8 Floreasca Street, Sector 1, Bucharest 014461, Romania. negoiionut@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. May 14, 2025; 31(18): 106670 Published online May 14, 2025. doi: 10.3748/wjg.v31.i18.106670
Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up
Ionut Negoi
Ionut Negoi, Department of General Surgery, Carol Davila University of Medicine and Pharmacy Bucharest, Clinical Emergency Hospital of Bucharest, Bucharest 014461, Romania
Author contributions: Negoi I contributed to this study, designed the overall concept and outline of the manuscript, contributed to the discussion and design of the manuscript, contributed to the writing and editing of the manuscript, and reviewed the literature.
Conflict-of-interest statement: The author has no conflicts of interest to disclose.
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: Ionut Negoi, MD, PhD, Associate Professor, Department of General Surgery, Carol Davila University of Medicine and Pharmacy Bucharest, Clinical Emergency Hospital of Bucharest, No. 8 Floreasca Street, Sector 1, Bucharest 014461, Romania. negoiionut@gmail.com
Received: March 4, 2025 Revised: April 7, 2025 Accepted: April 18, 2025 Published online: May 14, 2025 Processing time: 70 Days and 13.3 Hours
Abstract
Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its impact. Post-treatment surveillance has emerged as essential for early detection of recurrence, significantly improving patient outcomes. However, intensive surveillance strategies have shown mixed results compared to less intensive methods, emphasizing the necessity for personalized, risk-adapted approaches. The observed suboptimal adherence to existing surveillance protocols underscores the urgent need for more tailored and efficient strategies. In this context, circulating tumor DNA (ctDNA) emerges as a promising biomarker with significant potential to revolutionize post-treatment surveillance, demonstrating high specificity [0.95, 95% confidence interval (CI): 0.91-0.97] and robust diagnostic odds (37.6, 95%CI: 20.8-68.0) for recurrence detection. Furthermore, artificial intelligence and machine learning models integrating patient-specific and tumor features can enhance risk stratification and optimize surveillance strategies. The reported area under the receiver operating characteristic curve, measuring artificial intelligence model performance in predicting CRC recurrence, ranged from 0.581 and 0.593 at the lowest to 0.979 and 0.978 at the highest in training and validation cohorts, respectively. Despite this promise, addressing cost, accessibility, and extensive validation remains crucial for equitable integration into clinical practice.
Core Tip: Given the increasing incidence of colorectal cancer, especially among younger populations, effective post-treatment surveillance is essential for early detection of recurrence and improved outcomes. Intensive surveillance has variable efficacy, underscoring the need for personalized, risk-based protocols. Suboptimal adherence to guidelines further highlights the need for efficient, individualized approaches. Circulating tumor DNA shows promise as a biomarker, offering high specificity and diagnostic accuracy. Additionally, artificial intelligence models utilizing patient and tumor data have the potential to refine surveillance, with predictive accuracy ranging from 0.581 to 0.979. Nonetheless, cost, accessibility, and validation remain significant barriers to widespread clinical implementation.
