Developing Personalized Postoperative Follow‐Up Strategies for Patients With Locally Advanced Gastric Cancer Receiving Neoadjuvant Therapy Based on Dynamic Programming: A Multicenter Retrospective Cohort Study in China

The optimal follow-up strategy for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant therapy (NAT) remains unknown. Traditional follow-up strategies based on relatively fixed intervals fail to fully account for dynamic changes in recurrence risk. This study aimed to develop a personalized postoperative follow-up strategy using dynamic programming (DP) to optimize follow-up arrangements based on individual patient characteristics and dynamic recurrence risks. This study included 3397 patients with LAGC who underwent surgery after NAT at 21 medical centers between 2018 and 2023. By integrating multiple prognostic indicators using a random survival forest model, we estimated individual time-adjusted cumulative hazards. A conditional inference tree was then used to stratify patients into low-, medium-, and high-risk groups. The DP algorithm was employed to determine the optimal follow-up arrangements for recurrence detection. A Markov decision-analytic model was used to identify the most cost-effective follow-up strategy. Compared with the guideline strategies, the DP-based strategy significantly reduced the average delayed detection time, particularly in the high-risk group. Furthermore, the cost-effectiveness analysis showed that the DP-based strategy achieved the best incremental cost-effectiveness ratio. Finally, we determined that the optimal numbers of follow-ups for the low-, medium-, and high-risk groups were 9, 10, and 13, respectively. The study demonstrates that the DP-based personalized follow-up strategy significantly improved the efficiency of recurrence detection and resource utilization in LAGC. These findings highlight the potential of DP algorithms in clinical decision-making and provide a foundation for future personalized follow-up studies.

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Publication Details

Journal
International Journal of Cancer
Published
2026-09-01
DOI
https://doi.org/10.1002/ijc.70731
Primary Topic
Gastric Cancer Management and Outcomes
Type
article
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article

Developing Personalized Postoperative Follow‐Up Strategies for Patients With Locally Advanced Gastric Cancer Receiving Neoadjuvant Therapy Based on Dynamic Programming: A Multicenter Retrospective Cohort Study in China

Zhen Wang, Jian‐Xian Lin, Lei Lian, Xinlu Liu et al.
International Journal of Cancer
Gastric Cancer Management and Outcomes
article

Developing Personalized Postoperative Follow‐Up Strategies for Patients With Locally Advanced Gastric Cancer Receiving Neoadjuvant Therapy Based on Dynamic Programming: A Multicenter Retrospective Cohort Study in China

Zhen Wang, Jian‐Xian Lin, Lei Lian, Xinlu Liu, Hua Xiao, Kaixiong Tao, Shuqiang Yuan, 曹守根, Weiwei Sheng, Peng Zhang, Yi Cao, Wei Li, Xinxin Wang, Zheng Yang, Zhaoqing Tang, Tianhao Li, Jing Sun, Chenyu Liu, Kai Tao, Zizhen Zhang, Yuzhou Zhao, Jie Yin, Linjun Wang, Zheng Shi, Liang Shang
article en

Abstract

The optimal follow-up strategy for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant therapy (NAT) remains unknown. Traditional follow-up strategies based on relatively fixed intervals fail to fully account for dynamic changes in recurrence risk. This study aimed to develop a personalized postoperative follow-up strategy using dynamic programming (DP) to optimize follow-up arrangements based on individual patient characteristics and dynamic recurrence risks. This study included 3397 patients with LAGC who underwent surgery after NAT at 21 medical centers between 2018 and 2023. By integrating multiple prognostic indicators using a random survival forest model, we estimated individual time-adjusted cumulative hazards. A conditional inference tree was then used to stratify patients into low-, medium-, and high-risk groups. The DP algorithm was employed to determine the optimal follow-up arrangements for recurrence detection. A Markov decision-analytic model was used to identify the most cost-effective follow-up strategy. Compared with the guideline strategies, the DP-based strategy significantly reduced the average delayed detection time, particularly in the high-risk group. Furthermore, the cost-effectiveness analysis showed that the DP-based strategy achieved the best incremental cost-effectiveness ratio. Finally, we determined that the optimal numbers of follow-ups for the low-, medium-, and high-risk groups were 9, 10, and 13, respectively. The study demonstrates that the DP-based personalized follow-up strategy significantly improved the efficiency of recurrence detection and resource utilization in LAGC. These findings highlight the potential of DP algorithms in clinical decision-making and provide a foundation for future personalized follow-up studies.

International Journal of Cancer
Qingdao University (CN), Fujian Medical University (CN), Nanchang University (CN), Sun Yat-sen University (CN), Shanxi Medical University (CN), Second Military Medical University (CN), Capital Medical University (CN), Dalian Medical University (CN), Jilin University (CN), Anhui Medical University (CN), Fudan University (CN), Renji Hospital (CN), Ruijin Hospital (CN), Chinese PLA General Hospital (CN), Zhengzhou University (CN), Sixth Affiliated Hospital of Sun Yat-sen University (CN), First Affiliated Hospital of Jiangxi Medical College (CN), Shanxi Provincial Cancer Hospital (CN), Hunan Cancer Hospital (CN), Changhai Hospital (CN), Shandong Provincial Hospital (CN), Affiliated Hospital of Qingdao University (CN), First Hospital of Jilin University (CN), First Affiliated Hospital of GuangXi Medical University (CN), Zhongshan Hospital (CN), Wuhan Union Hospital (CN), First Affiliated Hospital of Anhui Medical University (CN), First Affiliated Hospital of Dalian Medical University (CN), Henan Cancer Hospital (CN), Sun Yat-sen University Cancer Center (CN), Beijing Friendship Hospital (CN), Shandong First Medical University (CN), Union Hospital (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, Science and Technology Department of Hubei Province
Openalex Percentile: Top 11%
Gastric Cancer Management and Outcomes
DOISourceOpenAlex
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