{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:21:24Z","timestamp":1788254484360,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100018806","name":"Department of Science and Technology of Hubei Province","doi-asserted-by":"publisher","award":["2021BCA116"],"award-info":[{"award-number":["2021BCA116"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018806","name":"Department of Science and Technology of Hubei Province","doi-asserted-by":"publisher","award":["2024AFB655"],"award-info":[{"award-number":["2024AFB655"]}],"id":[{"id":"10.13039\/501100018806","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072736"],"award-info":[{"award-number":["82072736"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82373123"],"award-info":[{"award-number":["82373123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl Journal of Cancer"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The optimal follow\u2010up strategy for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant therapy (NAT) remains unknown. Traditional follow\u2010up 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\u2010up strategy using dynamic programming (DP) to optimize follow\u2010up 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\u2010adjusted cumulative hazards. A conditional inference tree was then used to stratify patients into low\u2010, medium\u2010, and high\u2010risk groups. The DP algorithm was employed to determine the optimal follow\u2010up arrangements for recurrence detection. A Markov decision\u2010analytic model was used to identify the most cost\u2010effective follow\u2010up strategy. Compared with the guideline strategies, the DP\u2010based strategy significantly reduced the average delayed detection time, particularly in the high\u2010risk group. Furthermore, the cost\u2010effectiveness analysis showed that the DP\u2010based strategy achieved the best incremental cost\u2010effectiveness ratio. Finally, we determined that the optimal numbers of follow\u2010ups for the low\u2010, medium\u2010, and high\u2010risk groups were 9, 10, and 13, respectively. The study demonstrates that the DP\u2010based personalized follow\u2010up strategy significantly improved the efficiency of recurrence detection and resource utilization in LAGC. These findings highlight the potential of DP algorithms in clinical decision\u2010making and provide a foundation for future personalized follow\u2010up studies.<\/jats:p>","DOI":"10.1002\/ijc.70731","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:11:35Z","timestamp":1788253895000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Developing Personalized Postoperative Follow\u2010Up Strategies for Patients With Locally Advanced Gastric Cancer Receiving Neoadjuvant Therapy Based on Dynamic Programming: A Multicenter Retrospective Cohort Study in China"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6963-4469","authenticated-orcid":false,"given":"Tianhao","family":"Li","sequence":"first","affiliation":[{"name":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology  Wuhan 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